Industry Domain

Scaling GenAI Agents? Here’s How to Keep Security in Control

By Gaurav Agarwaal

Published January 27, 2026

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The Hardest Question in AI Security Right Now Isn’t About the Model As enterprises rush to adopt GenAI agents, most security conversations start with: “Is the model safe?” But that’s not the most urgent risk. The better question is: “What systems can this agent reach—and what sensitive data might it move, misuse, or expose at scale?” Because agents don’t just analyze. They act. They fetch documents, send messages, interface with external tools, and sometimes cascade tasks across workflows. And if left unchecked, they can quietly bypass the same controls we’ve spent years building for users and applications. That’s why the evolution of security is now focused not on the model—but on the agent layer. The Shift We’re Seeing: Agents Multiply, Oversight Struggles to Keep Up Today, AI agents are spun up by developers, operations teams, and even business users. Some are embedded into products. Others are customized in no-code platforms or integrated across clouds. And for many organizations, this speed outpaces traditional security models. The result? Agents that operate without clear governance, access sensitive systems autonomously, and introduce new risk surfaces—often without visibility from security teams. This isn’t just a tech problem. It’s a governance failure waiting to happen. What Microsoft’s New Purview Rollout Signals Loud and Clear With its latest enhancements, Microsoft is essentially saying: “If agents are going to act like users, it’s time we govern them like users.” The Purview platform now extends its data security and compliance capabilities to AI agents—across first-party, third-party, and custom-built deployments. This isn’t just about enforcement. It’s about giving organizations a unified control plane to see, assess, and manage risk in a world of autonomous agents. Here are the five shifts every enterprise security leader should understand: 1️⃣ Agent Inventory Becomes Non-Negotiable The new capabilities offer visibility into which agents exist, what data they access, and how risky they are—especially across Microsoft 365 and Foundry environments. 📌 Why it matters: You can’t protect what you can’t see. Observability is the entry point to any meaningful policy. 2️⃣ Agent Behavior Gets Its Own Risk Analytics Purview’s Insider Risk tools now include logic to flag risky agent behavior—like abnormal access patterns, excessive data movement, or misuse of sensitive content. 📌 Why it matters: Agents don’t follow user norms. Your detection systems must understand autonomy—not just identity. 3️⃣ The agent, not just the file, protects the data. DLP and sensitivity labels now cover agent actions, stopping unauthorized access or sharing, even when an agent, not a person, starts the action. 📌 Why it matters: This fills in a very important gap and stops AI-generated oversharing from becoming a major problem. 4️⃣ Compliance and Audit Grow to Include Interactions with Agents Organizations can now keep track of, audit, and find agent interactions in the same way that they do with employee communications. This includes messages sent by agents, responses based on RAG, and autonomous retrievals. 📌 Why it matters: You can&#8217;t be held accountable if you don&#8217;t keep records. And that&#8217;s a deal-breaker in places where there are rules. 5️⃣ Controls Extend Beyond Microsoft’s Ecosystem Microsoft has added protection for agent interactions even outside its ecosystem—through browser-based DLP and SDKs for embedding governance into custom agents. 📌 Why it matters: No enterprise runs a single-stack AI environment. Controls must move with the data—not stay tied to the platform. From Policy to Practice: How I’d Operationalize This Shift For any organization deploying GenAI agents, here’s the operating model I’d recommend: Start with a full agent inventory. Make it a requirement before scale. No unknown agents. No unmanaged behavior. Treat agent identities and permissions like privileged accounts. Every action must be mapped, monitored, and scoped by design. Bring DLP to the boundary. Don’t just secure storage. Secure prompts, responses, and agent-to-agent flows. Make audit trails part of your AI design. If you can’t prove who did what, when, and why—you’re exposed. Integrate label-aware retrieval in all RAG scenarios. Retrieval is the fastest path to data leakage. Label enforcement is your last line of defense. Closing Thought: Trust in Agents Starts with Discipline in Design The rise of AI agents introduces a new layer of value—and a new class of risk. And the hardest incidents won’t come from malicious intent. They’ll come from unmonitored actions, silent oversharing, and controls that weren’t designed to scale. Microsoft’s update to Purview isn’t just a product move. It’s a sign of where enterprise security must go next: toward systems that treat agents not as novelties, but as operational entities that must be governed, logged, and trusted—by design. Because if agents are now acting on your behalf—your security model better be ready to act on theirs.

The Hardest Question in AI Security Right Now Isn’t About the Model

As enterprises rush to adopt GenAI agents, most security conversations start with: “Is the model safe?”

But that’s not the most urgent risk.

The better question is: “What systems can this agent reach—and what sensitive data might it move, misuse, or expose at scale?”

Because agents don’t just analyze. They act. They fetch documents, send messages, interface with external tools, and sometimes cascade tasks across workflows. And if left unchecked, they can quietly bypass the same controls we’ve spent years building for users and applications.

That’s why the evolution of security is now focused not on the model—but on the agent layer.

The Shift We’re Seeing: Agents Multiply, Oversight Struggles to Keep Up

Today, AI agents are spun up by developers, operations teams, and even business users. Some are embedded into products. Others are customized in no-code platforms or integrated across clouds.

And for many organizations, this speed outpaces traditional security models.

The result?

Agents that operate without clear governance, access sensitive systems autonomously, and introduce new risk surfaces—often without visibility from security teams.

This isn’t just a tech problem. It’s a governance failure waiting to happen.

What Microsoft’s New Purview Rollout Signals Loud and Clear

With its latest enhancements, Microsoft is essentially saying: “If agents are going to act like users, it’s time we govern them like users.”

The Purview platform now extends its data security and compliance capabilities to AI agents—across first-party, third-party, and custom-built deployments. This isn’t just about enforcement. It’s about giving organizations a unified control plane to see, assess, and manage risk in a world of autonomous agents.

Here are the five shifts every enterprise security leader should understand:

1️⃣ Agent Inventory Becomes Non-Negotiable

The new capabilities offer visibility into which agents exist, what data they access, and how risky they are—especially across Microsoft 365 and Foundry environments.

📌 Why it matters: You can’t protect what you can’t see. Observability is the entry point to any meaningful policy.

2️⃣ Agent Behavior Gets Its Own Risk Analytics

Purview’s Insider Risk tools now include logic to flag risky agent behavior—like abnormal access patterns, excessive data movement, or misuse of sensitive content.

📌 Why it matters: Agents don’t follow user norms. Your detection systems must understand autonomy—not just identity.

3️⃣ The agent, not just the file, protects the data.

DLP and sensitivity labels now cover agent actions, stopping unauthorized access or sharing, even when an agent, not a person, starts the action.

📌 Why it matters:

This fills in a very important gap and stops AI-generated oversharing from becoming a major problem.

4️⃣ Compliance and Audit Grow to Include Interactions with Agents

Organizations can now keep track of, audit, and find agent interactions in the same way that they do with employee communications. This includes messages sent by agents, responses based on RAG, and autonomous retrievals.

📌 Why it matters:

You can’t be held accountable if you don’t keep records. And that’s a deal-breaker in places where there are rules.

5️⃣ Controls Extend Beyond Microsoft’s Ecosystem

Microsoft has added protection for agent interactions even outside its ecosystem—through browser-based DLP and SDKs for embedding governance into custom agents.

📌 Why it matters: No enterprise runs a single-stack AI environment. Controls must move with the data—not stay tied to the platform.

From Policy to Practice: How I’d Operationalize This Shift

For any organization deploying GenAI agents, here’s the operating model I’d recommend:

Start with a full agent inventory. Make it a requirement before scale. No unknown agents. No unmanaged behavior.
Treat agent identities and permissions like privileged accounts. Every action must be mapped, monitored, and scoped by design.
Bring DLP to the boundary. Don’t just secure storage. Secure prompts, responses, and agent-to-agent flows.
Make audit trails part of your AI design. If you can’t prove who did what, when, and why—you’re exposed.
Integrate label-aware retrieval in all RAG scenarios. Retrieval is the fastest path to data leakage. Label enforcement is your last line of defense.

Closing Thought: Trust in Agents Starts with Discipline in Design

The rise of AI agents introduces a new layer of value—and a new class of risk. And the hardest incidents won’t come from malicious intent. They’ll come from unmonitored actions, silent oversharing, and controls that weren’t designed to scale.

Microsoft’s update to Purview isn’t just a product move. It’s a sign of where enterprise security must go next: toward systems that treat agents not as novelties, but as operational entities that must be governed, logged, and trusted—by design.

Because if agents are now acting on your behalf—your security model better be ready to act on theirs.