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You deploy Copilot expecting a productivity breakthrough—but instead, you see a 300% spike in Data Loss Prevention events. That’s not failure. That’s visibility. AI isn’t discovering your best work—it’s exposing your permission debt. For years, overshared data sat quietly in SharePoint, buried in folders no one questioned. The “Everyone” group became an invisible open door. Now, with AI, that data is no longer buried—it’s conversational. Searchable. Actionable. And your current sensitivity labeling strategy? It’s not a shield. It’s a data graveyard—hiding information from the right people while doing nothing to stop the wrong exposure. This is the COLLABORATIVE AI SILO CRISIS, and it’s why your AI investment feels underwhelming instead of transformational.
⚠️ THE INHERITANCE PARADOX: AI MIRRORS YOUR MISTAKES
The biggest misconception in AI adoption is believing the tool enforces governance. It doesn’t. Copilot is a mirror—it inherits everything you’ve already configured, including years of messy permissions and inconsistent labeling. It doesn’t create risk; it reveals it at machine speed. What used to be hidden in a dusty folder is now instantly summarized in seconds. If a sensitive document was loosely labeled or broadly shared, AI will surface it without hesitation. This isn’t a breach—it’s your architecture working exactly as designed. The uncomfortable truth is that most organizations never achieved meaningful labeling coverage, often sitting below ten percent. We assumed “set it and forget it” would work, but data is fluid, and static labels simply can’t keep up with dynamic collaboration.
🔁 THE HIDDEN COST: THE AI REWORK LOOP
Here’s where the real damage happens. We celebrate AI productivity gains—hours saved per month—but ignore the silent tax: rework. When AI doesn’t have access to the right data, it doesn’t stop—it guesses. It pulls from outdated drafts, incomplete files, or irrelevant conversations. The result is output that looks polished but is fundamentally wrong. Employees then spend time verifying, correcting, and rebuilding those outputs. In many organizations, up to forty percent of AI-generated work requires correction. That means your top performers are losing weeks per year acting as validators instead of creators. The issue isn’t the AI—it’s the data silos and rigid labels blocking access to the real source of truth.
- AI saves time → but verification consumes it
- Restricted data → forces AI to guess
- Guessing → creates “confidently wrong” outputs
The old model of security was built on containment—lock data in folders, assign a label, and assume it’s safe. That model is broken. In a world of AI and distributed work, security must become context-aware. Instead of asking whether a file is labeled, we need to ask whether a specific user should access specific data at a specific moment. This is where modern approaches like Attribute-Based Access Control come in—evaluating user behavior, device health, location, and risk in real time. It’s a shift from static protection to dynamic intelligence. It allows organizations to remove unnecessary silos while still maintaining strong security boundaries. More importantly, it enables AI to access the right data at the right time, which is the only way to unlock real value.
🛠️ FIXING THE FOUNDATION BEFORE SCALING AI
Most organizations stuck in AI “pilot mode” don’t have a technology problem—they have a data architecture problem. Adding more sensitivity labels won’t fix it. In fact, it often makes things worse by increasing fragmentation. The real solution is structural: clean up permissions, automate labeling, and introduce context-aware access models. Start by auditing your SharePoint environment, especially broad access groups. Implement auto-labeling so coverage is no longer dependent on user behavior. Use restricted search controls to prevent AI from accessing high-risk data zones while you fix the underlying issues. This is not about locking everything down—it’s about enabling safe, intelligent flow of information.
- Audit and reduce permission sprawl
- Replace manual labeling with automated policies
- Introduce context-aware access decisions
For years, data governance was treated as a backend concern. In the AI era, it’s a frontline business strategy. Organizations that get this right will move faster, collaborate better, and extract real value from AI. Those that don’t will remain stuck—paying for powerful tools while only using a fraction of their capability. The difference comes down to one mindset shift: stop treating access as restriction and start treating it as controlled acceleration. When your data flows securely and intelligently, AI stops being a risk—and starts becoming a competitive advantage.
🔥 FINAL THOUGHT: YOUR AI IS ONLY AS GOOD AS YOUR DATA MODEL
The promise of AI isn’t broken—but your foundation might be. Sensitivity labels alone won’t save you. Static governance can’t keep up with dynamic work. And AI will continue to expose these gaps until they are fixed. The path forward is clear: move from containment to context, from static labels to dynamic access, and from siloed data to connected intelligence. If you want AI to deliver real results, you don’t need more prompts—you need a better model.
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You deploy co-pilot and wait for the productivity wave to hit,
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but instead of a breakthrough,
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you see a 300% spike in data loss prevention events.
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The assumption was that the AI would find our brilliance,
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but in reality, it is mostly finding our permission debt.
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The everyone group in your SharePoint environment
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is currently the largest security hole in your organization,
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and it acts like an open door that nobody bothered to close
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because the data was buried in the basement.
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Now that data is conversational.
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Your current sensitivity labeling strategy is not a shield.
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It is a data graveyard that hides information
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from the people who actually needed,
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while doing nothing to stop the AI from surfacing
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the wrong things to the wrong people.
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This is the collaborative AI-style or crisis.
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Today we are moving from containment to context,
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and we are going to save your AI investment
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by fixing the structural lie at the heart
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of your data governance model, the inheritance paradox.
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The fundamental flaw in most AI rollouts
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is a misunderstanding of how the machine thinks,
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and while you might assume that co-pilot makes decisions
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about who sees what, it actually does not.
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Co-pilot is a mirror that inherits your existing organizational
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entropy, so it does not create new access.
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It simply operationizes the access you already gave away years ago.
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Think of it as the spotlight effect.
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Before AI oversharing was latent,
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like a messy filing cabinet in a basement nobody visited,
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but AI makes that latent oversharing visible at machine speed.
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If a junior analyst asks for a summary of recent strategy shifts,
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and your M&A folder is set to internal,
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the AI will summarize the merger you have not announced yet.
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This is not a breach, it is a feature of your own architecture.
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The reality is that manual labeling coverage
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rarely exceeds 10% in the average enterprise,
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because we have spent a decade telling users
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to tag their files and they simply have not done it.
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We relied on a set it and forget it mentality for sensitivity tags,
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and assumed that once a file was marked public,
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it stayed that way forever.
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A data is fluid, which means a document that was public yesterday
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might contain a sensitive prompt response today.
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When we rely on static labels,
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we create invisible barriers to cross-departmental intelligence,
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and a highly confidential label on a project plan
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might stop the marketing team from seeing a launch date
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they actually need to do their jobs.
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It creates a silo, and these silos
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are the reason your AI feels less like an assistant
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and more like a restricted librarian.
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We have built a system based on the assumption
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that we can classify the world once and be done with it,
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but in a collaborative environment, that model breaks.
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If the labels are missing the AI sees too much,
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and if the labels are there, they are often too rigid,
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preventing the AI from connecting the dots between departments.
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We are currently seeing a massive gap between
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what the C-suite expects from AI,
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and what the data architecture allows,
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and you cannot have a high performing AI agent
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if your data is locked in 1990s style folder hierarchies.
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The inheritance paradox means the AI is only as secure
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as your worst SharePoint admins mistake from 2014.
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We have to admit that the current approach to classification
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is failing because it is static, manual, and treats data
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as an object to be locked away, rather than an asset to be used.
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Most organizations are currently in pilot purgatory
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because they realized six weeks in that their permissions are a mess.
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They try to fix it by adding more labels,
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but adding more labels to a broken system
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just creates more silos and does not solve
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the underlying problem of visibility.
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The shift we need is not about better tags,
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it is about a better model.
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We have been focusing on the what, which is the file itself,
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but we need to start focusing on the who, the where, and the why.
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The problem is not just that labels are missing,
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it is that the ones we have are static
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and do not account for the reality of how work happens today.
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Work is dynamic, so our security must be dynamic too.
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If we do not fix this, the AI will continue to hallucinate
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because it is looking at the wrong data or worse,
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it will tell the truth about data that should have been hidden.
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To fix the rework loop,
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we have to look at the underlying model of how we grant access,
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the high cost of rework.
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We talk about the productivity gains of AI
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as if they are a gross number
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and we see the nine hours saved per month
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and assume the job is done.
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But the reality is much messier because currently,
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about 40% of the time your employees saved by using AI
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is immediately lost to fixing what the AI got wrong.
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This is the hidden friction of the silo.
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When co-pilot doesn't have access to the good data,
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the finalized contract, the actual budget
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or the verified project plan, it doesn't stop.
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It guesses.
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It looks at the dark data it can see,
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like an old draft from a shared folder
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or speculative chat message
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and it builds a response based on that.
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The result is a rework loop that is quietly draining your ROI.
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We can actually quantify this digital debt.
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For your top performers, the power users
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who are leaning into these tools the most,
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this rework accounts for about one and a half weeks
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of lost time per year.
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Think about that.
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You are paying for a premium license to save time,
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but because your data is siloed,
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your best people are spending nearly two full work weeks
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just auditing machine generated errors.
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They are essentially acting as high paid proofreaders
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for a system that was supposed to be their co-pilot.
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This happens because rigid classification creates a vacuum.
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If the AI is barred from the system of record
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by an outdated sensitivity label,
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it will fill that vacuum
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with whatever system of convenience it can find.
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In departments like Finance and HR,
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this leads to outputs that are confidently wrong.
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A finance manager might ask for a summary of quarterly spend.
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If the actual ERP export is locked down,
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but a messy, unclassified working draft
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is sitting in a public teams channel,
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the AI will summarize the draft.
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The manager then spends 20 minutes finding the discrepancies.
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This is the friction between gross efficiency and net value.
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If it takes you two minutes to generate a report,
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but 20 minutes to verify the numbers,
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because the AI couldn't see the source of truth,
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you haven't actually gained anything.
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You've just shifted the labor from creation to correction.
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This rework isn't just a nuisance,
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it's a symptom of a structural failure.
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We've spent years building data silos to protect information,
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but in the age of generative AI,
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those silos act as blindfolds.
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When the AI is blindfolded, it hallucinates.
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It tries to please the user by connecting dots
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that shouldn't be connected.
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We are seeing organizations where the rework rate is so high
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that employees are starting to lose trust in the tool.
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They stop asking the AI for complex analysis
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and revert to using it for basic email drafting.
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The moment that happens, your AI strategy has failed.
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You are paying for an engine, but only using the headlights.
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To fix this rework loop, we have to stop looking at the AI
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and start looking at the plumbing.
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The good data exists.
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The brilliant insights are there.
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But they are trapped behind a wall of legacy permissions
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and static labels that were designed for a world
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where humans did all the searching.
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We need to move toward a model where the AI can see
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what it needs to see, exactly when it needs to see it,
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without compromising security.
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We have to look at the underlying model of how we grant access.
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Because as long as the AI is working with partial context,
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your employees will be working overtime to fix the results.
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That is a cost no organization can afford to ignore.
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From containment to context, we need to stop thinking
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about security as a wall.
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For decades, the industry relied on a hardened perimeter model.
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You were either inside the network or you were out,
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but that world is gone.
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Today, the perimeter is porous.
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People work from home.
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They use unmanaged devices and they collaborate
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with external vendors.
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In this environment, the old model of containment,
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where you lock data in a folder and assume it's safe
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because the folder is private is a dangerous illusion.
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It's dangerous because it assumes the threat is only outside.
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But the real threat in the age of co-pilot
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is the internal permission creep that has been accumulating
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for years.
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We need to move from a strategy of containment
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to a strategy of context.
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The old model was built for structure.
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It assumed that a file sensitivity was a fixed attribute.
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You tagged the document as confidential,
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and that was the end of the story.
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But in a collaborative AI environment,
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that tag is too blunt.
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It doesn't tell us enough.
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It doesn't account for who is asking,
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what device they are using, or where they are located.
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This is why we are seeing a massive shift
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toward dynamic access control or DAC.
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This isn't just a technical upgrade.
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It's a philosophical shift.
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It moves the decision-making process from the moment
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the file is created to the millisecond the data is requested.
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It's the difference between a static lock and a smart sensor.
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By 2026, the industry is mandating a shift
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toward attribute-based access control or ABAC.
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This is the foundation of the modern identity fabric.
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Instead of relying on a single static label,
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ABAC evaluates a dozen different risk signals in real time.
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It looks at the user's role, sure,
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but it also looks at the health of their laptop.
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It looks at their physical location.
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It even looks at their behavior.
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If a user who typically only accesses marketing files suddenly
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asks co-pilot to summarize the entire payroll database
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from a coffee shop in a different country,
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the system shouldn't just look at the sensitivity label.
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It should look at the context and say, no.
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This is how we move from, is this file labeled?
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Should this user see this data now?
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It's a subtle shift with massive implications.
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It allows us to be more permissive when the risk is low
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and more restrictive when the risk is high.
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It breaks the silos because it allows for just-in-time access.
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If the marketing team needs to see that highly confidential launch date,
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the system can grant them temporary,
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read-only access based on the context of their current project
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without permanently breaking the security of the file.
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This is the only way to enable the kind of cross-departmental
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intelligence that AI promises without turning your tenant
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into a data free for all.
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The ROI of these dynamic systems is already becoming clear.
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Organizations that move away from static labels
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to water context-aware model are seeing 15 to 30% reductions
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in their cyber insurance premiums.
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Why?
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Because insurers know that static labels are easy to bypass or ignore.
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Dynamic systems, on the other hand, are much harder to exploit.
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They provide a level of continuous verification
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that static models simply can't match.
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They reduce the blast radius of a compromised account
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because the attacker can't just inherit years of sloppy permissions.
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Every request is a new evaluation.
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Every interaction is a new opportunity to verify trust.
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This isn't just a theoretical shift for IT architects.
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It is a survival requirement for the next wave of a genetic AI.
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As we move toward agents that can take actions on our behalf,
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scheduling meetings, moving money, or updating records,
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we cannot rely on a "set it and forget it" security model.
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We need a system that understands the intent behind the action.
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We need security that is as smart as the AI it is protecting.
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This leads us to the technical roadmap.
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Because while the philosophy is changing,
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the tools to implement it are finally arriving in the purview ecosystem.
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The 2026 purview roadmap, the shift to what context-aware intelligence
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is finally showing up in the actual architecture of the Microsoft ecosystem.
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We are moving past the era where purview was just a tool for compliance reporting
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and by 2026 it has evolved into a real-time diagnostic engine.
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One of the most significant changes is the move to always on diagnostics for endpoint DLP.
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In the past, troubleshooting a policy failure meant a week of back-and-forth between IT and the user
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while they tried to reproduce a glitch that happened once.
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Now those diagnostic traces are stored locally on the device
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in a secure, compressed format for 90 days.
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This allows the system to analyze exactly why a specific file was blocked
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or why a label failed to apply without ever sending the actual sensitive content to the cloud.
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It is local visibility paired with global enforcement.
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This diagnostic power is being amplified by AI-powered explanation tools.
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We have reached a level of complexity where human admins can no longer manually track the difference
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between a dozen different DLP policies.
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When a policy changes, the system now generates a natural language summary
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to explain exactly what was modified and which user groups are affected.
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It also shows what the potential impact on collaborative workflows will be
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which reduces the risk of policy drift.
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This is when security rules become so tangled that they start blocking legitimate work.
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These tools allow the security team to act as enablers rather than gatekeepers
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by providing clarity.
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On the rules of the road in a language, the business can actually understand.
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A critical piece of this roadmap is the role of restricted SharePoint search or RSS.
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This is the emergency break for your AI deployment.
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It allows you to explicitly exclude high-risk sites like your board papers,
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your payroll data, or your legal strategy from being used as grounding for co-pilot.
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Before you even touch a sensitivity label, you can use RSS to ensure that the AI
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simply cannot see the most dangerous corners of your tenant.
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It is a foundational layer of protection that recognizes that not all data is created equal.
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This gives you the breathing room to fix your permission debt.
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Without having to shut down the entire AI experiment,
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we are also seeing a massive expansion in auto labeling capabilities.
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The goal for 2026 is to be secured by default.
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Your SharePoint libraries are no longer passive buckets because they are becoming active participants
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in your security posture.
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When a file is uploaded to a specific library,
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the default label of that library is applied at rest automatically.
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If that file contains sensitive information types,
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the system can now override lower priority manual labels.
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This closes the coverage gap that has plagued manual systems for years.
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It ensures that even if a user forgets to tag a document, the platform has their back.
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It is the move from a voluntary system to a mandatory automated infrastructure.
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To manage this at scale,
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we are increasingly relying on the Graph API for custom usage analytics.
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We can now look under the hood to see exactly which departments are siloed.
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If the R&D team has a 90% rework rate because they are blocked from seeing the engineering specs,
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the data will show it.
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We can identify the bottlenecks where rigid classification is killing productivity
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and adjust the policies in real time.
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This is how we move from a reactive security posture to a proactive governance strategy.
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We are using the data to tell us where the system is breaking rather than waiting for
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a user to complain or a breach to occur.
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But even with the best tools,
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the strategy fails without executive alignment.
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You can have the most advanced purview setup in the world,
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but if your leadership still views data as something to be hoarded,
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you will never realize the full value of AI.
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This brings us to a new kind of risk that is emerging from the ground up.
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It is a challenge that is not coming from hackers or external threats,
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but from your own employees trying to be productive.
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We need to talk about the Citizen Developer Delimmer.
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The Citizen Developer Delimmer,
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we are currently witnessing the largest explosion of unregulated creation
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in the history of enterprise computing.
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There are now over 1 million low-code assets living inside the Microsoft ecosystem.
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We have empowered employees to build their own apps,
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their own flows, and now their own AI agents.
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But this empowerment has a dark side.
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We have moved from Shadow IT to Shadow AI,
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where non-technical users are building complex agents on top of ungoverned messy data.
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This is the risk of agents sprawl.
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It is what happens when a marketing coordinator builds a custom GPT and co-pilot studio
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to help with vendor queries,
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but they inadvertently ground that agent on a sharepoint side
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containing every contract the company has signed since 2012.
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The dilemma is that we cannot simply turn it off.
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If you kill the ability for people to solve their own problems,
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you kill the very agility that AI is supposed to provide.
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Instead, we have to move toward a tiered governance model.
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Not all agents are created equal.
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A low-code tool that helps an individual summarise their own emails
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using standard Teams connectors is a low-risk asset.
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It should bypass the heavy duty review process.
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But the moment an agent touches a system of record-like dataverse
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or a sensitive SQL database, it needs a gatekeeper.
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We have to stop treating citizen development as a single category
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and start treating it as a spectrum of risk.
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The industry is pivoting from the old concept of citizen development
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to what we now call AI-augmented personal building.
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The difference is subtle but crucial.
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In the old world, the user was a developer,
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but in the new world, the user is an orchestrator.
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They are directing the AI to build the solution for them.
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This makes the guardrails in co-pilot studio more important than the code itself.
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We need to implement content moderation layers
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that prevent these agents from hallucinating or leaking data
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through prompt injection attacks
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that the user did not even know where possible.
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You would not let a summer intern write your firewall rules
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so we should not let them build agents that have read access to the entire corporate wiki.
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The fix is structural.
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We need to automate the enforcement of data loss prevention policies
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within the low-code environment.
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If a user tries to connect an AI agent to an external unmanaged dropbox account,
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the system should block it instantly.
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If they try to build a bot that queries a library marked
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with a highly confidential sensitivity label,
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the deployment should trigger an automatic manual review by the ITOPS team.
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This creates a secure-by-design pathway for innovation.
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It allows the builders to build while ensuring that the most sensitive assets of the organization
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remain behind the context-aware walls we have been discussing.
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We are moving into an era where every employee is a potential architect of the company's intelligence.
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That is a massive competitive advantage if managed correctly,
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but it is a catastrophic liability if left to chance.
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The goal is not to stop the sprawl, but to govern the flow.
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We need to provide the citizen builders with pre-approved templates and safe zones
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where they can experiment without risking the crown jewels.
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This ensures that the agents they create are an extension of our governance strategy
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rather than an exception to it.
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This leads us to the final hurdle, which is getting the C-suite to treat access as a strategic asset.
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You cannot govern a million agents from the basement.
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You need executive alignment.
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It is time to move the conversation from the server room to the boardroom.
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The executive buy-in strategy.
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The technical battle for context-aware security is being won in the code,
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but the strategic battle is still being lost in the boardroom.
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For years, identity and access management was treated like a plumbing issue.
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It was something for the basement teams to handle while the business focused on growth.
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In 2026, that mindset is a liability.
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We have to reframe the entire conversation because identity is no longer just a login.
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It is the core control plane for digital trust.
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If your leadership doesn't understand that, your AI deployment will remain a series of expensive,
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disconnected experiments.
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We need to stop pitching security as a cost center.
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Instead, we have to frame it as a decision-villacity enabler.
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Most executives view security as a series of no moments.
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No, you can't use that tool.
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No, you can't share that file.
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But a modern identity fabric does the opposite.
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It provides the yes that allows the business to move faster.
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When you have continuous context-aware verification,
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you can onboard a new partner in hours instead of weeks.
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You can give a consultant access to exactly what they need for a three-day project,
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and then you can watch that access vanish automatically the moment they finished.
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This isn't about restriction.
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It's about agility.
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It's about creating an environment where the right people can act on the right information
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without the friction of manual approvals.
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We can see the cost of ignoring this in the M&A market.
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There is a massive maturity gap emerging between organizations
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that have mastered identity governance and those that are still relying on static legacy models.
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When a high maturity firm acquires a competitor,
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they can integrate the new workforce into their AI ecosystem in days.
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But for firms with poor identity hygiene,
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that integration drags on for months,
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and this delay bleeds value while stalling the very synergies the merger was supposed to create.
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Poor identity governance is a direct hit to post-merger value.
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It is a drag on the balance sheet that most CEOs haven't even quantified yet.
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This is why the pitch for a 5-to-20% budget increase for identity fabric
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isn't a request for more securities bend.
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It's an investment in growth infrastructure.
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It's the digital equivalent of building a high-speed rail network between your data silos.
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You are moving from a hardened perimeter that everyone tries to bypass
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to a continuous verification model that actually supports the way people work.
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You are telling the C-suite that if they want the five times productivity boost that AI promises,
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they have to pay for the foundation that makes it safe.
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You have to move the goalposts from protecting the data to managing the flow of intelligence.
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The shift to a context-aware model is the only way to build a resilient enterprise
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in an age of a genetic AI.
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It allows the organization to scale its intelligence without scaling its risk.
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When the C-suite treats access as a strategic asset,
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the entire culture of the company shifts.
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Security becomes a shared responsibility,
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and data becomes a liquid resource rather than a hoarded secret.
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It's time to stop looking at the cost of the tools
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and start looking at the cost of the friction.
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Because in the AI era, the fastest company wins,
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and you can't be fast if your data is trapped in a lie.
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Your 2026 mandate is clear.
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You must audit your permission debt before you scale your AI licenses.
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The technology is ready, but your architecture likely isn't.
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The challenge for the next quarter is to implement mandatory auto labeling
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and execute a 90-day site cleanup to close the most obvious gaps.
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Stop the hooding, start the flow.
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If you want to share your silo-breaking progress or discuss how your team is handling the shift
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to context-aware permissions, connect with Mirko Peters on LinkedIn.
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Let's build an intelligence model that actually works.
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Subscribe for more deep dives into the future of work.
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Stay secure, stay productive.
00:00:00,000 --> 00:00:02,640
You deploy co-pilot and wait for the productivity wave to hit,
2
00:00:02,640 --> 00:00:03,840
but instead of a breakthrough,
3
00:00:03,840 --> 00:00:06,960
you see a 300% spike in data loss prevention events.
4
00:00:06,960 --> 00:00:09,360
The assumption was that the AI would find our brilliance,
5
00:00:09,360 --> 00:00:12,360
but in reality, it is mostly finding our permission debt.
6
00:00:12,360 --> 00:00:14,320
The everyone group in your SharePoint environment
7
00:00:14,320 --> 00:00:17,320
is currently the largest security hole in your organization,
8
00:00:17,320 --> 00:00:19,800
and it acts like an open door that nobody bothered to close
9
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because the data was buried in the basement.
10
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Now that data is conversational.
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Your current sensitivity labeling strategy is not a shield.
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It is a data graveyard that hides information
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from the people who actually needed,
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while doing nothing to stop the AI from surfacing
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the wrong things to the wrong people.
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This is the collaborative AI-style or crisis.
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Today we are moving from containment to context,
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and we are going to save your AI investment
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by fixing the structural lie at the heart
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of your data governance model, the inheritance paradox.
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The fundamental flaw in most AI rollouts
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is a misunderstanding of how the machine thinks,
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and while you might assume that co-pilot makes decisions
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about who sees what, it actually does not.
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Co-pilot is a mirror that inherits your existing organizational
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entropy, so it does not create new access.
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It simply operationizes the access you already gave away years ago.
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Think of it as the spotlight effect.
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Before AI oversharing was latent,
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like a messy filing cabinet in a basement nobody visited,
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but AI makes that latent oversharing visible at machine speed.
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If a junior analyst asks for a summary of recent strategy shifts,
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and your M&A folder is set to internal,
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the AI will summarize the merger you have not announced yet.
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This is not a breach, it is a feature of your own architecture.
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The reality is that manual labeling coverage
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rarely exceeds 10% in the average enterprise,
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because we have spent a decade telling users
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to tag their files and they simply have not done it.
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We relied on a set it and forget it mentality for sensitivity tags,
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and assumed that once a file was marked public,
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it stayed that way forever.
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A data is fluid, which means a document that was public yesterday
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might contain a sensitive prompt response today.
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When we rely on static labels,
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we create invisible barriers to cross-departmental intelligence,
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and a highly confidential label on a project plan
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might stop the marketing team from seeing a launch date
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they actually need to do their jobs.
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It creates a silo, and these silos
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are the reason your AI feels less like an assistant
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and more like a restricted librarian.
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We have built a system based on the assumption
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that we can classify the world once and be done with it,
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but in a collaborative environment, that model breaks.
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If the labels are missing the AI sees too much,
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and if the labels are there, they are often too rigid,
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preventing the AI from connecting the dots between departments.
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We are currently seeing a massive gap between
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what the C-suite expects from AI,
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and what the data architecture allows,
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and you cannot have a high performing AI agent
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if your data is locked in 1990s style folder hierarchies.
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The inheritance paradox means the AI is only as secure
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as your worst SharePoint admins mistake from 2014.
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We have to admit that the current approach to classification
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is failing because it is static, manual, and treats data
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as an object to be locked away, rather than an asset to be used.
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Most organizations are currently in pilot purgatory
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because they realized six weeks in that their permissions are a mess.
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They try to fix it by adding more labels,
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but adding more labels to a broken system
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just creates more silos and does not solve
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the underlying problem of visibility.
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The shift we need is not about better tags,
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it is about a better model.
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We have been focusing on the what, which is the file itself,
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but we need to start focusing on the who, the where, and the why.
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The problem is not just that labels are missing,
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it is that the ones we have are static
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and do not account for the reality of how work happens today.
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Work is dynamic, so our security must be dynamic too.
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If we do not fix this, the AI will continue to hallucinate
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because it is looking at the wrong data or worse,
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it will tell the truth about data that should have been hidden.
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To fix the rework loop,
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we have to look at the underlying model of how we grant access,
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the high cost of rework.
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We talk about the productivity gains of AI
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as if they are a gross number
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and we see the nine hours saved per month
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and assume the job is done.
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But the reality is much messier because currently,
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about 40% of the time your employees saved by using AI
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is immediately lost to fixing what the AI got wrong.
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This is the hidden friction of the silo.
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When co-pilot doesn't have access to the good data,
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the finalized contract, the actual budget
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or the verified project plan, it doesn't stop.
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It guesses.
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It looks at the dark data it can see,
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like an old draft from a shared folder
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or speculative chat message
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and it builds a response based on that.
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The result is a rework loop that is quietly draining your ROI.
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We can actually quantify this digital debt.
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For your top performers, the power users
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who are leaning into these tools the most,
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this rework accounts for about one and a half weeks
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of lost time per year.
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Think about that.
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You are paying for a premium license to save time,
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but because your data is siloed,
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your best people are spending nearly two full work weeks
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just auditing machine generated errors.
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They are essentially acting as high paid proofreaders
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for a system that was supposed to be their co-pilot.
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This happens because rigid classification creates a vacuum.
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If the AI is barred from the system of record
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by an outdated sensitivity label,
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it will fill that vacuum
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with whatever system of convenience it can find.
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In departments like Finance and HR,
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this leads to outputs that are confidently wrong.
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A finance manager might ask for a summary of quarterly spend.
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If the actual ERP export is locked down,
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but a messy, unclassified working draft
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is sitting in a public teams channel,
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the AI will summarize the draft.
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The manager then spends 20 minutes finding the discrepancies.
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This is the friction between gross efficiency and net value.
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If it takes you two minutes to generate a report,
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but 20 minutes to verify the numbers,
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because the AI couldn't see the source of truth,
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you haven't actually gained anything.
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You've just shifted the labor from creation to correction.
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This rework isn't just a nuisance,
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it's a symptom of a structural failure.
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We've spent years building data silos to protect information,
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but in the age of generative AI,
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those silos act as blindfolds.
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When the AI is blindfolded, it hallucinates.
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It tries to please the user by connecting dots
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that shouldn't be connected.
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We are seeing organizations where the rework rate is so high
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that employees are starting to lose trust in the tool.
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They stop asking the AI for complex analysis
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and revert to using it for basic email drafting.
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The moment that happens, your AI strategy has failed.
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You are paying for an engine, but only using the headlights.
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To fix this rework loop, we have to stop looking at the AI
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and start looking at the plumbing.
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The good data exists.
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The brilliant insights are there.
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But they are trapped behind a wall of legacy permissions
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and static labels that were designed for a world
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where humans did all the searching.
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We need to move toward a model where the AI can see
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what it needs to see, exactly when it needs to see it,
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without compromising security.
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We have to look at the underlying model of how we grant access.
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Because as long as the AI is working with partial context,
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your employees will be working overtime to fix the results.
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That is a cost no organization can afford to ignore.
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From containment to context, we need to stop thinking
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about security as a wall.
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For decades, the industry relied on a hardened perimeter model.
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You were either inside the network or you were out,
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but that world is gone.
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Today, the perimeter is porous.
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People work from home.
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They use unmanaged devices and they collaborate
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with external vendors.
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In this environment, the old model of containment,
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where you lock data in a folder and assume it's safe
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because the folder is private is a dangerous illusion.
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It's dangerous because it assumes the threat is only outside.
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But the real threat in the age of co-pilot
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is the internal permission creep that has been accumulating
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for years.
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We need to move from a strategy of containment
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to a strategy of context.
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The old model was built for structure.
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It assumed that a file sensitivity was a fixed attribute.
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You tagged the document as confidential,
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and that was the end of the story.
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But in a collaborative AI environment,
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that tag is too blunt.
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It doesn't tell us enough.
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It doesn't account for who is asking,
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what device they are using, or where they are located.
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This is why we are seeing a massive shift
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toward dynamic access control or DAC.
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This isn't just a technical upgrade.
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It's a philosophical shift.
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It moves the decision-making process from the moment
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the file is created to the millisecond the data is requested.
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It's the difference between a static lock and a smart sensor.
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By 2026, the industry is mandating a shift
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toward attribute-based access control or ABAC.
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This is the foundation of the modern identity fabric.
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Instead of relying on a single static label,
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ABAC evaluates a dozen different risk signals in real time.
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It looks at the user's role, sure,
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but it also looks at the health of their laptop.
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It looks at their physical location.
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It even looks at their behavior.
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If a user who typically only accesses marketing files suddenly
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asks co-pilot to summarize the entire payroll database
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from a coffee shop in a different country,
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the system shouldn't just look at the sensitivity label.
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It should look at the context and say, no.
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This is how we move from, is this file labeled?
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Should this user see this data now?
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It's a subtle shift with massive implications.
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It allows us to be more permissive when the risk is low
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and more restrictive when the risk is high.
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It breaks the silos because it allows for just-in-time access.
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If the marketing team needs to see that highly confidential launch date,
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the system can grant them temporary,
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read-only access based on the context of their current project
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without permanently breaking the security of the file.
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This is the only way to enable the kind of cross-departmental
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intelligence that AI promises without turning your tenant
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into a data free for all.
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The ROI of these dynamic systems is already becoming clear.
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Organizations that move away from static labels
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to water context-aware model are seeing 15 to 30% reductions
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in their cyber insurance premiums.
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Why?
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Because insurers know that static labels are easy to bypass or ignore.
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Dynamic systems, on the other hand, are much harder to exploit.
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They provide a level of continuous verification
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that static models simply can't match.
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They reduce the blast radius of a compromised account
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because the attacker can't just inherit years of sloppy permissions.
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Every request is a new evaluation.
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Every interaction is a new opportunity to verify trust.
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This isn't just a theoretical shift for IT architects.
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It is a survival requirement for the next wave of a genetic AI.
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As we move toward agents that can take actions on our behalf,
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scheduling meetings, moving money, or updating records,
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we cannot rely on a "set it and forget it" security model.
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We need a system that understands the intent behind the action.
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We need security that is as smart as the AI it is protecting.
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This leads us to the technical roadmap.
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Because while the philosophy is changing,
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the tools to implement it are finally arriving in the purview ecosystem.
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The 2026 purview roadmap, the shift to what context-aware intelligence
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is finally showing up in the actual architecture of the Microsoft ecosystem.
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We are moving past the era where purview was just a tool for compliance reporting
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and by 2026 it has evolved into a real-time diagnostic engine.
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One of the most significant changes is the move to always on diagnostics for endpoint DLP.
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In the past, troubleshooting a policy failure meant a week of back-and-forth between IT and the user
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while they tried to reproduce a glitch that happened once.
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Now those diagnostic traces are stored locally on the device
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in a secure, compressed format for 90 days.
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This allows the system to analyze exactly why a specific file was blocked
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or why a label failed to apply without ever sending the actual sensitive content to the cloud.
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It is local visibility paired with global enforcement.
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This diagnostic power is being amplified by AI-powered explanation tools.
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We have reached a level of complexity where human admins can no longer manually track the difference
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between a dozen different DLP policies.
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When a policy changes, the system now generates a natural language summary
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to explain exactly what was modified and which user groups are affected.
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It also shows what the potential impact on collaborative workflows will be
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which reduces the risk of policy drift.
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This is when security rules become so tangled that they start blocking legitimate work.
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These tools allow the security team to act as enablers rather than gatekeepers
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by providing clarity.
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On the rules of the road in a language, the business can actually understand.
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A critical piece of this roadmap is the role of restricted SharePoint search or RSS.
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This is the emergency break for your AI deployment.
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It allows you to explicitly exclude high-risk sites like your board papers,
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your payroll data, or your legal strategy from being used as grounding for co-pilot.
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Before you even touch a sensitivity label, you can use RSS to ensure that the AI
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simply cannot see the most dangerous corners of your tenant.
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It is a foundational layer of protection that recognizes that not all data is created equal.
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This gives you the breathing room to fix your permission debt.
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Without having to shut down the entire AI experiment,
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we are also seeing a massive expansion in auto labeling capabilities.
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The goal for 2026 is to be secured by default.
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Your SharePoint libraries are no longer passive buckets because they are becoming active participants
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in your security posture.
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When a file is uploaded to a specific library,
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the default label of that library is applied at rest automatically.
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If that file contains sensitive information types,
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the system can now override lower priority manual labels.
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This closes the coverage gap that has plagued manual systems for years.
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It ensures that even if a user forgets to tag a document, the platform has their back.
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It is the move from a voluntary system to a mandatory automated infrastructure.
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To manage this at scale,
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we are increasingly relying on the Graph API for custom usage analytics.
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We can now look under the hood to see exactly which departments are siloed.
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If the R&D team has a 90% rework rate because they are blocked from seeing the engineering specs,
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the data will show it.
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We can identify the bottlenecks where rigid classification is killing productivity
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and adjust the policies in real time.
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This is how we move from a reactive security posture to a proactive governance strategy.
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We are using the data to tell us where the system is breaking rather than waiting for
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a user to complain or a breach to occur.
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But even with the best tools,
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the strategy fails without executive alignment.
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You can have the most advanced purview setup in the world,
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but if your leadership still views data as something to be hoarded,
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you will never realize the full value of AI.
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This brings us to a new kind of risk that is emerging from the ground up.
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It is a challenge that is not coming from hackers or external threats,
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but from your own employees trying to be productive.
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We need to talk about the Citizen Developer Delimmer.
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The Citizen Developer Delimmer,
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we are currently witnessing the largest explosion of unregulated creation
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in the history of enterprise computing.
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There are now over 1 million low-code assets living inside the Microsoft ecosystem.
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We have empowered employees to build their own apps,
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their own flows, and now their own AI agents.
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But this empowerment has a dark side.
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We have moved from Shadow IT to Shadow AI,
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where non-technical users are building complex agents on top of ungoverned messy data.
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This is the risk of agents sprawl.
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It is what happens when a marketing coordinator builds a custom GPT and co-pilot studio
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to help with vendor queries,
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but they inadvertently ground that agent on a sharepoint side
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containing every contract the company has signed since 2012.
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The dilemma is that we cannot simply turn it off.
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If you kill the ability for people to solve their own problems,
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you kill the very agility that AI is supposed to provide.
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Instead, we have to move toward a tiered governance model.
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Not all agents are created equal.
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A low-code tool that helps an individual summarise their own emails
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using standard Teams connectors is a low-risk asset.
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It should bypass the heavy duty review process.
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But the moment an agent touches a system of record-like dataverse
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or a sensitive SQL database, it needs a gatekeeper.
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We have to stop treating citizen development as a single category
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and start treating it as a spectrum of risk.
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The industry is pivoting from the old concept of citizen development
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to what we now call AI-augmented personal building.
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The difference is subtle but crucial.
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In the old world, the user was a developer,
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but in the new world, the user is an orchestrator.
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They are directing the AI to build the solution for them.
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This makes the guardrails in co-pilot studio more important than the code itself.
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We need to implement content moderation layers
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that prevent these agents from hallucinating or leaking data
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through prompt injection attacks
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that the user did not even know where possible.
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You would not let a summer intern write your firewall rules
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so we should not let them build agents that have read access to the entire corporate wiki.
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The fix is structural.
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We need to automate the enforcement of data loss prevention policies
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within the low-code environment.
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If a user tries to connect an AI agent to an external unmanaged dropbox account,
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the system should block it instantly.
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If they try to build a bot that queries a library marked
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with a highly confidential sensitivity label,
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the deployment should trigger an automatic manual review by the ITOPS team.
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This creates a secure-by-design pathway for innovation.
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It allows the builders to build while ensuring that the most sensitive assets of the organization
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remain behind the context-aware walls we have been discussing.
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We are moving into an era where every employee is a potential architect of the company's intelligence.
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That is a massive competitive advantage if managed correctly,
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but it is a catastrophic liability if left to chance.
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The goal is not to stop the sprawl, but to govern the flow.
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We need to provide the citizen builders with pre-approved templates and safe zones
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where they can experiment without risking the crown jewels.
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This ensures that the agents they create are an extension of our governance strategy
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rather than an exception to it.
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This leads us to the final hurdle, which is getting the C-suite to treat access as a strategic asset.
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You cannot govern a million agents from the basement.
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You need executive alignment.
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It is time to move the conversation from the server room to the boardroom.
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The executive buy-in strategy.
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The technical battle for context-aware security is being won in the code,
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but the strategic battle is still being lost in the boardroom.
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For years, identity and access management was treated like a plumbing issue.
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It was something for the basement teams to handle while the business focused on growth.
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In 2026, that mindset is a liability.
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We have to reframe the entire conversation because identity is no longer just a login.
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It is the core control plane for digital trust.
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If your leadership doesn't understand that, your AI deployment will remain a series of expensive,
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disconnected experiments.
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We need to stop pitching security as a cost center.
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Instead, we have to frame it as a decision-villacity enabler.
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Most executives view security as a series of no moments.
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No, you can't use that tool.
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No, you can't share that file.
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But a modern identity fabric does the opposite.
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It provides the yes that allows the business to move faster.
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When you have continuous context-aware verification,
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you can onboard a new partner in hours instead of weeks.
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You can give a consultant access to exactly what they need for a three-day project,
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and then you can watch that access vanish automatically the moment they finished.
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This isn't about restriction.
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It's about agility.
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It's about creating an environment where the right people can act on the right information
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without the friction of manual approvals.
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We can see the cost of ignoring this in the M&A market.
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There is a massive maturity gap emerging between organizations
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that have mastered identity governance and those that are still relying on static legacy models.
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When a high maturity firm acquires a competitor,
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they can integrate the new workforce into their AI ecosystem in days.
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But for firms with poor identity hygiene,
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that integration drags on for months,
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and this delay bleeds value while stalling the very synergies the merger was supposed to create.
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Poor identity governance is a direct hit to post-merger value.
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It is a drag on the balance sheet that most CEOs haven't even quantified yet.
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This is why the pitch for a 5-to-20% budget increase for identity fabric
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isn't a request for more securities bend.
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It's an investment in growth infrastructure.
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It's the digital equivalent of building a high-speed rail network between your data silos.
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You are moving from a hardened perimeter that everyone tries to bypass
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to a continuous verification model that actually supports the way people work.
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You are telling the C-suite that if they want the five times productivity boost that AI promises,
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they have to pay for the foundation that makes it safe.
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You have to move the goalposts from protecting the data to managing the flow of intelligence.
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The shift to a context-aware model is the only way to build a resilient enterprise
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in an age of a genetic AI.
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It allows the organization to scale its intelligence without scaling its risk.
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When the C-suite treats access as a strategic asset,
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the entire culture of the company shifts.
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Security becomes a shared responsibility,
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and data becomes a liquid resource rather than a hoarded secret.
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It's time to stop looking at the cost of the tools
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and start looking at the cost of the friction.
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Because in the AI era, the fastest company wins,
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and you can't be fast if your data is trapped in a lie.
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Your 2026 mandate is clear.
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You must audit your permission debt before you scale your AI licenses.
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The technology is ready, but your architecture likely isn't.
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The challenge for the next quarter is to implement mandatory auto labeling
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and execute a 90-day site cleanup to close the most obvious gaps.
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Stop the hooding, start the flow.
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If you want to share your silo-breaking progress or discuss how your team is handling the shift
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to context-aware permissions, connect with Mirko Peters on LinkedIn.
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Let's build an intelligence model that actually works.
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Subscribe for more deep dives into the future of work.
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Stay secure, stay productive.