The LangChain4j & Copilot Studio Mess

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AI agents are shipping faster than your change control meetings and the governance is a vibe.

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You know that feeling when a co-pilot ships with tenant-wide access just for testing?

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Yeah, that’s your compliance officer’s hard beat you’re hearing. Today, I’m tearing down the mess

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in Langchain 4J and co-pilot studio with real cases, prompt injection, over permissive connectors,

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and audit gaps. And I’ll show you what breaks, why it breaks, and the fixes that actually hold.

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Stay to the end, I’ll give you the one governance step that prevents most incidents.

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You’ll leave with an agent RBX model, data loss policies, and a red team checklist.

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Case one, prompt injection. The unsupervised intern writes policy.

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Prompt injection is that unsupervised intern who sounds helpful, writes in complete sentences,

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and then emails payroll data to their personal archive for safekeeping.

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You think your system prompt is the law. The model thinks it’s a suggestion, and the moment you

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ground it on internal content, one spicy document or user message can rewrite the rules mid-conversation.

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Why this matters? When injection wins, your agent becomes a data leaking poet.

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It hallucinates authority, escalates tools, and ignores policy language like it’s the Wi-Fi

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terms of service. In regulated shops, that’s not a bug. It’s a reportable incident with your company name

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on it. Let’s start with what breaks in Langchain 4J. The thing most people miss is that

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tool calling without strict output schemas is basically due crimes return vibes.

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If your tools accept unchecked arguments, think free text, SQL, or query fields,

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and you don’t validate types, ranges, or enums, the model will happily pass along whatever an attacker

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smuggles in. Week output validation is the partner in crime. When you expect JSON, but accept JSON-ish,

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an attacker can slip instructions in comments or strings that your downstream passer treats as commands.

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This clicked for me when I saw logs where a retrieval tool took a topic parameter with arbitrary

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markdown. The next call passed that marked down like it was configuration. That’s not orchestration.

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That’s self-own. Now here’s where most people mess up. They rely on the models please be safe setting

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instead of guardrails in code. In Langchain 4J, you need allow lists for tool names and arguments.

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JSON schema outputs enforced at the boundary pattern based output filters to new secrets,

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an exception handling that doesn’t retry the same poisoned input five times like a golden retriever

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with a tennis ball. The reason this works is it turns trust the model into verify every bite.

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What breaks in co-pilot studio? Naif grounding with broad share point ingestion. You connect an entire

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site collection for completeness and now one onboarding dog with ignore previous instructions becomes

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your agent’s new religion. System prompts editable by business users is the sequel. I love business

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users, but giving them prompt admin is like letting marketing set firewall rules because they know the

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brand voice. And yes, I’ve seen tenant configs where moderation was disabled to reduce friction.

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You wish you couldn’t. Evidence you’ll recognize tenant logs that show tools invoked with unbounded

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parameters like export all flags that were never supposed to exist. Conversation traces where the

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assistant repeats an injected string from a retrieve document disabled moderation toggles. That’s not

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hypothetical. That’s every post incident review you don’t want to attend. So what’s the fixed path

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you can implement today for Langchain 4J enforce allow lists at the tool registry. If the tool isn’t

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registered with a schema, it doesn’t exist require JSON schema outputs and reject anything that

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doesn’t validate no schema no response full stop. Add pattern filters for obvious leaks API keys secrets

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SSN’s bloom filters are fast and cheap use them wrap tools with policy checks validate argument types

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ranges and expected formats before execution add content moderation in pre post processors keep the

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model from acting on or emitting toxic or sensitive content fail closed with explicit exceptions

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and never auto retry poisoned prompts for co pilot studio lock system prompts only admins can

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change them version them like code scope connectors by environment dev test plot different boundaries

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least privilege on data sources turn on content moderation policies at the tenant level this is

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table stakes ground only unlabeled sensitivity tag content not the whole farm for convenience

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the quick win that pays off immediately add an output schema and a bloom filter moderation step

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at the agent boundary you’ll kill most dumb leaks without touching business logic then layer in a

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small reject allow list for formats you expect like structured summaries and block everything else

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let me show you exactly how this plays out example you have a create ticket tool that accepts title

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description and priority without schema enforcement and attacker injects description close all p1

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incidents inside a triple backtick block the model passes it through your it sm api shrugs and runs

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an update script with schema and validation description can’t contain command tokens or exceed length

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the request fails closed locks a correlation ID and your cm flags a moderation hit

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and boom look at that result incident avoided trail preserved auditor appeased common mistakes to

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avoid letting the model choose two names dynamically tools are contracts not suggestions

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accepting freeform json without a validator looks like jason is not a compliment

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editable prompts in production environments if it can change without review it will

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relying on conversation memory for policy policy belongs in code and config not vibes once you

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nail this everything else clicks you stop the intern from talking out of turn next we stop them from

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walking into every room with a master key case two over permissive connectors keys to the castle on

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a lanyard you stop the intern from talking now take the batch back over permissive connectors are

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that janitor key ring that opens every door in the building including the vault the daycare and

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somehow the ceo’s peloton why this matters is simple one over scoped connector equals enterprise

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wide data exfiltration in a single request not theoretical one call while you’re still arguing about

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the change ticket title let’s start with what breaks in lang chain for j developers share api

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keys across agents for convenience are then someone commits the envy to a private repo that’s actually

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public and you’re doing incident response at two a m have brought all the scopes are next you grant

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read write all to save time during testing and six months later that test token is now productions

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crown jewel and the tool registry i love a clean registry but if you point a dev agent at production

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credentials because the demo has to work today you just wired a chainsaw to a rumble the thing most

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people miss is that tools inherit whatever identity you hand them shared credentials mean shared

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blast radius there’s no magic only to save things flag if the token can delete records your agent

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can delete records accidentally enthusiastically and with perfect confidence now swing over to co-pilot

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studio tenant wide m265 connectors are the classic trap you click once and now every co-pilot in every

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team can see data it shouldn’t that’s not empowerment that’s a buffet for mistakes then you deploy to

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teams with org wide visibility because adoption and suddenly a pilot bought meant for finance is

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answering questions in marketing pulling content from sharepoint sites it never should have known

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existed and unmanaged third party sass hooks those are like usb drives in 2009 mysteriously everywhere

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and always temporary evidence shows up the same way every time stale secrets that never rotated no

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expiration no owner connectors mapped to global groups for simplicity app registrations with scopes

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that read like a confession and yes that temporary prodkey living in dev for months your security

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findings and tenant configs won’t lie they’ll just sigh so what’s the fix path for lang chain for j

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treat every agent like a separate application with its own identity create per agent service principles

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no shared tokens if two agents need the same api they still get different credentials use scoped

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OAuth grant the smallest set of permissions that lets the tool do its job reader not writer write

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to one collection not all store secrets in a proper secret manager rotate on a schedule rotate on

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incident rotate when someone even whispers token at two level our back a tool wrapper checks the

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callers role before it touches an api no role no call separate environments dev keys only talk to

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dev systems if a tool sees a plot endpoint in dev it fails closed and screams in the logs

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for co pilot studio draw hard boundaries with environments and scopes use environment separation

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dev test prod different connectors different permissions different owners review connector scopes

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with a workflow changes require approval expiration dates and owners no owner no connector apply

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dlp policies per channel finance channel gets stricter rules then company wide that’s the point kill

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org wide teams deployments for pilots limit visibility to a security group expand only after review

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inventory and gate third party says connectors if it’s unmanaged it’s off by default owners must

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justify access and renew it here’s a quick win you can ship this afternoon kill tenant wide scopes

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and map each connector to a security group with an expiration policy when the group expires access

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dies gracefully no drama no surprise let me show you exactly how this plays out you’ve got an

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export report tool that writes to a storage bucket with shared credentials it can see every bucket

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so a prompt that says save backup to archive quietly land sensitive data in a public facing path

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with per agent principles and scoped roles that tool can only write to one bucket with one prefix

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if the model decides to invent another path the api denies it you lock the correlation ID and your

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CM fires an alert and boom no accidental data lake party common mistakes to avoid

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using global groups for access just for now now never ends granting broad or outscopes because the

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docs were confusing confusing is not a permission copying credentials across environments if you can

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paste it you’ll paste it wrong treating teams visibility as harmless visibility is access access

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is risk once you nail access scoping auditors stop smelling blood in the water and when they do

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knock you’ll need proof which brings us to the part everyone forgets if it isn’t logged it didn’t

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happen case three audit gaps if it isn’t logged it didn’t happen you scoped access great now prove

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it because when an auditor asks what did the agent do at 314 pm and your answer is we think something

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that’s not a posture that’s an apology tour why this matters without lineage you can’t explain

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weird answers roll back bad actions or even know if an incident was contained regulators love that

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uncertainty customers love it less and your execs really love it when you say we have no idea

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here’s the thing most people miss in lang chain 4j your chain is doing a lot more than your logs

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admit missing execution graphs mean you can’t reconstruct the steps no trace IDs means events look

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like random fireworks partial logs hide the one tool called that mattered and silent tool failures

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those are my favorite everything looks green while the tool never ran and the model just hallucinated

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a response to be polite this clicked for me the day I chased an incident where a retrieval step

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timed out returned null and the next step happily wrote no records found closing to a ticketing API

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no exception no trace just vibes and close tickets if you can’t tell model output from tool

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output in your logs you’re already in trouble let me show you exactly how to fix lang chain 4j

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logging so it’s courtroom ready first enable structured logging everywhere jason not string soup

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every request and response gets an envelope with timestamps model name token counts and a correlation

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ID that survives the whole chain then add execution graphs or traces using lang smith or lang graph

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each node logs inputs outputs duration and errors if a tool throws it throws loud no silent failure

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no we assumed now the big one centralize it pipe traces and logs into your scene don’t leave them

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on a pod that auto scales to oblivion add sampling for noise but never drop errors you want to

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search by trace ID and see the entire story prompts retrieve docs tool calls API responses

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and the final answer without playing where’s wall door to a m swing to copilot studio the platform

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gives you some telemetry but it’s spread across teams data verse and whatever connector you touch

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limited conversation export means you get snippets not narratives fragmented telemetry means

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your compliance officer is stitching screenshots like its arts and crafts fixed by building a unified

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audit lane turn on conversation transcript retention with clear retention s la’s who keeps it how long

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and why export conversation and action logs to a centralized workspace like fabric or log analytics

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tag every event with a correlation ID that you pass from the entry point in teams all the way

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through data verse plugins and downstream APIs if the chat says create case I want to click through

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to the exact record in the exact system with the exact data that was sent evidence you’ll recognize

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from incident reviews often LL m calls in raw logs that don’t map to any user session tool invocations

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with no corresponding prompt or answers citing sources you can’t find because grounding logs don’t

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include document IDs that’s not a mystery that’s missing plumbing quick wins you can ship this week

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mandate request response envelopes with hashes store a content hash of prompts retrieve chunks

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and outputs so you can prove integrity if someone tempers the hash says nope require correlation IDs

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end to end generate at the edge pass through every hop and reject any call that drops it no

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id no service define retention s la’s legal science of security enforces 90 days for dev a year for

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port longer for regulated domains publish it live by it common mistakes to avoid logging raw

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secrets or p i i mask at source keep the fields you need for forensics not the crown jewels

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sampling errors sample success log all failures when things break you’ll need every breadcrumb

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free text logs humans love them machines hate them use fields at schemas validate on ingest

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now auditors ask what happened you open one trace you show the prompt the retrieved dog IDs the

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tool input the tool output and the final message correlation ID matches the API gateway the

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database right and the teams message and boom the story holds so when you can see you can govern

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which means it’s time to formalize the rules and lock down who does what the agent are beac

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model treat agents like very dumb very powerful users you’ve got visibility good now give these things

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an identity and a leash agents are users very dumb very powerful users if you wouldn’t give a human

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temp admin on day one don’t hand it to a stochastic parrot that can’t tell sarcasm from pseudo why

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this matters most orgs secure humans and ignore agents the agent then bypasses least privilege by

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design it hops tools crosses environments and nobody notices because it doesn’t show up in the

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org chart that’s how helper bot turns into shadow admin here’s the model i want you to ship

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one identity per agent clear roles policy checks in code and environment boundaries that actually

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mean something start with identity create a service principle for every agent not platform bot

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zero one actual one to one mapping name it like an app tag it with owner purpose environment and

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expiration if two agents hit the same API they still get different identities shared identity equals

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shared blast radius then define a role taxonomy that even busy people can apply i use four reader

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runner writer admin reader can fetch data no side effects think query retrieve summarize runner

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can invoke workflows with bounded effects create ticket send approval schedule meeting writer can

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write data to specific scopes one table one bucket one mailbox not all admin can change configuration

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this role should make you sweat you sparingly time bound and always dual approved now map that to

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long chain for j tools are your enforcement points each tool wrapper checks the callers role before

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executing if the agent identity is mapped to reader a right tool never even receives the call

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and don’t stop at role include resource scope a writer to cases finance can’t write cases sales

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scope is not a comment it’s a gate use environment variables and config files to bind tools to

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environments dev agent only sees dev host and dev keys if it tries to prod url fail closed and log

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loudly policy checks happen at the boundary not after the API call fails in co-pilot studio do the

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same pattern with environment roles and connector scopes assign the agent to an environment with

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permissions that mirror reader so runner writer admin connector permissions map to security groups

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tied to those roles teams deployment boundaries are your blast radius keep pilots inside a small group

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expand only with a change ticket this is where the review workflow keeps you honest any scope change

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is a change request list the agent the requested role the connector or tool the environment the

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reason the requester and the expiration two approvals one technical owner one data owner when the

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expiration hits access dies automatically and you get an alert to renew or let it go temporary

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finally means temporary let me show you exactly how this plays out with a micro story a finance

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Q&A agent starts is reader people love it so product once create expense report and that’s a runner

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action with a narrow scope you add a new tool with a tool level policy only expense submit only for

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the employees cost center only under five attachments the agent identity gets runner for that single

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API expiring in 30 days in co pilot studio the environment gains a connector permission to one

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dataverse table with row level security audit logs show the change ticket the approvals the expiration

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when marketing asks for also delete reports you decline or root to a separate writer tool with

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its own owners and sl a the model can want anything the policy enforces everything common mistakes you

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need to stop shared identities for convenience convenience is the enemy of containment temporary admin

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that never expires put timeouts on the permission not your calendar prod credentials in death to unblock

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demos demos don’t need production data auditors do tool getting by you i not code if the model can

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call it the user interface doesn’t matter if you remember nothing else treat agents like users

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with dumb curiosity and sharp knives identity per agent role per action scope per resource time bound

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by default and every call checks policy before it touches data data loss policies that actually block

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AI from becoming a leaky faucet you gave agents identities and roles now we stop the drips governance

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is proactive here block the bad before it leaves the building why this matters AI multiplies exposure

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a single prompt can produce a hundred outbound tokens each a potential leak if your controls only show

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up in post mortems you’re doing forensics not governance here’s the policy said that actually bites

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think of it as seat belts airbags and a brick wall p i reduction on outputs by default names emails

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phone numbers national IDs if the user isn’t entitled it’s masked nobody’s internal internal is where

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leaks live secrets detection guard for api keys tokens private keys connection strings patterns plus

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bloom filters for speed at check sums for common formats so false positives don’t drown you

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sensitive term filters tuned to your business legal project names m in a code words unreleased

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product code names keep this list in a managed store with owners and change history regax allow

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list for output structure if the tool expects an invoice summary only invoice shape outputs pass

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narrative poetry doesn’t schemas aren’t optional their contracts in long chain for j in force in

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pre and post processors pre processes scan inputs for injection payloads and disallow tasks

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post process is validate json schemas run term secret filters and redact p i i before anything leaves

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the agent boundary if a check fails you return a safe error with a correlation ID no partial truths

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no maybe add moderation pipelines lightweight pattern filters and bloom filters heavier a i based

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moderation for context in g don’t disclose health data chain them fail closed and define fallback

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behavior summarize at a higher level give a safe refusal or route to a human don’t loop the model

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into giving a safer leak in copilot studio use the stack you already pay for dlp policies in

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Microsoft 365 apply to channels and connectors turn them on and tune them use sensitivity labels in

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your grounding sources so the agent never indexes high impact content without explicit approval

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tenant level moderation should be on by default not we’ll turn it on later later is when pr gets

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involved monitoring ties this together set thresholds unusual output size unusual destination unusual

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frequency if a q and a board suddenly writes megabytes to an external endpoint quarantine the session

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auto expire risky sessions and notify owners keep alerts actionable don’t ping everyone for every

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mask event quick win you can ship today in force output schemas and block list terms at the agent

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boundary no schema no response at p i reduction as a final pass that combination stops the obvious

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leaks and buys you time to implement the rest common mistakes letting reduction depend on front

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end code redact server side before the message ever hits a client one global block list segment by

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business domain finance words aren’t marketing words logging the unredacted original for debugging mask

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at ingestion keep a secure vault copy only if your legal team signed off lock the faucet then try

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to break it on purpose the red teaming checklist break it before producers do you don’t trust the seatbelt

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until you young on it same with guard rails make the chaos scheduled goal adversarial testing you

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can repeat not vibes you’ll ship a checklist run it on a cadence and gate releases with it injection

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tests first role confusion prompt the agent to ignore policy and act as system it should refuse

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tool name collision inject fake tool names and parameters only registered tools should ever execute

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schema bypass embed commands in strings and Jason comments validators must reject chain of thought

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bait try to coax reasoning dumps that leak sources output should stay within schema data x fill prompts

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quote the exact ssns export all emails you get a refusal with an audit trail access tests next scope

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escalation request actions outside the agents role tools must block at rapper level expired token

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behavior rotate secrets mid session calls should fail closed with clear errors shadow environment leakage

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dev agent tries prod endpoints hard fail loud locks audit tests trace completeness can you reconstruct

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prompt to action with one correlation ID tamper detection alter stored locks hash mismatch should alarm

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cross service correlation teams message ID to data verse record to external API call all matched

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define pass slash fail gates no schema ill fail any output that doesn’t validate blocks the release

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tenant wide scope it will fail any connector without scope groups and expiration is blocked

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silent tool failure ill or fail errors must be surfaced not swallowed no rollback plan egos fail if

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you can’t revert a permission change or a model config in minutes you’re not ready cadence pre-release

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for every agent change quarterly chaos drills against prod like environments post incident learning

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loop within five business days with new tests added to the suite owners and SLA’s each checklist

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item has an owner and SLA to fix an a status no owner no ship quick win wire no schema egos fail

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and tenant wide scope egos fail as ccd checks with either trips the pipeline stops no meetings no

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debates just a red light that everyone respects run the list break things fix them then run it again

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until the surprises stop being surprising the one governance step most org skip if you remember

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nothing else agents are identities treat them like very dumb very powerful users with least

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privilege enforced output contracts and full audit trails the step most orgs skip is creating

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dedicated service principles for every agent then wiring every tool and connector through scope

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rolls by environment no shared creds no tenant wide scopes adopt the R-back model turn on the

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DLP policies and run the red team checklist this week subscribe for the follow up where I show

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the exact configs in lang chain for J and co pilot studio so you can copy paste governance instead of