How Microsoft Purview Turns Com…

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Most leaders operate on assumptions

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that they’ve dressed up to look like a strategy.

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They point to an org chart, a stack of policies,

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and a few compliance frameworks,

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assuming these documents represent

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how the business actually functions.

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They are wrong.

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The space between what you think your company does

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and what it actually does is where risk lives,

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but it’s also where you’ll find your biggest opportunities

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and a massive amount of wasted money.

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This is the uncomfortable reality

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that Microsoft Pervue reveals provided you actually know

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how to look at the data it provides.

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Most people treat Pervue as a basic compliance tool

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or a simple checkbox to satisfy auditors

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and avoid regulatory fines.

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They think the goal is just ticking boxes on a dashboard,

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running a few policies to block an occasional email

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and generating reports to prove they aren’t breaking GDPR rules.

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That is not what Pervue is for.

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Pervue is a diagnostic platform and an operating system

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for understanding the mechanics of your business.

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It shows you where data actually flows

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and where decisions really happen in real time,

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highlighting exactly where your corporate assumptions collide

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with reality.

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As we move into 2026, you’ll be deploying

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co-pilot at scale and building AI agents

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to handle complex workflows,

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which means your competitive advantage depends entirely

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on how fast you can move safely.

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In this environment, Pervue becomes the lens you use

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to see if your infrastructure is actually ready for that speed.

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Here is the plan for this episode.

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We are going to reframe Pervue from a defensive protection mechanism

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into a high level business intelligence tool.

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I’ll show you how sensitivity labels reveal

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what your organization truly values

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and how DLP violations point to the exact spots

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where your official workflows are broken.

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We will look at how insider risk signals usually indicate

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organizational stress rather than actual malice,

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creating a diagnostic picture

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of the real company operating underneath your formal org chart.

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By the end of this discussion,

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you’ll understand why Pervue isn’t just something

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you implement to stay compliant.

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It’s a system you use because it exposes

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the critical details you didn’t even know you were missing.

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Let’s start with the core problem.

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Your company has an org chart where sales reports

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to a VP, finance reports to the CFO, and operations,

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answers to the COO.

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There is a clear structure with accountability and reporting lines,

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yet that chart is almost completely useless

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for understanding how work actually gets done.

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Work follows the data in the path of least resistance,

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not the lines on a PDF.

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In most organizations,

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the path of least resistance looks nothing

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like the official process your leadership team signed off on.

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If you ask your CFO where customer data lives,

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they’ll give you one answer,

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but the VP of sales will give you a different one.

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If you find the person actually responsible

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for maintaining that data,

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their answer will be different still,

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and none of those answers will be complete

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because they all involve a level of guessing.

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This isn’t a sign of incompetence,

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but rather the natural state of companies

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that have grown much faster than their governance infrastructure.

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The org chart doesn’t account for actual decision-making nodes,

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nor does it show you where information bottlenecks form

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or which small team is holding up three major workflows.

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We call this the illusion of control, yet,

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that you think you know how the business operates

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because you have roles and reporting structures,

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but you’re actually operating on a theoretical model

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while the real model is messier and more fragile.

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Per view is the tool that breaks that illusion.

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When you scan your SharePoint tenant or apply sensitivity labels,

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you aren’t just gathering compliance artifacts for a report.

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You are collecting diagnostic data about the real organization,

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using labels to see what people actually value

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and DLP violations to see where policy friction

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creates workarounds.

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Taken together, this data tells the story

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of how your business really functions.

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And here is the critical part.

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That story is almost never the same

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as the one told by your org chart.

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Most organizations have massive discovery gaps

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where they don’t fully know where their sensitive data is hiding.

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You might know the obvious spots like your CRM

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or financial systems, but you likely don’t know

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about the spreadsheets finance keeps in a private one drive

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or the customer list sales stored in a random team’s folder.

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You don’t have a data reality right now,

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you only have a collection of guesses.

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When you ask where your most critical data lives,

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the polite answer is in our systems of record,

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but the honest answer is usually everywhere

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and we aren’t sure it’s all protected.

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Perview solves this, not by locking everything down

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behind a wall, but by making the invisible parts

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of your business visible.

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Let me give you a concrete example of this in action.

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I recently looked at a financial services firm

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that had a purview implementation

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and a solid information protection strategy on paper.

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They had a clear classification scheme

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for internal and confidential files

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and their DLP rules were active,

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meaning they were technically compliant by every standard metric.

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But when they actually looked at the data distribution

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after running purview scans,

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they realized that 85% of their labeled data

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was marked as confidential or highly confidential.

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If nearly everything is classified as highly sensitive,

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then nothing is actually sensitive

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and the entire system just becomes background noise.

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In another organization, they found the opposite problem

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where clinical notes that should have been locked down

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were almost entirely unlabeled.

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The system had failed because nobody was actually using it

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or validating it.

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This is what purview reveals when you know

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how to read the signals.

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The massive gap between what your policy intended

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and what is actually happening on the ground.

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And that gap is exactly where your risk lives.

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This matters more than ever in 2026

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because Microsoft 365 co-pilot is rolling out

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and custom AI agents are being built.

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You are likely planning to give AI access to your data

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so it can run processes and handle decisions,

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but co-pilot respects the permissions and labels

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you already have in place.

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If your data is scattered across 17 systems

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with inconsistent labels,

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co-pilot will simply reflect that same chaos back to you.

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If you haven’t governed your data,

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you cannot safely govern how an AI access is that data.

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This is the moment where purview stops being a boring compliance task

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and starts being a requirement for competitive readiness.

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The organizations that win this year

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won’t be the ones with perfect governance

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but the ones that see their own structure clearly.

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They understand their risks, they know what data matters

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and they’ve made intentional decisions

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about what to protect and what to expose.

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Purview is the tool that makes that vision possible.

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Over the next hour, we’re going to walk through this together.

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In part one, we’ll examine the illusion of control

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and why your org chart doesn’t match your data flows.

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We will explore the three critical failures

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of traditional governance and introduce the three questions

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every organization must be able to answer about its data.

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In part two, we shift from theory to practice

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to show you how sensitivity labels work

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as a map of organizational priorities.

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We’ll look at why DLP violations show broken workflows

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rather than reckless people and reframe insider risk

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as a stress indicator.

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In part three, we’ll position purview

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as an X-ray machine for visibility.

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I’ll show you how to identify silos

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and visualize true dependencies

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that your org chart will never tell you about.

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In part four, we move from diagnosis to action

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with three specific audits you can run right now.

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These aren’t theoretical exercises.

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They are the foundation for actually seeing your business

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for what it is.

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Finally, in part five, we’ll connect all of this to AI readiness.

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AI doesn’t fail because the technology is bad.

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It fails because the data is a mess,

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making readiness a governance problem

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rather than a technical one.

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Throughout this, we’ll use real examples

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like a sales team that bypasses a slow CRM

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or a finance team wasting weeks

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on manual reconciliation.

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These aren’t rare edge cases.

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They are the default state for most companies.

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By the end of this, you’ll see that purview

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isn’t just a tool for compliance.

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It’s the only way to actually see your organization

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in an era of AI and high-speed data

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seeing clearly isn’t just a nice feature to have.

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It’s existential.

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Let’s get started.

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The confidence trap, why leaders don’t see the gap?

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You are operating on assumptions every single day

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and while it isn’t intentional,

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it is happening systematically.

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Your CFO makes decisions about data protection

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based on what they think is sensitive

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while the VP of sales structures the pipeline

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based on what they believe the numbers look like.

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At the same time, the IT team configures access controls

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based on assumptions about roles

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and the compliance officer designs policies

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based on what they assume the organization is doing.

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None of these people are lying to you

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and they certainly aren’t incompetent.

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They are simply working with incomplete information

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and the system they work in keeps reinforcing

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the idea that their information is complete.

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This is the confidence trap.

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It works like this.

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You have a policy stating all customer data

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must be labeled confidential

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and because that policy exists,

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you believe people are following it.

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You’ve created a mental model where your data is protected

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because you communicated the rule

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but the actual labeling is where that model usually falls apart.

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In most organizations, only about 30 to 50% of sensitive data

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is actually labeled leaving the rest to exist

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in the gap between your intent and the operational reality.

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People aren’t trying to violate the rules.

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It’s just that the policy doesn’t fit their workflow

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or the labeling tool is too inconvenient to use.

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You don’t see this gap because you aren’t measuring it.

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You see the policy and the training sessions

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which is just compliance theater

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but you don’t see the actual flow of data.

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The uncomfortable truth is that your org chart

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and your data flow chart represent

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two completely different organizations.

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The org chart says sales reports to the VP

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but the data flow might show

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that their decisions are actually driven by a spreadsheet

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owned by a junior analyst in operations.

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The official process says to use the CRM

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but the actual process bypasses it

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because the software is too slow

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or doesn’t have the right fields.

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Your chart says finance owns the customer master data

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but the reality is that the data lives in three different places

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with nobody truly owning it.

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Versions diverge, they never reconcile

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and the chief information security officers protection strategy

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ends up depending on whether a single team member

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remembered to click a button.

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Protection becomes a matter of probability instead of a certainty.

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You are left hoping the system works

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instead of knowing it works

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because you can’t see the actual data flow.

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You stay confident and assume the policy is being followed.

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This confidence is dangerous because it blinds you to risk

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and prevents you from understanding

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what is actually valuable in your company.

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When you try to deploy co-pilot,

258
00:09:11,800 –> 00:09:13,480
you’ll end up giving it access to data

259
00:09:13,480 –> 00:09:15,320
you think is classified but isn’t

260
00:09:15,320 –> 00:09:17,600
and you’ll assume the AI can operate safely

261
00:09:17,600 –> 00:09:20,400
in an environment that is actually quite fragile.

262
00:09:20,400 –> 00:09:23,120
This trap persists because the chain of command is too long

263
00:09:23,120 –> 00:09:24,760
and the system is too distributed

264
00:09:24,760 –> 00:09:26,320
for clear communication to survive.

265
00:09:26,320 –> 00:09:28,640
When a policy is created at the top and passed down

266
00:09:28,640 –> 00:09:30,920
every layer of management interprets a differently

267
00:09:30,920 –> 00:09:32,600
based on their own constraints.

268
00:09:32,600 –> 00:09:34,960
By the time it reaches the person actually doing the work,

269
00:09:34,960 –> 00:09:37,720
the original intent has been filtered through five layers

270
00:09:37,720 –> 00:09:39,040
of organizational assumption.

271
00:09:39,040 –> 00:09:40,360
You believe the system is working

272
00:09:40,360 –> 00:09:43,040
because you can point to a document or a training log

273
00:09:43,040 –> 00:09:45,400
but policies naturally drift over time.

274
00:09:45,400 –> 00:09:47,680
Work rarely conforms to the official process

275
00:09:47,680 –> 00:09:50,040
because the official process is usually less efficient

276
00:09:50,040 –> 00:09:52,840
than the work around people have developed for themselves.

277
00:09:52,840 –> 00:09:54,680
The finance team keeps a local spreadsheet

278
00:09:54,680 –> 00:09:56,920
because it’s faster than waiting for an official report

279
00:09:56,920 –> 00:09:59,960
and the sales team stores deals in a private folder

280
00:09:59,960 –> 00:10:02,320
because the CRM is missing key fields.

281
00:10:02,320 –> 00:10:04,240
These are rational decisions made by people

282
00:10:04,240 –> 00:10:05,880
trying to solve real problems

283
00:10:05,880 –> 00:10:07,760
but they create parallel data flows

284
00:10:07,760 –> 00:10:09,160
where ownership becomes a blur.

285
00:10:09,160 –> 00:10:11,080
You won’t see any of this if you aren’t looking

286
00:10:11,080 –> 00:10:12,040
at the actual data.

287
00:10:12,040 –> 00:10:14,120
This is where Perview changes the picture.

288
00:10:14,120 –> 00:10:15,520
It doesn’t just force compliance,

289
00:10:15,520 –> 00:10:16,920
it makes the invisible visible

290
00:10:16,920 –> 00:10:18,560
so you can see what is actually happening

291
00:10:18,560 –> 00:10:21,040
instead of what you assume is happening.

292
00:10:21,040 –> 00:10:23,120
The three failures of traditional governance.

293
00:10:23,120 –> 00:10:25,800
Traditional governance frameworks almost always fail

294
00:10:25,800 –> 00:10:27,280
in three specific ways,

295
00:10:27,280 –> 00:10:29,600
starting with a total breakdown in communication.

296
00:10:29,600 –> 00:10:31,600
Policies are abstract by nature

297
00:10:31,600 –> 00:10:33,120
but their application is concrete

298
00:10:33,120 –> 00:10:35,760
and the gap between those two things is where confusion lives.

299
00:10:35,760 –> 00:10:37,800
You might create a clear policy for customer data

300
00:10:37,800 –> 00:10:39,160
but does that include just the name

301
00:10:39,160 –> 00:10:41,480
or does it cover account numbers, contact history

302
00:10:41,480 –> 00:10:43,080
and invoice logs as well?

303
00:10:43,080 –> 00:10:45,720
What seems obvious to the person writing the policy

304
00:10:45,720 –> 00:10:47,880
is often ambiguous to the person implementing it

305
00:10:47,880 –> 00:10:49,400
leading to an inconsistent system

306
00:10:49,400 –> 00:10:51,400
where everyone labels things differently.

307
00:10:51,400 –> 00:10:52,600
Communication also fails

308
00:10:52,600 –> 00:10:54,840
because you usually only talk about the policy once

309
00:10:54,840 –> 00:10:56,160
during a training session.

310
00:10:56,160 –> 00:10:57,200
People not and listen

311
00:10:57,200 –> 00:10:59,880
but then they go back to a desk where 15 other things

312
00:10:59,880 –> 00:11:01,480
are competing for their attention

313
00:11:01,480 –> 00:11:03,680
and the policy quickly becomes an afterthought.

314
00:11:03,680 –> 00:11:05,280
Six months later, most of the team

315
00:11:05,280 –> 00:11:07,120
hasn’t thought about that training once

316
00:11:07,120 –> 00:11:09,200
and new hires have missed it entirely.

317
00:11:09,200 –> 00:11:10,960
The policy still exists on paper

318
00:11:10,960 –> 00:11:13,160
but the actual operation has drifted far away

319
00:11:13,160 –> 00:11:14,760
from the original intent.

320
00:11:14,760 –> 00:11:16,680
The second failure is one of enforcement.

321
00:11:16,680 –> 00:11:19,040
You simply cannot enforce what you cannot see.

322
00:11:19,040 –> 00:11:21,200
Most organizations only have visibility

323
00:11:21,200 –> 00:11:23,400
into about 30% of their data

324
00:11:23,400 –> 00:11:25,720
while the rest is shadow data

325
00:11:25,720 –> 00:11:28,800
living in unmonitored systems or local spreadsheets.

326
00:11:28,800 –> 00:11:30,280
Shadow data isn’t a secret

327
00:11:30,280 –> 00:11:32,080
but it is invisible to your governance system

328
00:11:32,080 –> 00:11:34,840
which means you can’t label it, protect it or control it

329
00:11:34,840 –> 00:11:36,880
because it exists outside your perimeter.

330
00:11:36,880 –> 00:11:38,840
It becomes the path of least resistance

331
00:11:38,840 –> 00:11:41,600
for employees who find the official systems too restrictive.

332
00:11:41,600 –> 00:11:42,840
This creates a feedback loop

333
00:11:42,840 –> 00:11:45,000
where more enforcement in the official system

334
00:11:45,000 –> 00:11:47,120
drives more people toward shadow systems.

335
00:11:47,120 –> 00:11:49,400
The more they move away, the less you can enforce

336
00:11:49,400 –> 00:11:52,200
and eventually people stop believing the system matters at all.

337
00:11:52,200 –> 00:11:53,920
The third failure is relevance.

338
00:11:53,920 –> 00:11:56,800
Static policies in a fast moving business environment

339
00:11:56,800 –> 00:11:59,280
eventually become friction for their own sake.

340
00:11:59,280 –> 00:12:01,360
A policy might have made sense two years ago

341
00:12:01,360 –> 00:12:03,880
but as new products launch and markets change,

342
00:12:03,880 –> 00:12:06,280
the old rules don’t evolve with the business.

343
00:12:06,280 –> 00:12:07,680
When policies become irrelevant,

344
00:12:07,680 –> 00:12:09,200
people stop seeing them as governance

345
00:12:09,200 –> 00:12:11,280
and start seeing them as obstacles to be avoided.

346
00:12:11,280 –> 00:12:13,200
We see this constantly with DLP policies

347
00:12:13,200 –> 00:12:15,000
that block credit card numbers.

348
00:12:15,000 –> 00:12:16,480
If a vendor needs a transaction list

349
00:12:16,480 –> 00:12:18,680
that happens to have one card number in a column,

350
00:12:18,680 –> 00:12:20,280
the policy blocks the email

351
00:12:20,280 –> 00:12:22,720
and the team immediately finds a workaround.

352
00:12:22,720 –> 00:12:24,840
They’ll send it through a non-monitor channel

353
00:12:24,840 –> 00:12:27,080
or copy it into a different file type

354
00:12:27,080 –> 00:12:28,120
just to get the job done.

355
00:12:28,120 –> 00:12:29,520
The policy was well intentioned

356
00:12:29,520 –> 00:12:32,200
but because it didn’t account for the actual business need,

357
00:12:32,200 –> 00:12:35,760
it created the very friction that led to the workaround.

358
00:12:35,760 –> 00:12:38,040
These three failures, communication, enforcement

359
00:12:38,040 –> 00:12:40,520
and relevance compound over time.

360
00:12:40,520 –> 00:12:42,600
They create an environment where policies exist

361
00:12:42,600 –> 00:12:44,120
but don’t reflect reality

362
00:12:44,120 –> 00:12:45,760
and where enforcement is attempted

363
00:12:45,760 –> 00:12:48,040
but doesn’t actually cover the data flow.

364
00:12:48,040 –> 00:12:49,240
You are blind to all of this

365
00:12:49,240 –> 00:12:51,520
unless you are looking at the actual data.

366
00:12:51,520 –> 00:12:53,600
The three questions that unlock clarity.

367
00:12:53,600 –> 00:12:55,240
To change this, you need a framework built

368
00:12:55,240 –> 00:12:56,520
on three specific questions.

369
00:12:56,520 –> 00:12:58,040
If you can answer these with confidence,

370
00:12:58,040 –> 00:12:59,720
you understand your organization

371
00:12:59,720 –> 00:13:02,600
but if you can’t, you have dangerous gaps in your strategy.

372
00:13:02,600 –> 00:13:06,280
Question one, do we know where our critical data is?

373
00:13:06,280 –> 00:13:08,160
I’m not asking if you think you know.

374
00:13:08,160 –> 00:13:09,600
I’m asking if you actually know

375
00:13:09,600 –> 00:13:11,400
across every system you own.

376
00:13:11,400 –> 00:13:12,800
Where are the financial records,

377
00:13:12,800 –> 00:13:15,680
the intellectual property and the strategic plans hiding?

378
00:13:15,680 –> 00:13:18,080
Most organizations can only point to the obvious places

379
00:13:18,080 –> 00:13:20,520
like the CRM or the main document repository.

380
00:13:20,520 –> 00:13:22,160
They have no idea about the spreadsheets

381
00:13:22,160 –> 00:13:24,920
in shadow systems or the files sitting in random teams,

382
00:13:24,920 –> 00:13:28,520
folders and cloud storage outside the official infrastructure.

383
00:13:28,520 –> 00:13:30,280
When you can answer this question completely,

384
00:13:30,280 –> 00:13:33,280
you have finally solved the discovery problem.

385
00:13:33,280 –> 00:13:35,520
Question two, do our policies reflect reality

386
00:13:35,520 –> 00:13:36,480
or just intention?

387
00:13:36,480 –> 00:13:38,680
Is your labeling policy actually being followed

388
00:13:38,680 –> 00:13:41,240
on a daily basis and are those labels even correct?

389
00:13:41,240 –> 00:13:42,880
You need to know if your DLP policy

390
00:13:42,880 –> 00:13:44,680
is actually blocking what you think it is

391
00:13:44,680 –> 00:13:47,320
or if your employees are just finding clever ways around it.

392
00:13:47,320 –> 00:13:49,480
The only way to answer this is by measuring the data.

393
00:13:49,480 –> 00:13:51,920
You have to run a scan and compare the actual data

394
00:13:51,920 –> 00:13:55,120
against your policy intent to see where the gaps are.

395
00:13:55,120 –> 00:13:56,400
That gap is your new priority

396
00:13:56,400 –> 00:13:58,320
because that is where your risk lives.

397
00:13:58,320 –> 00:14:01,120
Question three, are we managing behavior or assuming it?

398
00:14:01,120 –> 00:14:03,920
Are you actively measuring who is accessing your data

399
00:14:03,920 –> 00:14:05,720
or are you just hoping everything is fine

400
00:14:05,720 –> 00:14:07,280
because you have a policy in place?

401
00:14:07,280 –> 00:14:09,240
Active measurement means you can see patterns,

402
00:14:09,240 –> 00:14:11,160
detect anomalies and notice when someone

403
00:14:11,160 –> 00:14:14,080
accesses data outside their normal role.

404
00:14:14,080 –> 00:14:16,280
Assumption is the belief that the system is working

405
00:14:16,280 –> 00:14:18,560
simply because you haven’t seen evidence

406
00:14:18,560 –> 00:14:19,720
that it’s failing yet.

407
00:14:19,720 –> 00:14:22,480
These three questions form the data reality check framework

408
00:14:22,480 –> 00:14:24,080
forcing you to move away from belief

409
00:14:24,080 –> 00:14:25,720
and toward actual measurement.

410
00:14:25,720 –> 00:14:28,360
When you answer these questions with real data,

411
00:14:28,360 –> 00:14:31,040
you stop operating on hope and start operating on facts.

412
00:14:31,040 –> 00:14:32,920
Your organization becomes legible

413
00:14:32,920 –> 00:14:35,800
and while it won’t be perfect, it will finally be visible.

414
00:14:35,800 –> 00:14:38,480
Visibility is exactly where real change starts.

415
00:14:38,480 –> 00:14:40,280
What your data actually reveals.

416
00:14:40,280 –> 00:14:41,960
Now that we’ve established the framework,

417
00:14:41,960 –> 00:14:43,360
those three core questions,

418
00:14:43,360 –> 00:14:45,920
we need to look at what the data is actually screaming at you

419
00:14:45,920 –> 00:14:47,400
when you open the hood.

420
00:14:47,400 –> 00:14:51,000
Microsoft PerView generally hands you three primary signals.

421
00:14:51,000 –> 00:14:52,560
Sensitivity labels,

422
00:14:52,560 –> 00:14:55,840
DLP violations and insider risk indicators.

423
00:14:55,840 –> 00:14:59,400
Most leadership teams treat these as boring security artifacts

424
00:14:59,400 –> 00:15:01,960
or compliance objects that belong in a quarterly report,

425
00:15:01,960 –> 00:15:03,400
but they aren’t just check boxes.

426
00:15:03,400 –> 00:15:05,600
They are behavioral data points that map out

427
00:15:05,600 –> 00:15:08,120
how your organization actually thinks about information

428
00:15:08,120 –> 00:15:11,320
and where the structure is currently under life-threatening stress.

429
00:15:11,320 –> 00:15:13,000
When you learn how to read these signals,

430
00:15:13,000 –> 00:15:15,080
PerView stops being a security tool

431
00:15:15,080 –> 00:15:18,960
and becomes a diagnostic lens for your entire business reality.

432
00:15:18,960 –> 00:15:22,400
Sensitivity labels as a map of business criticality.

433
00:15:22,400 –> 00:15:25,320
Here is the uncomfortable truth about sensitivity labels.

434
00:15:25,320 –> 00:15:27,760
They don’t actually measure how sensitive your data is.

435
00:15:27,760 –> 00:15:30,440
Instead, they measure how your people perceive value and risk,

436
00:15:30,440 –> 00:15:31,800
which is a very different thing.

437
00:15:31,800 –> 00:15:34,880
This distinction matters because the theory of data classification

438
00:15:34,880 –> 00:15:36,600
is always much cleaner than the reality.

439
00:15:36,600 –> 00:15:39,800
In a perfect world, you define categories like internal, confidential

440
00:15:39,800 –> 00:15:40,800
and highly confidential,

441
00:15:40,800 –> 00:15:43,800
then everyone applies them perfectly so that data flows securely.

442
00:15:43,800 –> 00:15:46,840
In practice, labels are a mirror of organizational anxiety

443
00:15:46,840 –> 00:15:48,200
and cultural confusion.

444
00:15:48,200 –> 00:15:51,360
If you scan your environment and find that 85% of your files

445
00:15:51,360 –> 00:15:53,040
are marked highly confidential,

446
00:15:53,040 –> 00:15:55,360
it doesn’t mean you’re the most secretive company on Earth.

447
00:15:55,360 –> 00:15:58,040
It means your team doesn’t actually know what matters,

448
00:15:58,040 –> 00:16:00,280
so they’ve decided that everything is important,

449
00:16:00,280 –> 00:16:02,080
which effectively means nothing is.

450
00:16:02,080 –> 00:16:05,040
The label has lost all signal value and turned into pure noise.

451
00:16:05,040 –> 00:16:06,640
Compare that to a healthy distribution

452
00:16:06,640 –> 00:16:10,560
where you see a graduated scale of public, internal, and restricted data.

453
00:16:10,560 –> 00:16:12,240
That spread tells me the organization

454
00:16:12,240 –> 00:16:15,120
has made intentional decisions about what truly carries value.

455
00:16:15,120 –> 00:16:16,760
The difference isn’t the data itself.

456
00:16:16,760 –> 00:16:19,440
It’s the level of clarity the leadership has provided

457
00:16:19,440 –> 00:16:21,240
about what the company actually protects.

458
00:16:21,240 –> 00:16:24,360
When a finance team overclassifies every routine spreadsheet

459
00:16:24,360 –> 00:16:27,360
as highly confidential, they aren’t being extra cautious.

460
00:16:27,360 –> 00:16:29,040
They are admitting they can’t differentiate

461
00:16:29,040 –> 00:16:31,720
between a critical fiscal secret and a standard report,

462
00:16:31,720 –> 00:16:34,080
so they lock everything behind the same heavy door.

463
00:16:34,080 –> 00:16:36,160
This creates massive operational friction

464
00:16:36,160 –> 00:16:38,480
because you can’t optimize permissions or move quickly

465
00:16:38,480 –> 00:16:41,600
when every single file requires the highest level of clearance.

466
00:16:41,600 –> 00:16:44,880
The labeling data is revealing your maturity in decision making

467
00:16:44,880 –> 00:16:46,920
rather than your strength in security.

468
00:16:46,920 –> 00:16:48,560
We also see the opposite pattern,

469
00:16:48,560 –> 00:16:51,280
massive amounts of sensitive, clinical, or financial data

470
00:16:51,280 –> 00:16:52,360
with no labels at all.

471
00:16:52,360 –> 00:16:54,800
This is a sign that the organization has essentially given up

472
00:16:54,800 –> 00:16:57,800
and the labeling system has become so invisible or difficult

473
00:16:57,800 –> 00:16:59,000
that people just ignore it.

474
00:16:59,000 –> 00:17:00,720
This is governance theater where the actors

475
00:17:00,720 –> 00:17:02,680
have stopped showing up for rehearsals.

476
00:17:02,680 –> 00:17:04,560
When you look at these distributions in Pervue,

477
00:17:04,560 –> 00:17:06,040
you are looking at a behavioral map

478
00:17:06,040 –> 00:17:08,800
of what your organization actually prioritizes

479
00:17:08,800 –> 00:17:11,560
versus what it claims to protect in a policy manual.

480
00:17:11,560 –> 00:17:14,880
These patterns also tell you exactly where change will be the hardest.

481
00:17:14,880 –> 00:17:17,040
Teams with consistent, accurate labeling

482
00:17:17,040 –> 00:17:20,280
have already built the operational muscle to handle data with care

483
00:17:20,280 –> 00:17:21,320
and they will adapt quickly

484
00:17:21,320 –> 00:17:24,400
when you introduce new AI tools or workflows.

485
00:17:24,400 –> 00:17:27,720
Teams that haven’t labeled a single document have no such muscle

486
00:17:27,720 –> 00:17:30,240
and they will require a complete shift in mindset

487
00:17:30,240 –> 00:17:32,520
before they can safely use modern automation.

488
00:17:32,520 –> 00:17:35,120
This is why labeling isn’t just a compliance report.

489
00:17:35,120 –> 00:17:37,600
It’s a high-level organizational diagnostic.

490
00:17:37,600 –> 00:17:40,320
DLP violations as windows into broken processes,

491
00:17:40,320 –> 00:17:43,560
data loss prevention violations are almost never security events.

492
00:17:43,560 –> 00:17:47,160
In my experience, they are almost always indicators of process friction

493
00:17:47,160 –> 00:17:49,880
where the official way of working has become a burden.

494
00:17:49,880 –> 00:17:52,080
When a team repeatedly triggers a DLP alert,

495
00:17:52,080 –> 00:17:54,720
they usually aren’t trying to be reckless or malicious.

496
00:17:54,720 –> 00:17:56,640
They are simply trying to solve a real-world problem

497
00:17:56,640 –> 00:17:59,880
that your official secure process makes impossible to finish on time.

498
00:17:59,880 –> 00:18:01,880
If you want to find a rot in your operations,

499
00:18:01,880 –> 00:18:03,680
look at where the violations cluster.

500
00:18:03,680 –> 00:18:06,600
You might see a sales team constantly trying to email customer lists

501
00:18:06,600 –> 00:18:08,040
because the CRM is too slow

502
00:18:08,040 –> 00:18:11,760
or doesn’t have the specific fields they need to hit their end of month targets.

503
00:18:11,760 –> 00:18:13,400
They aren’t trying to steal the data.

504
00:18:13,400 –> 00:18:14,880
They are trying to do their jobs

505
00:18:14,880 –> 00:18:17,520
and the approved method is simply slower than the workaround.

506
00:18:17,520 –> 00:18:20,840
We see the same thing in finance when teams move data between blocked systems

507
00:18:20,840 –> 00:18:22,800
because the official integration takes two weeks

508
00:18:22,800 –> 00:18:24,360
and they need the numbers now.

509
00:18:24,360 –> 00:18:28,040
Customer service teams often trigger these rules by sharing contacts with partners

510
00:18:28,040 –> 00:18:32,720
because the official support workflow didn’t account for how they actually collaborate in the real world.

511
00:18:32,720 –> 00:18:36,240
These violations are a loud signal that your official rules are misaligned

512
00:18:36,240 –> 00:18:38,240
with how work actually gets done.

513
00:18:38,240 –> 00:18:41,480
Most organizations see a violation and immediately try to tighten the screws

514
00:18:41,480 –> 00:18:43,440
by adding more blocks and more rules.

515
00:18:43,440 –> 00:18:46,720
What they should be doing is asking why the official process is failing

516
00:18:46,720 –> 00:18:49,680
the people who are actually trying to generate value for the company.

517
00:18:49,680 –> 00:18:54,400
It is very rare that the behavior is purely wrong while the policy is right.

518
00:18:54,400 –> 00:18:56,200
More often, the process is structurally broken

519
00:18:56,200 –> 00:18:58,920
and needs to be redesigned to match the speed of the business.

520
00:18:58,920 –> 00:19:00,760
When you run per view in audit mode,

521
00:19:00,760 –> 00:19:03,480
you get a clear picture of where your friction points live.

522
00:19:03,480 –> 00:19:05,760
High concentrations of violations in one department

523
00:19:05,760 –> 00:19:09,120
tell you that the organization hasn’t designed a workflow that actually works

524
00:19:09,120 –> 00:19:11,320
for that specific team’s reality.

525
00:19:11,320 –> 00:19:14,120
A finance team spending two weeks on reconciliation

526
00:19:14,120 –> 00:19:16,920
because they have to manually sync three different data sources

527
00:19:16,920 –> 00:19:19,360
is a DLP violation waiting to happen.

528
00:19:19,360 –> 00:19:21,160
A sales team keeping a shadow database

529
00:19:21,160 –> 00:19:24,640
because the CRM is a nightmare is another disaster in the making.

530
00:19:24,640 –> 00:19:27,000
Looking at these patterns gives you the business intelligence

531
00:19:27,000 –> 00:19:29,240
needed to decide where to invest in better systems.

532
00:19:29,240 –> 00:19:30,720
You aren’t just stopping leaks.

533
00:19:30,720 –> 00:19:34,440
You are identifying where the pipes were poorly designed in the first place.

534
00:19:34,440 –> 00:19:37,320
Inside a risk as organizational stress detection,

535
00:19:37,320 –> 00:19:40,120
we need to reframe how we look at inside a risk management

536
00:19:40,120 –> 00:19:42,480
because these signals are about organizational pressure,

537
00:19:42,480 –> 00:19:44,160
not just internal threats.

538
00:19:44,160 –> 00:19:47,600
When you see an access spike in finance right before the quarter ends,

539
00:19:47,600 –> 00:19:48,600
that isn’t a red flag.

540
00:19:48,600 –> 00:19:51,400
It’s the sound of the engine revving during a normal business cycle.

541
00:19:51,400 –> 00:19:52,960
The team is accessing more data

542
00:19:52,960 –> 00:19:55,160
because the reconciliation process demands it.

543
00:19:55,160 –> 00:19:57,920
The same logic applies to a team exploring new systems

544
00:19:57,920 –> 00:19:59,760
during a merger or acquisition.

545
00:19:59,760 –> 00:20:03,040
Their behavior changes because the business environment has changed

546
00:20:03,040 –> 00:20:06,160
and the access patterns are simply reflecting that new reality.

547
00:20:06,160 –> 00:20:08,800
However, when you see elevated access during a period

548
00:20:08,800 –> 00:20:10,800
where a team is severely understaffed,

549
00:20:10,800 –> 00:20:13,400
you are looking at a structural stress signal.

550
00:20:13,400 –> 00:20:15,600
One person is likely doing the work of three

551
00:20:15,600 –> 00:20:19,400
and they are working odd hours and touching more data just to keep the lights on.

552
00:20:19,400 –> 00:20:21,120
Per view might flag this as risky,

553
00:20:21,120 –> 00:20:24,960
but the reality is that the person is just exhausted and overextended.

554
00:20:24,960 –> 00:20:27,680
Inside a risk signals often correlate more closely

555
00:20:27,680 –> 00:20:31,720
with burnout and systemic friction than they do with actual malicious intent.

556
00:20:31,720 –> 00:20:35,760
A team with high risk scores is usually a team that is being asked to do too much with too little.

557
00:20:35,760 –> 00:20:39,040
They are compensating for bad architecture by putting in extra effort

558
00:20:39,040 –> 00:20:41,360
and they are often under immense deadline pressure

559
00:20:41,360 –> 00:20:43,280
that forces them to take shortcuts.

560
00:20:43,280 –> 00:20:45,320
If you treat this as a security problem,

561
00:20:45,320 –> 00:20:49,000
you’ll run an investigation and add more friction to an already stressed department.

562
00:20:49,000 –> 00:20:50,640
If you treat it as a system outcome,

563
00:20:50,640 –> 00:20:52,680
you’ll ask what is broken in the workflow

564
00:20:52,680 –> 00:20:54,240
and how you can make their jobs easier.

565
00:20:54,240 –> 00:20:57,360
One approach treats your people as potential threats to be managed.

566
00:20:57,360 –> 00:20:59,120
The other treats your people as sensors

567
00:20:59,120 –> 00:21:02,760
that are telling you exactly where your organizational design is failing.

568
00:21:02,760 –> 00:21:06,240
Using insider risk as a stress detector allows you to identify burnout

569
00:21:06,240 –> 00:21:07,560
before your best people quit.

570
00:21:07,560 –> 00:21:10,280
You can catch understaffed teams before they collapse

571
00:21:10,280 –> 00:21:14,960
and spot architectural flaws that are forcing people into dangerous shadow systems.

572
00:21:14,960 –> 00:21:18,160
The three signals labels DLP and insider risk

573
00:21:18,160 –> 00:21:19,960
are not separate streams of data.

574
00:21:19,960 –> 00:21:23,720
They are three different angles on the same reality of how your company operates

575
00:21:23,720 –> 00:21:25,320
versus how you think it operates.

576
00:21:25,320 –> 00:21:28,560
Label distribution shows you what the organization truly values

577
00:21:28,560 –> 00:21:31,720
while DLP violations show you where the processes are broken.

578
00:21:31,720 –> 00:21:35,400
Insider risk signals show you where the pressure is becoming unbearable.

579
00:21:35,400 –> 00:21:37,680
Together, they create a diagnostic picture

580
00:21:37,680 –> 00:21:40,680
that we can finally translate into real business action.

581
00:21:40,680 –> 00:21:42,080
Per view as an X-ray.

582
00:21:42,080 –> 00:21:44,040
Now that we have this diagnostic data,

583
00:21:44,040 –> 00:21:46,200
the next step is deciding what to do with it.

584
00:21:46,200 –> 00:21:48,960
Whether Per view becomes a useless compliance checkbox

585
00:21:48,960 –> 00:21:51,360
or a transformative visibility tool

586
00:21:51,360 –> 00:21:53,240
depends entirely on your mental approach.

587
00:21:53,240 –> 00:21:56,680
You have to stop looking at this data as evidence of being good

588
00:21:56,680 –> 00:21:59,120
and start looking at it as evidence of what is.

589
00:21:59,120 –> 00:22:02,560
You are treating these patterns as a map of the actual organization

590
00:22:02,560 –> 00:22:05,960
which is almost never the one described in your official documents.

591
00:22:05,960 –> 00:22:09,680
The org chart is a theoretical model that shows who reports to whom

592
00:22:09,680 –> 00:22:13,160
but it doesn’t show you how work actually moves through the building.

593
00:22:13,160 –> 00:22:15,200
Per view data is the model of reality,

594
00:22:15,200 –> 00:22:17,080
showing you where information flows,

595
00:22:17,080 –> 00:22:18,560
where dependencies live

596
00:22:18,560 –> 00:22:20,680
and where friction is slowing everything down.

597
00:22:20,680 –> 00:22:23,280
When you examine access patterns and violation clusters,

598
00:22:23,280 –> 00:22:27,600
you are seeing the real organization that lives underneath the one on the PowerPoint slides.

599
00:22:27,600 –> 00:22:30,960
And the truth is, those two versions of your company rarely match.

600
00:22:30,960 –> 00:22:33,080
Once you start treating Per view as an X-ray,

601
00:22:33,080 –> 00:22:34,800
you stop asking if you are compliant

602
00:22:34,800 –> 00:22:37,440
and start asking what the data says about your business.

603
00:22:37,440 –> 00:22:39,520
Overclassification isn’t a failure.

604
00:22:39,520 –> 00:22:42,480
It’s a sign of deep, organizational uncertainty

605
00:22:42,480 –> 00:22:44,320
about what is actually valuable.

606
00:22:44,320 –> 00:22:46,480
DLP clusters aren’t just policy breaks.

607
00:22:46,480 –> 00:22:48,000
They are architectural failures

608
00:22:48,000 –> 00:22:50,880
where the official system didn’t account for the speed of the market.

609
00:22:50,880 –> 00:22:53,680
Inside a risk concentrations aren’t just security threats.

610
00:22:53,680 –> 00:22:56,680
They are the heat maps of where your people are most likely to burn out.

611
00:22:56,680 –> 00:23:00,440
These are structural signals that tell you how your company is actually built.

612
00:23:00,440 –> 00:23:03,440
Seeing the actual organization, not the theoretical one.

613
00:23:03,440 –> 00:23:08,200
Your org chart says sales reports to the VP and finance reports to the CFO.

614
00:23:08,200 –> 00:23:11,560
It’s a clean logical structure that looks great in an annual report.

615
00:23:11,560 –> 00:23:15,080
But the Per view data tells the story of the actual decision-making structure.

616
00:23:15,080 –> 00:23:18,000
It shows you which teams are truly the pillars of your operations

617
00:23:18,000 –> 00:23:19,920
and where information gets trapped.

618
00:23:19,920 –> 00:23:22,080
Because a specific role has become a bottleneck.

619
00:23:22,080 –> 00:23:24,000
If you look at actual access patterns,

620
00:23:24,000 –> 00:23:26,760
who is reading, modifying and sharing what,

621
00:23:26,760 –> 00:23:29,520
you might find that a small junior operations team

622
00:23:29,520 –> 00:23:32,000
is actually holding up three major workflows.

623
00:23:32,000 –> 00:23:34,640
They might be the only ones who understand a legacy system

624
00:23:34,640 –> 00:23:38,000
making them an infrastructure pillar that the org chart completely ignores.

625
00:23:38,000 –> 00:23:41,480
You might find a junior analyst who is the sole maintainer of a data source

626
00:23:41,480 –> 00:23:44,480
that five different departments rely on every single day.

627
00:23:44,480 –> 00:23:46,160
The org chart doesn’t show that relationship,

628
00:23:46,160 –> 00:23:48,840
but the Per view data makes it impossible to miss.

629
00:23:48,840 –> 00:23:51,920
In many cases, you’ll find that teams are making decisions faster

630
00:23:51,920 –> 00:23:54,400
by bypassing formal processes not to be rebellious,

631
00:23:54,400 –> 00:23:55,840
but to keep the business moving.

632
00:23:55,840 –> 00:23:59,040
The actual organization is revealed by these data flows and dependencies

633
00:23:59,040 –> 00:24:01,120
not by the lines drawn on a piece of paper.

634
00:24:01,120 –> 00:24:02,560
When you see the company this way,

635
00:24:02,560 –> 00:24:04,680
silos finally become visible.

636
00:24:04,680 –> 00:24:07,320
A silo isn’t just a team that won’t share.

637
00:24:07,320 –> 00:24:11,760
It’s a natural boundary that forms when information can’t flow easily between groups.

638
00:24:11,760 –> 00:24:14,400
You see this most clearly in data duplication,

639
00:24:14,400 –> 00:24:18,560
where the same customer list is maintained in three different systems by three different teams.

640
00:24:18,560 –> 00:24:20,120
This isn’t a storage problem.

641
00:24:20,120 –> 00:24:23,240
It’s a coordination failure where teams have given up on the official system

642
00:24:23,240 –> 00:24:24,680
because it’s too hard to use.

643
00:24:24,680 –> 00:24:28,200
Because there is too much friction in getting data from one team to another,

644
00:24:28,200 –> 00:24:29,920
people create their own copies.

645
00:24:29,920 –> 00:24:32,240
Per view shows you exactly where these shadow copies live

646
00:24:32,240 –> 00:24:34,120
and where the versions are starting to diverge.

647
00:24:34,120 –> 00:24:36,040
This is the true definition of a silo,

648
00:24:36,040 –> 00:24:39,720
an information architecture problem that forces people to work in isolation.

649
00:24:39,720 –> 00:24:43,720
Secondly, you start to see the true dependencies that don’t follow reporting lines.

650
00:24:43,720 –> 00:24:45,480
If a warehouse system goes down,

651
00:24:45,480 –> 00:24:47,880
the org chart says the warehouse team is affected,

652
00:24:47,880 –> 00:24:52,480
but the data shows that pricing, forecasting, and fulfillment all grind to a halt as well.

653
00:24:52,480 –> 00:24:54,320
These aren’t failures of the org chart itself.

654
00:24:54,320 –> 00:24:58,040
It’s just that the chart was never meant to show how data connects your departments.

655
00:24:58,040 –> 00:25:00,560
Per view shows those operational relationships

656
00:25:00,560 –> 00:25:02,840
and those are the only ones that matter when something breaks.

657
00:25:02,840 –> 00:25:06,840
Finally, this makes architectural inefficiency something you can actually quantify.

658
00:25:06,840 –> 00:25:09,080
You can see the manual work that should be automated

659
00:25:09,080 –> 00:25:12,040
and the redundant data movement that is eating up your team’s time.

660
00:25:12,040 –> 00:25:15,680
When a finance team spends 80 hours a month on manual reconciliation,

661
00:25:15,680 –> 00:25:19,360
Per view shows you the three different sources of truth that are causing the headache.

662
00:25:19,360 –> 00:25:22,400
Eliminate that duplication and you don’t just improve security.

663
00:25:22,400 –> 00:25:25,400
You save thousands of dollars in wasted human effort.

664
00:25:25,400 –> 00:25:27,880
Identifying silos through data duplication.

665
00:25:27,880 –> 00:25:31,560
Let’s look at a concrete example of how you use Per view to find these silos.

666
00:25:31,560 –> 00:25:34,840
Imagine a financial firm with three different customer databases,

667
00:25:34,840 –> 00:25:39,600
one in Salesforce, one in the core banking system, and one in a data warehouse.

668
00:25:39,600 –> 00:25:43,400
On the org chart, these are three separate divisions that answer to different leaders

669
00:25:43,400 –> 00:25:45,600
and have no formal reason to talk to each other.

670
00:25:45,600 –> 00:25:48,920
But when you look at the data, you see the exact same customers

671
00:25:48,920 –> 00:25:51,200
and overlapping fields across all three systems.

672
00:25:51,200 –> 00:25:52,920
The Salesforce version has the contact info,

673
00:25:52,920 –> 00:25:56,440
the banking system has the transactions and the warehouse has the behavioral history

674
00:25:56,440 –> 00:25:58,480
because there is no sync mechanism.

675
00:25:58,480 –> 00:26:02,160
A phone number change in Salesforce never makes it to the other two systems.

676
00:26:02,160 –> 00:26:06,000
Months later, a marketing campaign fails because the analytics team pulled all data

677
00:26:06,000 –> 00:26:07,000
from the warehouse.

678
00:26:07,000 –> 00:26:10,160
From a leadership perspective, this looks like three separate systems,

679
00:26:10,160 –> 00:26:13,320
but from a data perspective, it is a massive coordination failure.

680
00:26:13,320 –> 00:26:17,840
Per view identifies where this data is stored and how it’s being accessed by different teams.

681
00:26:17,840 –> 00:26:21,120
It’s the fact that the same data exists in three places with different labels

682
00:26:21,120 –> 00:26:24,240
and different controls is a structural problem, not a policy one.

683
00:26:24,240 –> 00:26:27,400
You don’t fix this by writing a new memo about data integrity.

684
00:26:27,400 –> 00:26:29,200
You fix it by redesigning the architecture

685
00:26:29,200 –> 00:26:32,240
so that information propagates automatically across the entire company.

686
00:26:32,240 –> 00:26:36,440
Per view doesn’t fix the silo for you, but it makes the cost of that silo visible.

687
00:26:36,440 –> 00:26:39,280
It quantifies the redundancy and the consistency gaps

688
00:26:39,280 –> 00:26:41,600
that are currently quietly draining your resources.

689
00:26:41,600 –> 00:26:44,640
Visualizing true dependencies and critical parts.

690
00:26:44,640 –> 00:26:49,040
Per view also reveals that not all critical infrastructure looks important on an org chart.

691
00:26:49,040 –> 00:26:53,160
I’ve seen data teams that are buried deep in IT maintaining ETL processes

692
00:26:53,160 –> 00:26:57,040
that move data between systems on paper, they are a utility function.

693
00:26:57,040 –> 00:27:00,560
But the access data shows that the entire company’s analytics, reporting

694
00:27:00,560 –> 00:27:03,480
and financial close processes depend entirely on their work.

695
00:27:03,480 –> 00:27:07,000
If that small team goes down, the major workflows of the entire company stop.

696
00:27:07,000 –> 00:27:10,000
They are a critical path even if their titles don’t reflect it.

697
00:27:10,000 –> 00:27:14,320
We see the same thing with junior analysts who maintain unofficial pricing spreadsheets.

698
00:27:14,320 –> 00:27:18,280
They aren’t managers, but the sales team uses their sheet to quote customers

699
00:27:18,280 –> 00:27:21,160
and finance uses it to forecast the entire year.

700
00:27:21,160 –> 00:27:23,520
If that analyst leaves or the spreadsheet breaks,

701
00:27:23,520 –> 00:27:26,760
three different departments lose their ability to function correctly.

702
00:27:26,760 –> 00:27:29,200
The org chart shows a junior employee,

703
00:27:29,200 –> 00:27:33,240
the per view data shows a single point of failure that could derail the quarter.

704
00:27:33,240 –> 00:27:36,920
Organizational resilience comes from understanding these hidden dependencies.

705
00:27:36,920 –> 00:27:40,640
You need to know where one person or one process failure would cause a massive

706
00:27:40,640 –> 00:27:42,640
cascading disruption to the business.

707
00:27:42,640 –> 00:27:47,560
Per views, access patterns and lineage data show you which systems are actually critical to which teams.

708
00:27:47,560 –> 00:27:50,640
It moves you away from guessing based on titles and toward knowing

709
00:27:50,640 –> 00:27:52,880
based on actual operational reality.

710
00:27:52,880 –> 00:27:54,640
Exposing operational waste.

711
00:27:54,640 –> 00:27:57,920
The final category of inside per view provides is operational waste.

712
00:27:57,920 –> 00:28:00,440
The work that your people are doing that shouldn’t have to happen at all.

713
00:28:00,440 –> 00:28:04,480
Think about that finance team spending two weeks every month just to close the books.

714
00:28:04,480 –> 00:28:10,280
They are manually comparing data sources, finding discrepancies and fixing inconsistencies for hours on end.

715
00:28:10,280 –> 00:28:13,760
They are doing this because the customer data, billing data and ledger data

716
00:28:13,760 –> 00:28:16,480
all live in different models with different refresh times.

717
00:28:16,480 –> 00:28:19,160
Per view shows you this mess in high definition.

718
00:28:19,160 –> 00:28:21,960
Three different sources of truth that never talk to each other.

719
00:28:21,960 –> 00:28:24,000
You can actually put a dollar amount on this.

720
00:28:24,000 –> 00:28:26,280
If four people are spending 80 hours a month on this,

721
00:28:26,280 –> 00:28:30,120
you are burning nearly a thousand hours a year on a problem that shouldn’t exist.

722
00:28:30,120 –> 00:28:36,440
At a standard rate, that’s over 70,000 dollars a year spent on manual labor that could be solved with better architecture.

723
00:28:36,440 –> 00:28:40,560
The problem isn’t that the finance team is incompetent, it’s that the systems are disconnected.

724
00:28:40,560 –> 00:28:45,000
When you fix the architecture and automate that flow, the waste disappears.

725
00:28:45,000 –> 00:28:47,480
And the close happens in two days instead of two weeks.

726
00:28:47,480 –> 00:28:51,880
Per view makes this waste visible so you can finally justify the investment to fix the root cause.

727
00:28:51,880 –> 00:28:54,880
This is the real power of using per view as an x-ray.

728
00:28:54,880 –> 00:28:57,480
It isn’t just about staying out of trouble with regulators.

729
00:28:57,480 –> 00:29:02,200
It’s about understanding where your money is being wasted and where your processes are too brittle to survive.

730
00:29:02,200 –> 00:29:03,960
The org chart is a static dead model.

731
00:29:03,960 –> 00:29:06,080
Per view data is a dynamic living one.

732
00:29:06,080 –> 00:29:09,280
As we move into an era of AI and autonomous workflows,

733
00:29:09,280 –> 00:29:13,720
the dynamic model is the only one that will actually tell you the truth about your business.

734
00:29:13,720 –> 00:29:16,280
From risk signals to business intelligence.

735
00:29:16,280 –> 00:29:17,920
Now you have the diagnostic picture.

736
00:29:17,920 –> 00:29:22,560
You’ve seen the silos, you’ve identified the true dependencies and you’ve finally quantified the waste.

737
00:29:22,560 –> 00:29:25,440
But the real question is, what do you actually do with this information?

738
00:29:25,440 –> 00:29:28,720
This is where per view transforms from a diagnostic tool into an action tool.

739
00:29:28,720 –> 00:29:32,880
It’s the moment you move from just understanding the problem to actually solving it.

740
00:29:32,880 –> 00:29:36,560
And these three specific audits will become your roadmap.

741
00:29:36,560 –> 00:29:38,280
The classification clarity audit.

742
00:29:38,280 –> 00:29:41,920
The first audit is about understanding what your organization actually values.

743
00:29:41,920 –> 00:29:46,000
You’re going to systematically examine how the organization classifies its data.

744
00:29:46,000 –> 00:29:48,880
But I’m not just talking about whether a label exists.

745
00:29:48,880 –> 00:29:52,400
You need to look at the distribution, see which teams label consistently,

746
00:29:52,400 –> 00:29:54,160
and identify who is falling behind.

747
00:29:54,160 –> 00:29:58,640
This labeling behavior is a direct window into your organizational maturity and clarity.

748
00:29:58,640 –> 00:30:00,000
Here’s how you run this audit.

749
00:30:00,000 –> 00:30:04,240
You take the per view scan data and look at the labeling distribution across the board.

750
00:30:04,240 –> 00:30:09,240
You break it down by department, by system, and by the specific person doing the work to see what patterns emerge.

751
00:30:09,240 –> 00:30:14,640
For example, you might find the finance department has labeled 85% of its data as highly confidential.

752
00:30:14,640 –> 00:30:15,440
Why is that?

753
00:30:15,440 –> 00:30:18,880
Is it because finance genuinely handles more sensitive data than everyone else?

754
00:30:18,880 –> 00:30:23,360
Or is it because they don’t actually know what sensitive and are just airing on the side of caution?

755
00:30:23,360 –> 00:30:25,360
Go deeper and look at what they actually marked.

756
00:30:25,360 –> 00:30:28,000
Is it every single spreadsheet or just certain types?

757
00:30:28,000 –> 00:30:31,680
Are they classifying salary information the same way they classify a simple invoice?

758
00:30:31,680 –> 00:30:35,120
And are they treating customer transactions the same as internal ones?

759
00:30:35,120 –> 00:30:38,800
When you drill into the specifics, you usually find a lot of inconsistency.

760
00:30:38,800 –> 00:30:44,160
You’ll see public facing data labeled highly confidential while truly sensitive files are marked as basic,

761
00:30:44,160 –> 00:30:47,120
which means the classification doesn’t reflect the actual risk.

762
00:30:47,120 –> 00:30:49,120
It reflects organizational anxiety.

763
00:30:49,120 –> 00:30:52,320
Now look at sales where they’ve only labeled 15% of their data.

764
00:30:52,320 –> 00:30:54,080
Most of it is marked as confidential.

765
00:30:54,080 –> 00:30:58,080
Even though they handle deal info, customer contacts, and pricing.

766
00:30:58,080 –> 00:31:00,800
Everything is classified the same way with zero differentiation.

767
00:31:00,800 –> 00:31:04,720
This tells a different story because sales isn’t anxious, they’re just ignoring the process.

768
00:31:04,720 –> 00:31:10,560
They’ve labeled just enough to satisfy a policy requirement without actually thinking about what’s inside the files,

769
00:31:10,560 –> 00:31:14,400
essentially applying a blanket label so they can move on to the next task.

770
00:31:14,400 –> 00:31:17,680
The answer to whether your labeling correctly is different for every team.

771
00:31:17,680 –> 00:31:21,120
Finance needs to learn the difference between what’s truly sensitive and what isn’t,

772
00:31:21,120 –> 00:31:23,280
so they can move toward nuanced classification.

773
00:31:23,280 –> 00:31:27,280
Sales on the other hand needs to move from indifference to actual engagement.

774
00:31:27,280 –> 00:31:30,400
The classification clarity audit doesn’t just tell you if you’re compliant.

775
00:31:30,400 –> 00:31:35,280
It tells you how mature the organization’s thinking is regarding information sensitivity.

776
00:31:35,280 –> 00:31:37,120
That answer shapes your entire response.

777
00:31:37,120 –> 00:31:42,080
If finance is overclassifying, you might relax controls and help them understand what’s actually valuable,

778
00:31:42,080 –> 00:31:45,440
but if sales is ignoring the rules, you might need to tighten enforcement

779
00:31:45,440 –> 00:31:46,880
until the behavior changes.

780
00:31:46,880 –> 00:31:48,800
But you only know which path to take.

781
00:31:48,800 –> 00:31:51,440
If you run the audit and look at what the data is telling you.

782
00:31:51,440 –> 00:31:56,560
Per view also shows you which teams have built muscle around classification through consistent patterns.

783
00:31:56,560 –> 00:31:58,480
Consistency is a sign of maturity.

784
00:31:58,480 –> 00:32:02,160
And if a team labels the same data the same way every time they’ll adapt quickly

785
00:32:02,160 –> 00:32:03,440
when your requirements change.

786
00:32:03,440 –> 00:32:08,560
If a team is inconsistent where the same file type gets three different labels from three different people,

787
00:32:08,560 –> 00:32:11,280
you’re looking at a group that hasn’t internalized the system.

788
00:32:11,280 –> 00:32:14,320
They’re treating it as a box to check rather than a decision to make.

789
00:32:14,320 –> 00:32:16,480
This is a true organizational diagnostic.

790
00:32:16,480 –> 00:32:20,640
From these patterns you learn how well different departments understand their own information,

791
00:32:20,640 –> 00:32:26,000
which tells you exactly where to invest in education or where to add automation to reduce the guesswork.

792
00:32:26,000 –> 00:32:28,240
The policy reality alignment assessment.

793
00:32:28,240 –> 00:32:32,960
The second audit is about the gap between what you’re trying to do and what’s actually happening on the ground.

794
00:32:32,960 –> 00:32:36,560
You likely have a policy stating all customer data must be labeled

795
00:32:36,560 –> 00:32:39,200
and you’ve probably done the training and set the expectations.

796
00:32:39,200 –> 00:32:40,480
Now it’s time to measure the gap.

797
00:32:40,480 –> 00:32:44,160
Run a scan across your systems and count how much customer data actually has a label.

798
00:32:44,160 –> 00:32:49,840
If you find only 30% is covered that’s your policy reality gap meaning 70% of your customer data

799
00:32:49,840 –> 00:32:52,480
is sitting there without the protection you require.

800
00:32:52,480 –> 00:32:56,640
This gap is your baseline and it represents where you actually are.

801
00:32:56,640 –> 00:32:58,000
Not where you think you are.

802
00:32:58,000 –> 00:33:01,040
Now you investigate the 30% that is labeled.

803
00:33:01,040 –> 00:33:04,320
Is it clustered in certain systems or tied to specific teams?

804
00:33:04,320 –> 00:33:09,600
Is it mostly recent data or is it only the information that goes through your formal standard processes?

805
00:33:09,600 –> 00:33:12,720
Ask the same questions about the 70% that’s missing labels.

806
00:33:12,720 –> 00:33:15,360
Where is it hiding and which teams are working with it every day?

807
00:33:15,360 –> 00:33:18,800
What you’re trying to figure out is why the gap exists in the first place.

808
00:33:18,800 –> 00:33:23,120
Is it because the policy is too new or is the labeling process so hard

809
00:33:23,120 –> 00:33:24,560
that people are just skipping it?

810
00:33:24,560 –> 00:33:26,880
Maybe certain systems don’t support labeling at all?

811
00:33:26,880 –> 00:33:29,520
Or perhaps certain teams don’t think the rules apply to them?

812
00:33:29,520 –> 00:33:33,040
Each of those answers requires a completely different solution.

813
00:33:33,040 –> 00:33:35,360
If the gap exists because the policy is new,

814
00:33:35,360 –> 00:33:38,240
it will likely close over time as more data is created,

815
00:33:38,240 –> 00:33:41,120
though you might use an automation project to speed things up.

816
00:33:41,120 –> 00:33:44,000
If the process is just too difficult, you need to simplify things.

817
00:33:44,000 –> 00:33:46,480
Auto labeling can close these gaps quickly

818
00:33:46,480 –> 00:33:50,640
and pre-populated classifications can reduce the number of decisions a human has to make.

819
00:33:50,640 –> 00:33:54,320
If the problem is technical and certain systems don’t support labeling,

820
00:33:54,320 –> 00:33:59,280
you either need to implement a new tool or find an alternative way to govern that specific data.

821
00:33:59,280 –> 00:34:00,800
If a team thinks they’re exempt,

822
00:34:00,800 –> 00:34:03,040
you need to have a real conversation about why.

823
00:34:03,040 –> 00:34:06,320
They might actually be right and the policy isn’t relevant to their use case

824
00:34:06,320 –> 00:34:08,560
or they might just need a better explanation of the risks.

825
00:34:08,560 –> 00:34:12,160
The policy reality alignment assessment isn’t about swinging a hammer.

826
00:34:12,160 –> 00:34:14,160
It’s about understanding why the gap exists

827
00:34:14,160 –> 00:34:17,440
so you can design an intervention that actually fits the problem.

828
00:34:17,440 –> 00:34:19,840
Prioritize these gaps by business impact.

829
00:34:19,840 –> 00:34:23,440
A gap in a production system where live customer data is processed

830
00:34:23,440 –> 00:34:26,720
matters way more than a gap in a test environment or an old archive.

831
00:34:26,720 –> 00:34:31,600
Investigate the specific patterns because a finance team with 0% coverage

832
00:34:31,600 –> 00:34:34,400
is a very different problem than one with 90%.

833
00:34:34,400 –> 00:34:36,400
Zero means they’ve opted out entirely,

834
00:34:36,400 –> 00:34:39,760
while 90% means they’re close and just need a small push to finish.

835
00:34:39,760 –> 00:34:42,080
Concentrate your effort where the impact is highest.

836
00:34:42,080 –> 00:34:43,520
Don’t try to boil the ocean.

837
00:34:43,520 –> 00:34:47,920
Solve the high-risk gaps first and use that success to build momentum for the next phase.

838
00:34:47,920 –> 00:34:50,640
This is where alignment becomes a business practice.

839
00:34:50,640 –> 00:34:52,560
You aren’t just asking if you’re compliant.

840
00:34:52,560 –> 00:34:54,800
You’re asking where your governance is actually working

841
00:34:54,800 –> 00:34:56,640
and what it will take to move the needle.

842
00:34:56,640 –> 00:34:59,040
Organizational behavior pattern analysis.

843
00:34:59,040 –> 00:35:04,160
The third audit uses inside a risk and access data as a signal for organizational stress.

844
00:35:04,160 –> 00:35:08,320
You want to look at when access spikes happen in which teams are showing elevated risk signals.

845
00:35:08,320 –> 00:35:10,240
When you correlate these with business events,

846
00:35:10,240 –> 00:35:12,960
you can start to see the story the data is trying to tell.

847
00:35:12,960 –> 00:35:17,120
A spike in finance access right before the end of the month is perfectly normal.

848
00:35:17,120 –> 00:35:19,760
That’s just a closing process and you expect teams to be running

849
00:35:19,760 –> 00:35:22,240
reconciliations and accessing more data than usual.

850
00:35:22,240 –> 00:35:24,240
A spike during a merger is also normal.

851
00:35:24,240 –> 00:35:29,040
Teams are exploring new systems and trying to understand the entity they’re integrating

852
00:35:29,040 –> 00:35:31,040
so that exploratory access isn’t a risk.

853
00:35:31,040 –> 00:35:32,160
It’s a requirement.

854
00:35:32,160 –> 00:35:36,560
But a spike in the middle of a quiet quarter with no business event to explain it is a real signal.

855
00:35:36,560 –> 00:35:37,440
Something has changed.

856
00:35:37,440 –> 00:35:40,080
Maybe they’re short staffed or there’s an audit you didn’t know about

857
00:35:40,080 –> 00:35:42,560
or perhaps a regulatory deadline is looming.

858
00:35:42,560 –> 00:35:47,200
Elevated access spikes often correlate with organizational stress, not malicious intent.

859
00:35:47,200 –> 00:35:50,480
Take a customer service team showing high insider risk signals.

860
00:35:50,480 –> 00:35:53,760
You might expect them to be flagged because they handle customer data

861
00:35:53,760 –> 00:35:55,280
but look closer at the pattern.

862
00:35:55,280 –> 00:35:57,680
If they’re accessing files outside of normal hours

863
00:35:57,680 –> 00:36:00,640
or looking at accounts they aren’t assigned to, you have to ask why.

864
00:36:00,640 –> 00:36:01,920
Is this a security risk?

865
00:36:01,920 –> 00:36:04,560
It could be, but it’s more likely staffing stress.

866
00:36:04,560 –> 00:36:07,200
They’re short staffed and working nights just to keep up

867
00:36:07,200 –> 00:36:10,320
taking escalations that aren’t theirs because the workload is too high.

868
00:36:10,320 –> 00:36:12,960
The signal is real but how you interpret it changes everything.

869
00:36:12,960 –> 00:36:14,640
If you treat it as a security threat,

870
00:36:14,640 –> 00:36:16,400
you add friction and tighten controls

871
00:36:16,400 –> 00:36:18,080
but if you treat it as staffing stress,

872
00:36:18,080 –> 00:36:20,240
you add headcount or improve the tools

873
00:36:20,240 –> 00:36:22,480
so they don’t have to work in unusual ways.

874
00:36:22,480 –> 00:36:25,040
One interpretation treats the person as a threat

875
00:36:25,040 –> 00:36:28,000
while the other treats the pattern as a symptom of a design problem.

876
00:36:28,000 –> 00:36:31,120
To run this audit, you take your insider risk data

877
00:36:31,120 –> 00:36:33,760
and map it against business events like quarter ends,

878
00:36:33,760 –> 00:36:35,440
budget cycles or product launches.

879
00:36:35,440 –> 00:36:36,960
You ask where the patterns make sense

880
00:36:36,960 –> 00:36:38,320
and where they look like anomalies.

881
00:36:38,320 –> 00:36:41,440
Patterns that align with the business calendar are just noise

882
00:36:41,440 –> 00:36:43,760
but an access spike with no obvious trigger

883
00:36:43,760 –> 00:36:45,360
is a signal you need to follow.

884
00:36:45,360 –> 00:36:47,280
Correlate these risk patterns with other metrics

885
00:36:47,280 –> 00:36:49,280
like turnover or burnout indicators.

886
00:36:49,280 –> 00:36:51,840
You’re building a story about where the system is under pressure

887
00:36:51,840 –> 00:36:55,040
which is a massive shift from traditional security analysis.

888
00:36:55,040 –> 00:36:57,680
Traditional security asks who is doing something bad

889
00:36:57,680 –> 00:37:00,080
but organizational behavior analysis asks

890
00:37:00,080 –> 00:37:02,000
where the system is failing its people.

891
00:37:02,000 –> 00:37:04,880
Once you understand the stress, you can fix the root cause.

892
00:37:04,880 –> 00:37:06,560
You don’t do that by tightening the screws.

893
00:37:06,560 –> 00:37:09,600
You do it by adding capacity or redesigning the work itself.

894
00:37:09,600 –> 00:37:12,560
These three audits, classification, policy alignment

895
00:37:12,560 –> 00:37:15,920
and behavior analysis work together to create a full picture

896
00:37:15,920 –> 00:37:18,080
of how your company actually operates.

897
00:37:18,080 –> 00:37:20,800
This is the diagnostic output you bring to leadership.

898
00:37:20,800 –> 00:37:22,560
Nobody is going to invest in governance

899
00:37:22,560 –> 00:37:24,960
because of a boring compliance dashboard.

900
00:37:24,960 –> 00:37:26,880
They’ll invest because they finally see the waste,

901
00:37:26,880 –> 00:37:29,440
the friction and the stress and they want to fix the business

902
00:37:29,440 –> 00:37:31,440
and that’s when PerView stops being a checkbox

903
00:37:31,440 –> 00:37:33,440
and starts being a business tool.

904
00:37:33,440 –> 00:37:35,760
AI readiness and competitive advantage.

905
00:37:35,760 –> 00:37:38,720
Here is the one thing nobody tells you about getting ready for AI.

906
00:37:38,720 –> 00:37:41,520
The bottleneck isn’t the technology, it’s the data.

907
00:37:41,520 –> 00:37:44,080
And that data constraint is fundamentally a governance problem,

908
00:37:44,080 –> 00:37:45,360
not a technical one.

909
00:37:45,360 –> 00:37:47,360
You can buy all the co-pilot licenses you want

910
00:37:47,360 –> 00:37:48,720
and deploy them tomorrow

911
00:37:48,720 –> 00:37:51,840
but if your data is scattered, unclassified and locked in silos

912
00:37:51,840 –> 00:37:53,040
co-pilot will fail.

913
00:37:53,040 –> 00:37:54,880
It won’t be because the AI is broken

914
00:37:54,880 –> 00:37:57,360
but because the fuel you’re feeding it is low quality.

915
00:37:57,360 –> 00:37:58,480
This is the big reframe.

916
00:37:58,480 –> 00:38:01,520
Governance isn’t an obstacle you have to get past to reach AI.

917
00:38:01,520 –> 00:38:04,240
It’s the foundation that makes AI possible in the first place.

918
00:38:04,240 –> 00:38:07,360
Why data clarity is the foundation of AI?

919
00:38:07,360 –> 00:38:09,120
When someone asks co-pilot a question,

920
00:38:09,120 –> 00:38:10,720
there’s a lot happening under the hood.

921
00:38:10,720 –> 00:38:13,520
If they ask for the customer acquisition cost by region,

922
00:38:13,520 –> 00:38:15,600
co-pilot has to search through spreadsheets,

923
00:38:15,600 –> 00:38:18,880
financial reports and CRM records to synthesize an answer.

924
00:38:18,880 –> 00:38:21,440
But co-pilot is only as good as the data it can find.

925
00:38:21,440 –> 00:38:24,400
Its success depends on whether that data is discoverable,

926
00:38:24,400 –> 00:38:28,800
whether it’s actually trustworthy and whether the AI can even understand what it’s looking at.

927
00:38:28,800 –> 00:38:32,000
If that acquisition data lives in 17 different places

928
00:38:32,000 –> 00:38:34,400
with 17 different definitions of a customer,

929
00:38:34,400 –> 00:38:36,560
co-pilot will find every single one of them.

930
00:38:36,560 –> 00:38:39,040
It will try to mash them together and generate garbage

931
00:38:39,040 –> 00:38:41,440
that sounds confident but is completely wrong.

932
00:38:41,440 –> 00:38:44,400
We call that a hallucination but it’s actually just a data failure.

933
00:38:44,400 –> 00:38:46,800
Without governance, your data is just noise.

934
00:38:46,800 –> 00:38:49,760
And co-pilot treats noise exactly the same way it treats a signal.

935
00:38:49,760 –> 00:38:51,440
It processes it and gives you an output

936
00:38:51,440 –> 00:38:53,600
but that output is only as good as the input.

937
00:38:53,600 –> 00:38:55,600
With governance, your data has structure.

938
00:38:55,600 –> 00:38:58,960
When customer data is labeled and there’s a single version of the truth,

939
00:38:58,960 –> 00:39:02,960
co-pilot can distinguish the authoritative source from the shadow copies.

940
00:39:02,960 –> 00:39:06,720
The difference between AI success and failure isn’t the model you use.

941
00:39:06,720 –> 00:39:08,400
It’s the data foundation you build.

942
00:39:08,400 –> 00:39:10,400
When a company gets poor results from co-pilot,

943
00:39:10,400 –> 00:39:13,600
they usually blame the AI and try to upgrade to a newer model

944
00:39:13,600 –> 00:39:16,160
but they should be looking at their messy data instead.

945
00:39:16,160 –> 00:39:18,480
The problem almost always goes back to governance.

946
00:39:18,480 –> 00:39:20,240
If the data isn’t classified or connected,

947
00:39:20,240 –> 00:39:22,400
there’s no way for the AI to tell what’s important.

948
00:39:22,400 –> 00:39:24,320
Here’s what clarity actually gives you.

949
00:39:24,320 –> 00:39:26,880
When your data is governed and ownership is clear,

950
00:39:26,880 –> 00:39:28,560
co-pilot understands the context.

951
00:39:28,560 –> 00:39:31,680
It respects the boundaries of sensitive data and understands the lineage

952
00:39:31,680 –> 00:39:33,840
so it can actually explain where its answers came from.

953
00:39:33,840 –> 00:39:36,320
Even better, you can create AI safe zones.

954
00:39:36,320 –> 00:39:39,280
These are collections of data that are properly secured

955
00:39:39,280 –> 00:39:42,240
and documented allowing you to scope co-pilot’s access

956
00:39:42,240 –> 00:39:43,920
so it only learns from the best sources.

957
00:39:43,920 –> 00:39:46,320
You might start with finance data because it’s clean and ready.

958
00:39:46,320 –> 00:39:48,400
Once those teams see reliable insights,

959
00:39:48,400 –> 00:39:51,600
you move to sales and repeat the process of classifying and labeling.

960
00:39:51,600 –> 00:39:54,720
You do this incrementally building readiness one domain at a time.

961
00:39:54,720 –> 00:39:57,440
Co-pilot becomes more valuable not because the model got smarter

962
00:39:57,440 –> 00:39:58,880
but because your data got better.

963
00:39:58,880 –> 00:40:01,760
The governance is the fuel quality control for your AI engine.

964
00:40:01,760 –> 00:40:03,200
Without it, the engine might run,

965
00:40:03,200 –> 00:40:05,200
but it’s only going to produce smoke.

966
00:40:05,200 –> 00:40:08,640
The 30, 60, 90-day road map to AI readiness.

967
00:40:08,640 –> 00:40:12,000
Here is how you actually get ready for co-pilot in the real world.

968
00:40:12,000 –> 00:40:13,600
Days one through 30.

969
00:40:13,600 –> 00:40:15,360
Visibility and baseline.

970
00:40:15,360 –> 00:40:17,200
Your first goal is to see the landscape

971
00:40:17,200 –> 00:40:18,640
and understand what you’re working with.

972
00:40:18,640 –> 00:40:20,880
You need to establish your baseline metrics immediately.

973
00:40:20,880 –> 00:40:23,120
Run discovery across your most critical sources

974
00:40:23,120 –> 00:40:26,080
like where your intellectual property and financial records live.

975
00:40:26,080 –> 00:40:27,920
Identify the three to five data types

976
00:40:27,920 –> 00:40:29,680
that will actually power your AI

977
00:40:29,680 –> 00:40:33,120
and use purview to find the gaps in classification or ownership.

978
00:40:33,120 –> 00:40:34,960
You’re building a picture of your current state

979
00:40:34,960 –> 00:40:38,320
so you can document exactly how much of your data is actually protected.

980
00:40:38,320 –> 00:40:40,080
When you communicate this to stakeholders,

981
00:40:40,080 –> 00:40:41,760
you aren’t talking in theories anymore.

982
00:40:41,760 –> 00:40:43,760
You can show them exactly what needs to improve

983
00:40:43,760 –> 00:40:46,640
before you can safely deploy co-pilot at scale.

984
00:40:46,640 –> 00:40:48,320
Days 31 through 60.

985
00:40:48,320 –> 00:40:49,760
Taxonomy and audit.

986
00:40:49,760 –> 00:40:50,960
Now it’s time to move.

987
00:40:50,960 –> 00:40:52,240
You’re going to apply governance

988
00:40:52,240 –> 00:40:54,560
to those critical data types you identified.

989
00:40:54,560 –> 00:40:56,160
Use auto labeling wherever you can

990
00:40:56,160 –> 00:40:58,640
and use manual reviews to validate the results.

991
00:40:58,640 –> 00:41:00,320
The goal here isn’t perfection.

992
00:41:00,320 –> 00:41:01,520
It’s consistency.

993
00:41:01,520 –> 00:41:04,160
You want a coherent framework rather than a scattered mess.

994
00:41:04,160 –> 00:41:05,840
Turn on DLP in audit mode

995
00:41:05,840 –> 00:41:08,640
so you can observe patterns without blocking anyone’s work yet.

996
00:41:08,640 –> 00:41:10,720
This lets you see where policies would trigger

997
00:41:10,720 –> 00:41:12,480
and helps you understand friction points

998
00:41:12,480 –> 00:41:13,760
before you start enforcement.

999
00:41:13,760 –> 00:41:16,720
This is also when you run the three audits we talked about.

1000
00:41:16,720 –> 00:41:20,400
Classification clarity, policy alignment and behavior patterns.

1001
00:41:20,400 –> 00:41:22,720
You use these to create a narrative for leadership.

1002
00:41:22,720 –> 00:41:24,480
You show them where the processes are broken

1003
00:41:24,480 –> 00:41:27,120
and what needs to be fixed to make the organization AI ready.

1004
00:41:27,120 –> 00:41:29,120
This story becomes your justification

1005
00:41:29,120 –> 00:41:30,960
for the entire investment.

1006
00:41:30,960 –> 00:41:32,320
Days 61 through 90.

1007
00:41:32,320 –> 00:41:33,680
Readiness and enablement.

1008
00:41:33,680 –> 00:41:35,520
You’ve seen the landscape and run the audits

1009
00:41:35,520 –> 00:41:37,360
so now you build the final foundation.

1010
00:41:37,360 –> 00:41:40,240
Refine your policies based on what you learned in audit mode.

1011
00:41:40,240 –> 00:41:42,960
Your DLP rules should only block actual risks

1012
00:41:42,960 –> 00:41:45,360
and your labeling should reflect real priorities

1013
00:41:45,360 –> 00:41:46,640
rather than just anxiety.

1014
00:41:46,640 –> 00:41:48,800
Create those AI ready data zones

1015
00:41:48,800 –> 00:41:52,960
for specific departments like finance or customer service.

1016
00:41:52,960 –> 00:41:54,480
You aren’t locking people out.

1017
00:41:54,480 –> 00:41:55,760
You’re just limiting the scope

1018
00:41:55,760 –> 00:41:57,840
while you build up your confidence in the system.

1019
00:41:57,840 –> 00:42:00,880
Make that data easy to find by putting it in the unified catalog

1020
00:42:00,880 –> 00:42:02,480
and setting up clear access workflows.

1021
00:42:02,480 –> 00:42:04,240
By day 90, you’re AI ready.

1022
00:42:04,240 –> 00:42:06,000
It’s not because you checked every single box

1023
00:42:06,000 –> 00:42:07,600
but because you’ve built a foundation

1024
00:42:07,600 –> 00:42:10,000
that can actually support AI safely.

1025
00:42:10,000 –> 00:42:12,000
Cleaning up data entropy.

1026
00:42:12,000 –> 00:42:14,960
There’s a concept that becomes vital when you prepare for AI

1027
00:42:14,960 –> 00:42:16,240
and that’s data entropy.

1028
00:42:16,240 –> 00:42:17,760
Entropy is the noise in your system

1029
00:42:17,760 –> 00:42:20,480
like duplicate records and conflicting versions of the truth.

1030
00:42:20,480 –> 00:42:23,440
It’s the natural decay that happens when the system is running

1031
00:42:23,440 –> 00:42:25,680
but nobody is actively maintaining it.

1032
00:42:25,680 –> 00:42:28,320
When humans make decisions, entropy doesn’t matter as much

1033
00:42:28,320 –> 00:42:30,480
because a person can look at two records

1034
00:42:30,480 –> 00:42:31,760
and figure out which one is right.

1035
00:42:31,760 –> 00:42:33,760
But for an AI entropy is a disaster.

1036
00:42:33,760 –> 00:42:35,360
It treats every record as equal

1037
00:42:35,360 –> 00:42:37,200
and can’t tell the difference between a current file

1038
00:42:37,200 –> 00:42:38,160
and an old duplicate.

1039
00:42:38,160 –> 00:42:40,640
So it processes the noise as if it were a signal.

1040
00:42:40,640 –> 00:42:42,240
If you’ve been running for 10 years

1041
00:42:42,240 –> 00:42:44,400
without a cleanup, you have a lot of entropy.

1042
00:42:44,400 –> 00:42:47,600
Retention policies are your best tool for managing this.

1043
00:42:47,600 –> 00:42:50,560
They aren’t just for compliance, therefore data quality.

1044
00:42:50,560 –> 00:42:52,400
Stale data that hasn’t been touched in a year

1045
00:42:52,400 –> 00:42:53,760
is a lie that looks like the truth

1046
00:42:53,760 –> 00:42:55,360
and it will lead to bad decisions.

1047
00:42:55,360 –> 00:42:57,040
Identify that stale data

1048
00:42:57,040 –> 00:42:58,560
and get it out of your active systems

1049
00:42:58,560 –> 00:43:01,280
whether you delete it or archive it, it needs to be gone.

1050
00:43:01,280 –> 00:43:02,880
Data lineage is also critical here.

1051
00:43:02,880 –> 00:43:04,320
You need to know where data came from

1052
00:43:04,320 –> 00:43:05,440
and if it’s still valid.

1053
00:43:05,440 –> 00:43:06,800
If you can’t answer those questions,

1054
00:43:06,800 –> 00:43:08,800
you can’t trust the AI’s output.

1055
00:43:08,800 –> 00:43:10,880
Cleaning up this mess isn’t a one-time project.

1056
00:43:10,880 –> 00:43:12,000
It’s a continuous practice

1057
00:43:12,000 –> 00:43:14,080
that you have to maintain every time you integrate

1058
00:43:14,080 –> 00:43:15,040
a new source.

1059
00:43:15,040 –> 00:43:17,040
And this is where per views quality tools come in.

1060
00:43:17,040 –> 00:43:19,120
You can set up automated rules to flag

1061
00:43:19,120 –> 00:43:21,520
incomplete records or give data sets health scores

1062
00:43:21,520 –> 00:43:24,000
so you know which ones are actually trustworthy.

1063
00:43:24,000 –> 00:43:25,840
Aligning permissions with reality.

1064
00:43:25,840 –> 00:43:27,760
There’s a very specific pattern that emerges

1065
00:43:27,760 –> 00:43:29,360
when you look at access data.

1066
00:43:29,360 –> 00:43:32,160
Users accumulate permissions over their entire careers.

1067
00:43:32,160 –> 00:43:34,720
They move to new roles but keep their old access

1068
00:43:34,720 –> 00:43:36,720
or contractors stay longer than expected

1069
00:43:36,720 –> 00:43:38,240
and keep keys they should have returned.

1070
00:43:38,240 –> 00:43:39,760
We call this permission creep

1071
00:43:39,760 –> 00:43:42,480
and it’s usually invisible until you turn on co-pilot.

1072
00:43:42,480 –> 00:43:44,400
Co-pilot respects your existing permissions

1073
00:43:44,400 –> 00:43:46,880
so if you have access to something the AI can see it.

1074
00:43:46,880 –> 00:43:48,640
If you’ve inherited access to systems

1075
00:43:48,640 –> 00:43:50,240
you don’t even use any more co-pilot

1076
00:43:50,240 –> 00:43:51,680
is going to surface that data.

1077
00:43:51,680 –> 00:43:54,400
This isn’t a security failure, it’s a visibility failure.

1078
00:43:54,400 –> 00:43:55,440
You just weren’t looking.

1079
00:43:55,440 –> 00:43:57,520
Aligning permissions isn’t about being restrictive.

1080
00:43:57,520 –> 00:43:59,040
It’s about being clear.

1081
00:43:59,040 –> 00:44:01,600
People should have exactly what they need to do their jobs.

1082
00:44:01,600 –> 00:44:03,760
Nothing more and nothing less.

1083
00:44:03,760 –> 00:44:06,400
Use per views access reviews to ask the hard questions.

1084
00:44:06,400 –> 00:44:08,560
Does this person still need this level of access?

1085
00:44:08,560 –> 00:44:10,720
And is it proportional to their current role?

1086
00:44:10,720 –> 00:44:14,320
The data will tell the story through access patterns and role changes.

1087
00:44:14,320 –> 00:44:15,840
You’ll see exactly where the permissions

1088
00:44:15,840 –> 00:44:17,520
have drifted away from reality.

1089
00:44:17,520 –> 00:44:18,960
Fix the alignment iteratively.

1090
00:44:18,960 –> 00:44:20,720
You don’t have to revoke everything at once

1091
00:44:20,720 –> 00:44:22,640
but you do need to remove what isn’t needed

1092
00:44:22,640 –> 00:44:23,840
and document what’s left.

1093
00:44:23,840 –> 00:44:27,120
This clean foundation is what co-pilot operates against.

1094
00:44:27,120 –> 00:44:28,880
The decision accelerator effect.

1095
00:44:28,880 –> 00:44:32,080
When you do this work correctly, something interesting happens.

1096
00:44:32,080 –> 00:44:35,440
Organizations with governed data actually move faster than those without it.

1097
00:44:35,440 –> 00:44:37,760
It’s not because governance adds speed

1098
00:44:37,760 –> 00:44:41,040
but because it removes the friction that slows everyone down.

1099
00:44:41,040 –> 00:44:43,520
When you have a single source of truth and clear ownership,

1100
00:44:43,520 –> 00:44:45,600
decision making becomes instant.

1101
00:44:45,600 –> 00:44:47,600
You don’t have to waste time validating data

1102
00:44:47,600 –> 00:44:50,000
or arguing about which spreadsheet is the real one.

1103
00:44:50,000 –> 00:44:52,320
Finance can close the books in two days instead of two weeks

1104
00:44:52,320 –> 00:44:54,400
because the data flows automatically.

1105
00:44:54,400 –> 00:44:57,680
Sales can forecast accurately because their pipeline is clean

1106
00:44:57,680 –> 00:45:00,240
and operations can respond to issues in real time.

1107
00:45:00,240 –> 00:45:02,800
This is where governance becomes a true competitive advantage.

1108
00:45:02,800 –> 00:45:05,040
Companies with governed data deploy AI faster

1109
00:45:05,040 –> 00:45:06,640
because their foundation is solid.

1110
00:45:06,640 –> 00:45:09,200
They get reliable results because their inputs are trustworthy

1111
00:45:09,200 –> 00:45:13,120
and they can scale without being afraid of what co-pilot might accidentally surface.

1112
00:45:13,120 –> 00:45:14,800
The advantage always goes to the organization

1113
00:45:14,800 –> 00:45:16,880
with the clearest view of its own operations.

1114
00:45:16,880 –> 00:45:18,320
That clarity comes from purview

1115
00:45:18,320 –> 00:45:21,200
and from running these audits to see the business as it actually is.

1116
00:45:21,200 –> 00:45:23,680
You don’t build governance because an auditor told you to.

1117
00:45:23,680 –> 00:45:26,720
You build it because it’s the only way to move fast in 2026.

1118
00:45:26,720 –> 00:45:28,160
Purview isn’t a compliance tool.

1119
00:45:28,160 –> 00:45:30,480
It’s the lens you use to see your company clearly

1120
00:45:30,480 –> 00:45:33,280
and in a world where speed and AI determine the winners.

1121
00:45:33,280 –> 00:45:35,280
That clarity is everything.

1122
00:45:35,280 –> 00:45:37,040
Strategic synthesis and action.

1123
00:45:37,040 –> 00:45:40,000
Purview as the bridge between IT and the boardroom.

1124
00:45:40,000 –> 00:45:43,040
In most organizations, the conversation around Microsoft Purview

1125
00:45:43,040 –> 00:45:45,840
follows a very predictable, very broken script.

1126
00:45:45,840 –> 00:45:47,760
It usually starts by saying they need Purview

1127
00:45:47,760 –> 00:45:49,280
because it’s a compliance requirement

1128
00:45:49,280 –> 00:45:52,160
which immediately prompts the CFO to ask about the total cost.

1129
00:45:52,160 –> 00:45:55,600
When IT lists off licensing, implementation and management fees,

1130
00:45:55,600 –> 00:45:58,800
the CFO naturally asks what the actual business value is.

1131
00:45:58,800 –> 00:46:00,800
This is where IT usually hits a wall

1132
00:46:00,800 –> 00:46:03,680
because they frame the entire project as a cost center,

1133
00:46:03,680 –> 00:46:06,080
a checkbox or just another boring compliance tool.

1134
00:46:06,080 –> 00:46:09,040
That conversation almost always ends with a tiny budget,

1135
00:46:09,040 –> 00:46:12,240
a grudging approval and an investment that is destined to fail.

1136
00:46:12,240 –> 00:46:13,840
We need to change the script to reflect

1137
00:46:13,840 –> 00:46:16,240
how technology actually shapes business reality.

1138
00:46:16,240 –> 00:46:18,640
The right conversation starts with business strategy,

1139
00:46:18,640 –> 00:46:21,840
announcing a plan to deploy AI and build autonomous workflows

1140
00:46:21,840 –> 00:46:24,080
that require moving fast without breaking things.

1141
00:46:24,080 –> 00:46:27,680
I can then explain that the current data foundation is scattered and unclassified

1142
00:46:27,680 –> 00:46:31,680
meaning they can’t safely deploy AI at scale without better visibility.

1143
00:46:31,680 –> 00:46:34,640
When the business asks what is needed to get that visibility,

1144
00:46:34,640 –> 00:46:37,040
the answer is Purview, not for the sake of compliance

1145
00:46:37,040 –> 00:46:41,280
but because it’s the only way to see if the organization is actually ready for AI.

1146
00:46:41,280 –> 00:46:43,120
This shift in framing changes everything

1147
00:46:43,120 –> 00:46:46,080
because business leaders don’t actually care about the tool itself.

1148
00:46:46,080 –> 00:46:49,280
They care about revenue, risk and competitive advantage.

1149
00:46:49,280 –> 00:46:51,280
Purview only matters to the boardroom

1150
00:46:51,280 –> 00:46:54,320
if it translates into the outcomes they are already chasing

1151
00:46:54,320 –> 00:46:58,320
like whether they can move fast without exposing sensitive trade secrets.

1152
00:46:58,320 –> 00:46:59,680
It has to build a bridge from

1153
00:46:59,680 –> 00:47:02,640
we need this for the auditors to this tool shows us

1154
00:47:02,640 –> 00:47:04,640
if we can actually execute our strategy.

1155
00:47:04,640 –> 00:47:07,280
This bridge is built on the audits you’ve already done.

1156
00:47:07,280 –> 00:47:10,160
The quantified ways you found and the silos you’ve identified.

1157
00:47:10,160 –> 00:47:11,520
When you bring this data to the board,

1158
00:47:11,520 –> 00:47:14,080
you aren’t asking for a Purview budget anymore.

1159
00:47:14,080 –> 00:47:17,280
You are asking for an AI readiness and data governance budget.

1160
00:47:17,280 –> 00:47:20,640
You are asking for the resources to fix the structural problems

1161
00:47:20,640 –> 00:47:23,200
that are currently blocking your company’s most important goals.

1162
00:47:23,200 –> 00:47:24,880
The data makes your request concrete

1163
00:47:24,880 –> 00:47:26,720
so instead of saying governance is important,

1164
00:47:26,720 –> 00:47:31,520
you can point to the $72,000 lost every year to manual reconciliation.

1165
00:47:31,520 –> 00:47:34,080
You can tell them that co-pilot cannot be safely deployed

1166
00:47:34,080 –> 00:47:36,160
until you know where the sensitive data lives

1167
00:47:36,160 –> 00:47:39,760
and that discovery process requires a specific timeline and cost.

1168
00:47:39,760 –> 00:47:44,560
This is the ultimate reframe where Purview becomes the diagnostic tool

1169
00:47:44,560 –> 00:47:48,160
that justifies the organizational transformation you need to win it.

1170
00:47:48,160 –> 00:47:49,680
The executive narrative.

1171
00:47:49,680 –> 00:47:52,880
The old way of talking about data is defensive and reactive

1172
00:47:52,880 –> 00:47:55,120
which positions governance as a heavy overhead

1173
00:47:55,120 –> 00:47:56,320
that everyone wants to avoid.

1174
00:47:56,320 –> 00:47:57,120
You’ve heard it before.

1175
00:47:57,120 –> 00:47:59,600
We need this because the regulators are getting stricter

1176
00:47:59,600 –> 00:48:01,760
and we need to avoid massive fines.

1177
00:48:01,760 –> 00:48:03,680
While that might be true, it’s a weak narrative

1178
00:48:03,680 –> 00:48:05,760
that only justifies the bare minimum investment

1179
00:48:05,760 –> 00:48:07,840
and never captures the attention of the CEO.

1180
00:48:07,840 –> 00:48:10,560
It treats the system as a burden rather than an asset.

1181
00:48:10,560 –> 00:48:13,360
The new narrative is strategic because it positions governance

1182
00:48:13,360 –> 00:48:15,840
as a high performance enabler for the entire company.

1183
00:48:15,840 –> 00:48:17,040
Instead of talking about rules,

1184
00:48:17,040 –> 00:48:18,720
you tell the board you deployed Purview

1185
00:48:18,720 –> 00:48:20,960
to understand how the business actually operates

1186
00:48:20,960 –> 00:48:22,320
and then you show them the results.

1187
00:48:22,320 –> 00:48:24,320
You might show them that two weeks of every month

1188
00:48:24,320 –> 00:48:26,080
are wasted on data reconciliation

1189
00:48:26,080 –> 00:48:28,960
because three different systems aren’t talking to each other.

1190
00:48:28,960 –> 00:48:32,080
By quantifying that cost at $72,000 a year,

1191
00:48:32,080 –> 00:48:34,800
you turn a technical problem into a clear savings opportunity

1192
00:48:34,800 –> 00:48:36,640
that any executive will want to fund.

1193
00:48:36,640 –> 00:48:40,240
You can point out that the sales team is using a shadow system in Excel

1194
00:48:40,240 –> 00:48:41,680
because the CRM is too slow,

1195
00:48:41,680 –> 00:48:44,080
which is why your deal forecasting is so inaccurate.

1196
00:48:44,080 –> 00:48:47,520
You can show that while finance and HR have mature data systems,

1197
00:48:47,520 –> 00:48:50,080
the operations and customer service teams are fragmented

1198
00:48:50,080 –> 00:48:51,760
and need a better template to follow.

1199
00:48:51,760 –> 00:48:54,000
This narrative is about efficiency and readiness

1200
00:48:54,000 –> 00:48:56,560
and it uses Purview to reveal the hidden friction

1201
00:48:56,560 –> 00:48:57,840
that is slowing everyone down.

1202
00:48:57,840 –> 00:49:00,800
If you find that permission creep has given people access to files

1203
00:49:00,800 –> 00:49:01,680
they shouldn’t see,

1204
00:49:01,680 –> 00:49:04,000
you can explain that copilot will expose that data

1205
00:49:04,000 –> 00:49:04,880
the moment it’s turned on.

1206
00:49:04,880 –> 00:49:08,320
You can even show where the organization is under the most stress

1207
00:49:08,320 –> 00:49:10,080
by correlating high-risk signals

1208
00:49:10,080 –> 00:49:13,120
with understaffed departments and broken processes.

1209
00:49:13,120 –> 00:49:15,760
Every one of these statements is grounded in hard data,

1210
00:49:15,760 –> 00:49:17,360
showing exactly what Purview revealed

1211
00:49:17,360 –> 00:49:19,360
that you didn’t know just a few months ago.

1212
00:49:19,360 –> 00:49:21,520
This is the narrative that gets real investment

1213
00:49:21,520 –> 00:49:23,760
because it solves actual business problems

1214
00:49:23,760 –> 00:49:26,400
rather than just satisfying a legal requirement.

1215
00:49:26,400 –> 00:49:28,000
The 90-day pilot plan,

1216
00:49:28,000 –> 00:49:30,160
you don’t execute a shift like this in theory,

1217
00:49:30,160 –> 00:49:32,960
you do it in practice over a 90-day sprint.

1218
00:49:32,960 –> 00:49:36,400
Phase one, audit and discover, days one to 30.

1219
00:49:36,400 –> 00:49:39,040
Start by identifying the three most critical data types

1220
00:49:39,040 –> 00:49:40,560
for your specific business,

1221
00:49:40,560 –> 00:49:43,040
whether that is customer data, financial records

1222
00:49:43,040 –> 00:49:44,640
or your intellectual property.

1223
00:49:44,640 –> 00:49:47,760
You need to run discovery scans across Microsoft 365 Azure

1224
00:49:47,760 –> 00:49:48,960
and your on-premises systems

1225
00:49:48,960 –> 00:49:50,560
to see where that data actually lives

1226
00:49:50,560 –> 00:49:51,600
and who has access to it.

1227
00:49:51,600 –> 00:49:52,640
This gives you a baseline

1228
00:49:52,640 –> 00:49:55,600
so you can see the gap between your official policies

1229
00:49:55,600 –> 00:49:58,000
and the messy reality of how people are working.

1230
00:49:58,000 –> 00:49:58,720
Once you have that,

1231
00:49:58,720 –> 00:50:00,720
you create a data reality check for leadership

1232
00:50:00,720 –> 00:50:02,400
that outlines exactly what needs to happen

1233
00:50:02,400 –> 00:50:04,960
before co-pilot can be safely switched on.

1234
00:50:04,960 –> 00:50:08,240
Phase two, automate and observe, days 31 to 60.

1235
00:50:08,240 –> 00:50:09,360
Once you have the baseline,

1236
00:50:09,360 –> 00:50:12,240
start applying auto labeling to those critical data types

1237
00:50:12,240 –> 00:50:15,120
and use manual reviews to make sure the system is working.

1238
00:50:15,120 –> 00:50:17,040
You aren’t looking for perfection here.

1239
00:50:17,040 –> 00:50:19,600
You are looking for consistency across the system.

1240
00:50:20,560 –> 00:50:22,800
Turn on data loss prevention in audit mode

1241
00:50:22,800 –> 00:50:24,720
so you can observe patterns and document

1242
00:50:24,720 –> 00:50:26,160
where violations would happen

1243
00:50:26,160 –> 00:50:28,640
without actually blocking anyone’s work yet.

1244
00:50:28,640 –> 00:50:30,400
This allows you to translate your findings

1245
00:50:30,400 –> 00:50:32,960
into business language, identifying where the friction is

1246
00:50:32,960 –> 00:50:34,640
and highlighting the stress points

1247
00:50:34,640 –> 00:50:35,920
in your current workflows.

1248
00:50:35,920 –> 00:50:40,560
Phase three, build control plane, days 61 to 90.

1249
00:50:40,560 –> 00:50:42,640
In the final month, you refine your policies

1250
00:50:42,640 –> 00:50:45,200
based on what you actually learned during the audit phase

1251
00:50:45,200 –> 00:50:48,160
so they reflect reality instead of just good intentions.

1252
00:50:48,160 –> 00:50:50,720
You create AI ready data zones by securing

1253
00:50:50,720 –> 00:50:53,440
and documenting the most important collections of data,

1254
00:50:53,440 –> 00:50:55,760
starting small with finance or product teams.

1255
00:50:55,760 –> 00:50:58,560
By establishing easy access, request workflows,

1256
00:50:58,560 –> 00:51:00,640
and clear ownership, you make it simple for people

1257
00:51:00,640 –> 00:51:03,040
to find what they need while maintaining total security.

1258
00:51:03,040 –> 00:51:05,120
By day 90, you are ready to move forward

1259
00:51:05,120 –> 00:51:06,480
because you’ve built a foundation

1260
00:51:06,480 –> 00:51:08,880
that can support AI safely and sustainably.

1261
00:51:08,880 –> 00:51:10,160
This isn’t a one-time project,

1262
00:51:10,160 –> 00:51:12,720
but a permanent shift toward ongoing monitoring

1263
00:51:12,720 –> 00:51:15,680
and refinement that keeps your data foundation solid as you scale.

1264
00:51:15,680 –> 00:51:18,560
Connecting data clarity to competitive advantage.

1265
00:51:18,560 –> 00:51:21,040
The organizations that can see themselves clearly

1266
00:51:21,040 –> 00:51:23,760
have a massive advantage over the ones operating in the dark.

1267
00:51:23,760 –> 00:51:25,680
When you understand your real constraints,

1268
00:51:25,680 –> 00:51:27,520
the silos that slow down decisions

1269
00:51:27,520 –> 00:51:29,840
and the manual processes holding things together,

1270
00:51:29,840 –> 00:51:32,560
you can make choices based on reality instead of assumptions.

1271
00:51:32,560 –> 00:51:34,960
These companies deploy AI faster

1272
00:51:34,960 –> 00:51:37,200
because their data foundation is trustworthy

1273
00:51:37,200 –> 00:51:38,960
and they reduce their overall risk

1274
00:51:38,960 –> 00:51:41,120
because they know exactly what they are protecting.

1275
00:51:41,120 –> 00:51:42,560
They don’t just move faster,

1276
00:51:42,560 –> 00:51:44,000
they move with a level of confidence

1277
00:51:44,000 –> 00:51:46,000
that their competitors simply cannot match.

1278
00:51:46,000 –> 00:51:47,760
Think about two different companies.

1279
00:51:47,760 –> 00:51:50,320
Organization A operates on a gut feeling

1280
00:51:50,320 –> 00:51:51,840
that their data is fine

1281
00:51:51,840 –> 00:51:55,120
while organization B operates on observed reality.

1282
00:51:55,120 –> 00:51:57,600
Organization A thinks their processes are working

1283
00:51:57,600 –> 00:51:58,720
and their teams are aligned,

1284
00:51:58,720 –> 00:51:59,920
but they are usually wrong

1285
00:51:59,920 –> 00:52:02,640
and that overconfidence eventually leads to a crisis.

1286
00:52:02,640 –> 00:52:04,960
When organization A tries to roll out co-pilot,

1287
00:52:04,960 –> 00:52:07,040
they suddenly realize their permissions are a mess

1288
00:52:07,040 –> 00:52:09,120
and their data is scattered everywhere,

1289
00:52:09,120 –> 00:52:12,400
forcing them to stop everything for six months to fix it.

1290
00:52:12,400 –> 00:52:14,480
Organization B has already done that work

1291
00:52:14,480 –> 00:52:16,160
so they move methodically

1292
00:52:16,160 –> 00:52:19,200
hitting their targets months ahead of everyone else.

1293
00:52:19,200 –> 00:52:20,800
The same thing happens with automation.

1294
00:52:20,800 –> 00:52:23,200
Organization A tries to automate a workflow

1295
00:52:23,200 –> 00:52:25,440
only to find out it’s a non-linear mess

1296
00:52:25,440 –> 00:52:28,080
that depends on one person’s undocumented knowledge

1297
00:52:28,080 –> 00:52:31,280
because organization B has already mapped their actual workflows

1298
00:52:31,280 –> 00:52:33,120
and documented the dependencies

1299
00:52:33,120 –> 00:52:34,800
their automation works the first time.

1300
00:52:34,800 –> 00:52:37,360
This isn’t about governance for the sake of having rules.

1301
00:52:37,360 –> 00:52:39,680
It’s about building a system that allows you to win.

1302
00:52:39,680 –> 00:52:42,480
The winners in 2020 won’t be the ones with the most data

1303
00:52:42,480 –> 00:52:45,040
but the ones who actually understand the data they have

1304
00:52:45,040 –> 00:52:46,800
and where the risks are hiding.

1305
00:52:46,800 –> 00:52:48,800
Per view is the tool that makes this vision possible

1306
00:52:48,800 –> 00:52:51,120
by letting you see the actual organization operating

1307
00:52:51,120 –> 00:52:52,640
underneath the official org chart.

1308
00:52:52,640 –> 00:52:55,440
In an era where speed and AI determine the winners,

1309
00:52:55,440 –> 00:52:58,000
seeing your organization clearly isn’t just a nice feature.

1310
00:52:58,000 –> 00:52:59,040
It’s a matter of survival.

1311
00:52:59,040 –> 00:53:01,520
The strategic imperative.

1312
00:53:01,520 –> 00:53:03,120
By now you should understand that per view

1313
00:53:03,120 –> 00:53:04,800
is not just a compliance solution.

1314
00:53:04,800 –> 00:53:06,400
You can treat it like a checkbox if you want

1315
00:53:06,400 –> 00:53:07,280
but if you do,

1316
00:53:07,280 –> 00:53:10,000
you are leaving almost all of the actual value on the table.

1317
00:53:10,000 –> 00:53:11,760
Per view is a diagnostic platform

1318
00:53:11,760 –> 00:53:14,080
that shows you how your business really operates

1319
00:53:14,080 –> 00:53:16,000
revealing the gap between your assumptions

1320
00:53:16,000 –> 00:53:18,160
and the reality where risk and opportunity live.

1321
00:53:18,160 –> 00:53:20,400
The organizations that win over the next few years

1322
00:53:20,400 –> 00:53:22,160
will be the ones that close that gap

1323
00:53:22,160 –> 00:53:24,720
by identifying silos and quantifying waste.

1324
00:53:24,720 –> 00:53:26,720
They use these tools not because they have to

1325
00:53:26,720 –> 00:53:28,800
but because they want the clarity required

1326
00:53:28,800 –> 00:53:29,920
to lead their industry.

1327
00:53:29,920 –> 00:53:33,120
The 30 to 60 90 road map I’ve shared is your starting point

1328
00:53:33,120 –> 00:53:36,080
and the data reality check is your primary diagnostic tool.

1329
00:53:36,080 –> 00:53:37,920
Run the audits, build the narrative

1330
00:53:37,920 –> 00:53:39,520
and then you can deploy co-pilot

1331
00:53:39,520 –> 00:53:42,480
with the kind of confidence that only comes from a solid foundation.

1332
00:53:42,480 –> 00:53:44,960
If you want to discuss your specific governance challenges

1333
00:53:44,960 –> 00:53:46,880
connect with me, Mirko Peters, on LinkedIn.

1334
00:53:46,880 –> 00:53:49,520
You can also subscribe to the M365FM podcast

1335
00:53:49,520 –> 00:53:53,200
for more deep dives into the intersection of strategy, data and AI.

1336
00:53:53,200 –> 00:53:54,800
If you found this perspective helpful,

1337
00:53:54,800 –> 00:53:57,680
please leave a review on Apple podcasts or Spotify

1338
00:53:57,680 –> 00:53:58,960
so others can find it too.

1339
00:53:58,960 –> 00:54:01,920
Your next big competitive advantage is seeing your organization

1340
00:54:01,920 –> 00:54:03,280
for what it actually is

1341
00:54:03,280 –> 00:54:04,720
and everything else you want to achieve

1342
00:54:04,720 –> 00:54:06,000
follows from that one truth.

1343
00:54:06,000 –> 00:54:08,960
My name is Mirko Peters

1344
00:54:08,960 –> 00:54:12,480
and I translate how technology actually shapes business reality.

1345
00:54:12,480 –> 00:54:14,880
By now you understand the mechanics of the system

1346
00:54:14,880 –> 00:54:17,360
but we need to talk about what actually changes because of it.

1347
00:54:17,360 –> 00:54:20,800
You can treat PerView as a simple compliance solution if you want to.

1348
00:54:20,800 –> 00:54:22,800
It’s easy to use it that way to tick a box,

1349
00:54:22,800 –> 00:54:24,720
run a few reports and show your auditors

1350
00:54:24,720 –> 00:54:27,520
that you have some controls in place before moving on with your day

1351
00:54:27,520 –> 00:54:30,480
but if that’s all you do you are leaving the real value on the table.

1352
00:54:30,480 –> 00:54:32,560
PerView is actually a diagnostic platform.

1353
00:54:32,560 –> 00:54:37,040
It functions as the operating system for understanding how your business really operates in the wild.

1354
00:54:37,040 –> 00:54:38,800
This isn’t about how you think things work

1355
00:54:38,800 –> 00:54:40,720
or what the official org chart says.

1356
00:54:40,720 –> 00:54:42,800
It’s about seeing the truth in real time

1357
00:54:42,800 –> 00:54:45,600
backed by hard data and moving away from guesswork.

1358
00:54:45,600 –> 00:54:47,680
The gap between your assumptions and reality,

1359
00:54:47,680 –> 00:54:49,760
the space between the theoretical organization

1360
00:54:49,760 –> 00:54:52,240
and the actual one is exactly where risk lives.

1361
00:54:52,240 –> 00:54:55,040
That same gap is also where your best opportunities hide.

1362
00:54:55,040 –> 00:54:56,080
If we’re being honest,

1363
00:54:56,080 –> 00:54:59,520
most organizations are currently wasting significant money on processes

1364
00:54:59,520 –> 00:55:01,040
that shouldn’t exist and manual work

1365
00:55:01,040 –> 00:55:03,040
that should have been automated years ago.

1366
00:55:03,040 –> 00:55:05,520
Most leadership teams have never actually measured this gap

1367
00:55:05,520 –> 00:55:07,600
because they’ve never looked at the data

1368
00:55:07,600 –> 00:55:08,800
and asked the hard questions.

1369
00:55:08,800 –> 00:55:10,320
They don’t know what they don’t know

1370
00:55:10,320 –> 00:55:12,960
so they continue to operate in a state of confident ignorance.

1371
00:55:12,960 –> 00:55:14,800
This brings us back to the illusion of control

1372
00:55:14,800 –> 00:55:17,040
we discussed at the beginning of this conversation.

1373
00:55:17,040 –> 00:55:18,880
You have your policies, your processes,

1374
00:55:18,880 –> 00:55:20,160
and your training frameworks.

1375
00:55:20,160 –> 00:55:23,200
So you naturally assume the system is working as intended.

1376
00:55:23,200 –> 00:55:26,000
You assume your data is protected and your teams are aligned

1377
00:55:26,000 –> 00:55:28,320
but that assumption is almost always wrong.

1378
00:55:28,320 –> 00:55:30,160
Here is what happens when you stop assuming

1379
00:55:30,160 –> 00:55:31,280
and actually start measuring.

1380
00:55:31,280 –> 00:55:35,040
You might discover that 85% of your data is overclassified

1381
00:55:35,040 –> 00:55:36,320
because the people inside the system

1382
00:55:36,320 –> 00:55:38,400
don’t actually know what counts as sensitive.

1383
00:55:38,400 –> 00:55:40,320
You’ll see that policies exist on paper

1384
00:55:40,320 –> 00:55:42,320
but nobody follows them because they don’t match

1385
00:55:42,320 –> 00:55:43,840
how work actually gets done.

1386
00:55:43,840 –> 00:55:45,920
These discoveries show that critical workflows

1387
00:55:45,920 –> 00:55:47,520
often depend on specific people

1388
00:55:47,520 –> 00:55:48,960
rather than resilient systems

1389
00:55:48,960 –> 00:55:51,680
creating undocumented single points of failure.

1390
00:55:51,680 –> 00:55:53,200
You will likely find that you’re wasting

1391
00:55:53,200 –> 00:55:54,720
a lot of money on reconciliation

1392
00:55:54,720 –> 00:55:56,080
and manual data movement.

1393
00:55:56,080 –> 00:55:57,920
This friction is created by systems

1394
00:55:57,920 –> 00:55:59,120
that don’t talk to each other,

1395
00:55:59,120 –> 00:56:01,680
forcing your people to work around broken processes

1396
00:56:01,680 –> 00:56:03,280
instead of fixing the root cause.

1397
00:56:03,280 –> 00:56:05,200
This discovery is usually uncomfortable.

1398
00:56:05,200 –> 00:56:06,720
It challenges the narrative

1399
00:56:06,720 –> 00:56:08,800
you’ve been telling yourself about your organization

1400
00:56:08,800 –> 00:56:11,600
and suggests that leadership knows much less than they thought.

1401
00:56:11,600 –> 00:56:13,280
It means there is real work to be done

1402
00:56:13,280 –> 00:56:14,960
but this matters more now than ever

1403
00:56:14,960 –> 00:56:16,720
as we move through 2026.

1404
00:56:16,720 –> 00:56:18,480
You are likely deploying AI

1405
00:56:18,480 –> 00:56:20,560
or building autonomous workflows right now.

1406
00:56:20,560 –> 00:56:23,360
You’re planning to give digital systems access to your data

1407
00:56:23,360 –> 00:56:25,440
and trusting them to operate safely.

1408
00:56:25,440 –> 00:56:27,600
You are betting your entire competitive advantage

1409
00:56:27,600 –> 00:56:29,040
on moving fast with AI

1410
00:56:29,040 –> 00:56:31,280
while trying to maintain some level of control.

1411
00:56:31,280 –> 00:56:32,400
But here’s the thing,

1412
00:56:32,400 –> 00:56:36,080
you cannot do this without seeing your organization clearly.

1413
00:56:36,080 –> 00:56:38,240
Copilot is designed to respect your permissions,

1414
00:56:38,240 –> 00:56:40,400
your sensitivity labels, and your data governance.

1415
00:56:40,400 –> 00:56:42,400
However, copilot only works effectively

1416
00:56:42,400 –> 00:56:44,080
if your governance is actually real

1417
00:56:44,080 –> 00:56:46,640
and your permissions are aligned with actual roles.

1418
00:56:46,640 –> 00:56:48,320
If you haven’t done the foundational work

1419
00:56:48,320 –> 00:56:50,240
and you’re still operating on assumptions,

1420
00:56:50,240 –> 00:56:52,480
the AI will expose those flaws immediately.

1421
00:56:52,480 –> 00:56:54,720
It will surface data that should have stayed hidden

1422
00:56:54,720 –> 00:56:56,240
and give confidential information

1423
00:56:56,240 –> 00:56:57,840
to people who were never supposed to see it.

1424
00:56:57,840 –> 00:56:59,760
This is not a theoretical risk for the future.

1425
00:56:59,760 –> 00:57:03,440
In 2026, many organizations have already paused

1426
00:57:03,440 –> 00:57:05,360
their copilot rollouts because they realized

1427
00:57:05,360 –> 00:57:08,240
they didn’t actually know where their sensitive data was located.

1428
00:57:08,240 –> 00:57:09,440
But they started the deployment,

1429
00:57:09,440 –> 00:57:11,920
the system started oversharing information

1430
00:57:11,920 –> 00:57:14,480
and they had to stop everything to clean up the foundation.

1431
00:57:14,480 –> 00:57:16,240
That is a six-month delay in a market

1432
00:57:16,240 –> 00:57:19,040
where speed is your only real competitive advantage.

1433
00:57:19,040 –> 00:57:20,720
The organizations that win this year

1434
00:57:20,720 –> 00:57:22,960
won’t be the ones with perfect flawless data.

1435
00:57:22,960 –> 00:57:24,960
Those companies probably don’t even exist.

1436
00:57:24,960 –> 00:57:27,520
The winners will be the ones that see themselves clearly

1437
00:57:27,520 –> 00:57:30,320
and understand exactly where their silos and dependencies are.

1438
00:57:30,320 –> 00:57:32,000
They have identified the stress points,

1439
00:57:32,000 –> 00:57:32,960
quantified the waste,

1440
00:57:32,960 –> 00:57:35,840
and made intentional decisions about what needs to change.

1441
00:57:35,840 –> 00:57:37,840
These leaders know what they are protecting

1442
00:57:37,840 –> 00:57:39,840
and whether that protection is actually working.

1443
00:57:39,840 –> 00:57:41,280
They can move fast with AI

1444
00:57:41,280 –> 00:57:43,040
because they’ve built a structural foundation

1445
00:57:43,040 –> 00:57:44,560
that actually supports it.

1446
00:57:44,560 –> 00:57:46,640
Now, let’s talk about what this requires from you.

1447
00:57:46,640 –> 00:57:49,600
This isn’t just about running a piece of software like PerView.

1448
00:57:49,600 –> 00:57:52,080
It’s about a total organizational transformation.

1449
00:57:52,080 –> 00:57:53,760
The foundation work is significant

1450
00:57:53,760 –> 00:57:56,000
and requires you to scan your data,

1451
00:57:56,000 –> 00:57:58,800
classify it, and document who actually owns it.

1452
00:57:58,800 –> 00:58:00,640
You have to build access control frameworks

1453
00:58:00,640 –> 00:58:02,080
and retention policies

1454
00:58:02,080 –> 00:58:03,920
while establishing constant monitoring.

1455
00:58:03,920 –> 00:58:06,240
This takes time, resources, and a level of discipline

1456
00:58:06,240 –> 00:58:08,080
that most companies struggle to maintain.

1457
00:58:08,080 –> 00:58:09,520
You will also run into resistance

1458
00:58:09,520 –> 00:58:11,920
because the status quo technically works for now.

1459
00:58:11,920 –> 00:58:13,360
Work gets done, money flows,

1460
00:58:13,360 –> 00:58:15,120
and the organization survives another day.

1461
00:58:15,120 –> 00:58:16,960
But that status quo is incredibly fragile

1462
00:58:16,960 –> 00:58:18,720
because it depends on people knowing things

1463
00:58:18,720 –> 00:58:19,760
that aren’t documented

1464
00:58:19,760 –> 00:58:22,320
and systems being held together by manual effort.

1465
00:58:22,320 –> 00:58:24,560
This fragility becomes a critical failure point

1466
00:58:24,560 –> 00:58:26,960
when you try to scale or automate your business.

1467
00:58:26,960 –> 00:58:28,720
Scale always exposes weakness

1468
00:58:28,720 –> 00:58:30,960
and automation requires you to document things

1469
00:58:30,960 –> 00:58:32,400
that used to be implicit.

1470
00:58:32,400 –> 00:58:34,400
Governance forces you to look at that fragility

1471
00:58:34,400 –> 00:58:36,960
which is exactly why so many organizations resist it.

1472
00:58:36,960 –> 00:58:38,400
It’s not because governance is bad

1473
00:58:38,400 –> 00:58:40,800
but because it makes invisible problems visible.

1474
00:58:40,800 –> 00:58:43,040
Once a problem is visible, you are forced to fix it.

1475
00:58:43,040 –> 00:58:44,560
But you have to fix these issues anyway

1476
00:58:44,560 –> 00:58:46,240
because the invisible problems are already

1477
00:58:46,240 –> 00:58:47,440
costing you a fortune.

1478
00:58:47,440 –> 00:58:49,520
The only real question is whether you’ll fix them

1479
00:58:49,520 –> 00:58:52,720
intentionally as part of a plan or wait until something breaks

1480
00:58:52,720 –> 00:58:54,000
and you’re forced to react.

1481
00:58:54,000 –> 00:58:56,800
Think about a finance team spending two weeks

1482
00:58:56,800 –> 00:58:59,040
every month on reconciliation that shouldn’t have to happen.

1483
00:58:59,040 –> 00:59:00,880
That is a hidden cost that is happening right now

1484
00:59:00,880 –> 00:59:02,480
even if you aren’t calling it that.

1485
00:59:02,480 –> 00:59:04,800
When a sales team maintains a shadow CRM

1486
00:59:04,800 –> 00:59:06,640
because the official one is too slow,

1487
00:59:06,640 –> 00:59:09,760
you are paying for redundant systems and wasted effort.

1488
00:59:09,760 –> 00:59:11,440
When teams fight over data access

1489
00:59:11,440 –> 00:59:13,120
because ownership is unclear,

1490
00:59:13,120 –> 00:59:15,840
you pay for that in miscommunication and delays.

1491
00:59:15,840 –> 00:59:18,080
If you choose to govern intentionally,

1492
00:59:18,080 –> 00:59:21,120
you expose these costs and fix them once and for all.

1493
00:59:21,120 –> 00:59:23,600
If you don’t, you will simply pay those costs forever.

1494
00:59:23,600 –> 00:59:25,360
This is what governance actually buys you.

1495
00:59:25,360 –> 00:59:28,000
It’s not about auditor approval or simple compliance.

1496
00:59:28,000 –> 00:59:30,400
It’s about cost reduction and faster decision making.

1497
00:59:30,400 –> 00:59:33,440
It’s about better data quality and a faster path to AI deployment.

1498
00:59:33,440 –> 00:59:36,400
This is the narrative that actually gets investment from the board.

1499
00:59:36,400 –> 00:59:38,160
Don’t tell them you need to be compliant.

1500
00:59:38,160 –> 00:59:41,280
Tell them you’re wasting $72,000 a year on a process

1501
00:59:41,280 –> 00:59:43,200
that shouldn’t exist and you have a plan to fix it.

1502
00:59:43,200 –> 00:59:44,400
That gets a yes.

1503
00:59:44,400 –> 00:59:46,560
Tell them you can deploy co-pilot faster.

1504
00:59:46,560 –> 00:59:50,000
And with more confidence, if you have a clear picture of your data landscape,

1505
00:59:50,000 –> 00:59:52,080
show them the investment, the timeline,

1506
00:59:52,080 –> 00:59:54,000
and exactly what the business gains.

1507
00:59:54,000 –> 00:59:56,080
That is how you get the resources you need.

1508
00:59:56,080 –> 00:59:58,400
Most companies are underutilizing their data

1509
00:59:58,400 –> 01:00:00,720
because it’s scattered and nobody knows what’s available.

1510
01:00:00,720 –> 01:00:03,120
If you centralize and govern that data,

1511
01:00:03,120 –> 01:00:05,600
you unlock insights that drive better decisions.

1512
01:00:05,600 –> 01:00:06,880
That is a real business case.

1513
01:00:06,880 –> 01:00:09,920
This is where the 306090 road map becomes relevant.

1514
01:00:09,920 –> 01:00:11,360
It isn’t a compliance checklist.

1515
01:00:11,360 –> 01:00:13,440
It’s a transformation road map designed to move you

1516
01:00:13,440 –> 01:00:16,160
from assumption-based operating to reality-based operating.

1517
01:00:16,160 –> 01:00:18,320
In phase one, you focus on seeing the landscape

1518
01:00:18,320 –> 01:00:20,960
by running scans and establishing your baselines.

1519
01:00:20,960 –> 01:00:22,640
This is where you create the narrative.

1520
01:00:22,640 –> 01:00:25,120
In phase two, you build the initial governance

1521
01:00:25,120 –> 01:00:26,880
by classifying critical data

1522
01:00:26,880 –> 01:00:29,200
and observing where your process is actually break.

1523
01:00:29,200 –> 01:00:30,960
This is where you create the executive story.

1524
01:00:30,960 –> 01:00:33,200
By phase three, you are establishing control

1525
01:00:33,200 –> 01:00:36,400
and preparing for AI by creating AI-ready zones

1526
01:00:36,400 –> 01:00:37,440
and access frameworks.

1527
01:00:37,440 –> 01:00:39,920
By the end of 90 days, you won’t be perfectly governed

1528
01:00:39,920 –> 01:00:41,520
but you will be intentionally governed.

1529
01:00:41,520 –> 01:00:43,280
You’ll have the visibility and the road map

1530
01:00:43,280 –> 01:00:46,000
you need to deploy co-pilot with total confidence.

1531
01:00:46,000 –> 01:00:48,720
In the organizations that do this work, the results are obvious.

1532
01:00:48,720 –> 01:00:51,200
They move faster because they have intentional governance,

1533
01:00:51,200 –> 01:00:52,560
not because they have less of it.

1534
01:00:52,560 –> 01:00:53,680
They make decisions quickly

1535
01:00:53,680 –> 01:00:56,480
because they aren’t fighting over conflicting data sources.

1536
01:00:56,480 –> 01:00:59,440
These companies reduce costs by eliminating redundant work

1537
01:00:59,440 –> 01:01:00,800
and retiring broken systems.

1538
01:01:00,800 –> 01:01:02,720
They reduce risk through intentional protection

1539
01:01:02,720 –> 01:01:04,480
because they actually know what they are guarding

1540
01:01:04,480 –> 01:01:05,600
and who has access to it.

1541
01:01:05,600 –> 01:01:07,200
They can scale AI safely

1542
01:01:07,200 –> 01:01:08,960
because their foundation is clean

1543
01:01:08,960 –> 01:01:10,560
and their permissions are aligned.

1544
01:01:10,560 –> 01:01:11,920
They even attract better talent

1545
01:01:11,920 –> 01:01:13,520
because the organization is legible

1546
01:01:13,520 –> 01:01:15,920
and people understand why decisions are being made.

1547
01:01:15,920 –> 01:01:17,680
Work doesn’t require constant workarounds

1548
01:01:17,680 –> 01:01:20,720
which leads to less frustration and higher retention.

1549
01:01:20,720 –> 01:01:23,120
This is your true return on investment.

1550
01:01:23,120 –> 01:01:24,800
It’s not just a financial metric.

1551
01:01:24,800 –> 01:01:26,080
It’s an organizational one

1552
01:01:26,080 –> 01:01:29,440
that determines how competitive and sustainable your business will be.

1553
01:01:29,440 –> 01:01:31,360
You might be thinking this sounds great in theory

1554
01:01:31,360 –> 01:01:34,160
but you’re wondering how to actually get started tomorrow morning.

1555
01:01:34,160 –> 01:01:36,000
You likely already have pervue licenses

1556
01:01:36,000 –> 01:01:38,160
that you haven’t fully activated yet.

1557
01:01:38,160 –> 01:01:39,840
You might be using it for basic reporting

1558
01:01:39,840 –> 01:01:42,960
but you aren’t using it as the diagnostic tool it was meant to be.

1559
01:01:42,960 –> 01:01:44,960
Start by picking one critical data type

1560
01:01:44,960 –> 01:01:47,680
like your customer data or your intellectual property.

1561
01:01:47,680 –> 01:01:49,120
Run a scan and see where it lives,

1562
01:01:49,120 –> 01:01:51,120
how it’s classified and who has access to it.

1563
01:01:51,120 –> 01:01:52,560
Give yourself two weeks for this.

1564
01:01:52,560 –> 01:01:55,280
Then run the audits to check for policy reality alignment

1565
01:01:55,280 –> 01:01:57,440
and observe the behavior patterns of your organization.

1566
01:01:57,440 –> 01:01:58,560
Take another two weeks for that.

1567
01:01:58,560 –> 01:02:00,960
Once you have the data, create a 10-slide deck

1568
01:02:00,960 –> 01:02:02,640
that tells the story of what you found.

1569
01:02:02,640 –> 01:02:04,240
Take that deck to your leadership team.

1570
01:02:04,240 –> 01:02:05,680
Don’t ask for a software budget.

1571
01:02:05,680 –> 01:02:07,120
Ask for a data governance budget

1572
01:02:07,120 –> 01:02:09,120
to fix the specific gaps the data revealed.

1573
01:02:09,120 –> 01:02:10,720
That is how this becomes real.

1574
01:02:10,720 –> 01:02:12,480
From there you build incrementally,

1575
01:02:12,480 –> 01:02:14,560
phase by phase and domain by domain.

1576
01:02:14,560 –> 01:02:18,000
Eventually the organization becomes transparent and intentional.

1577
01:02:18,000 –> 01:02:19,920
This is not a technical transformation.

1578
01:02:19,920 –> 01:02:22,400
It is an architectural shift from operating on hope

1579
01:02:22,400 –> 01:02:23,920
to operating on observation.

1580
01:02:23,920 –> 01:02:27,840
In 2026, when your competitive advantage depends on AI and speed,

1581
01:02:27,840 –> 01:02:29,440
this shift is existential.

1582
01:02:29,440 –> 01:02:30,960
So here is what I want you to do next.

1583
01:02:30,960 –> 01:02:33,360
Connect with me, Mercopetus, on LinkedIn

1584
01:02:33,360 –> 01:02:35,520
and share your governance challenges.

1585
01:02:35,520 –> 01:02:38,640
The M365FM podcast is a community of people

1586
01:02:38,640 –> 01:02:41,280
thinking deeply about these exact questions.

1587
01:02:41,280 –> 01:02:42,480
Subscribe to the podcast

1588
01:02:42,480 –> 01:02:44,240
because this episode is just the beginning.

1589
01:02:44,240 –> 01:02:46,160
We have a full catalog of deep dives

1590
01:02:46,160 –> 01:02:47,840
on governance, fabric and co-pilot

1591
01:02:47,840 –> 01:02:49,840
that show you how to transform your organization

1592
01:02:49,840 –> 01:02:50,800
from the inside out.

1593
01:02:50,800 –> 01:02:52,560
If this reframing of purview

1594
01:02:52,560 –> 01:02:55,200
as a business intelligence tool resonates with you,

1595
01:02:55,200 –> 01:02:58,000
please leave a review on Apple podcasts or Spotify.

1596
01:02:58,000 –> 01:03:00,720
Help other leaders see purview as a lens for their business,

1597
01:03:00,720 –> 01:03:02,880
rather than just a checkbox for their auditors.

1598
01:03:02,880 –> 01:03:03,760
Start the work today.

1599
01:03:03,760 –> 01:03:05,520
Start with one scan and one narrative

1600
01:03:05,520 –> 01:03:08,160
to see what the data tells you about your organization.

1601
01:03:08,160 –> 01:03:09,600
Your next competitive advantage

1602
01:03:09,600 –> 01:03:12,480
isn’t a new piece of technology or a fancy new process.

1603
01:03:12,480 –> 01:03:14,960
Your next advantage is seeing your organization

1604
01:03:14,960 –> 01:03:16,400
clearly for the first time.

1605
01:03:16,400 –> 01:03:19,120
Everything else from cost reduction to AI speed

1606
01:03:19,120 –> 01:03:20,560
follows from that visibility.

1607
01:03:20,560 –> 01:03:22,560
Purview is the tool that makes that vision possible

1608
01:03:22,560 –> 01:03:24,080
so use it as the diagnostic tool

1609
01:03:24,080 –> 01:03:25,520
that shows you who you really are.

1610
01:03:25,520 –> 01:03:27,040
Thanks for listening to this episode

1611
01:03:27,040 –> 01:03:29,280
of the M365FM podcast.

1612
01:03:29,280 –> 01:03:31,520
I’m Mercopetus and I’ll see you next time.