Agentic AI Security Isn't Just A Technical Problem - It's a Strategic One

If you’ve started exploring how to secure AI agents in your environment (or even just reading about it), you likely already know that it’s not as straightforward as applying traditional AppSec practices. AI agents aren’t just another workload or API to monitor, they’re dynamic, semi-autonomous entities operating at the intersection of user intent, agent behavior, and enterprise systems.

And not all AI agents are created equal or secure.

Some agents are homegrown and deeply embedded into business processes. Others, like Microsoft 365 Copilot or Salesforce Einstein, are commercial off-the-shelf (COTS) solutions. Each demands a slightly different approach to security.

This nuance is exactly what makes agentic AI security so critical (though admittedly a bit cumbersome to understand). It’s not just about controlling access to systems; it’s about understanding what the agent can do, how it behaves, and how its actions might evolve over time. And to do that effectively security teams need to evaluate both build-time and runtime.

This is a theme echoed in Gartner’s recent research, How to Secure Custom-Built AI Agents. While their report focuses on securing homegrown agents, many principles (like access control, runtime defense, etc.) are just as critical to COTS AI agents in the enterprise.

Commercial Off-The-Shelf (COTS) AI Agents Are Not “Set and Forget”

When organizations adopt commercial AI agents like Microsoft Copilot, it’s easy to assume they’re secure by default. After all, these are enterprise-ready tools from well-known vendors.But enterprise-ready doesn’t mean risk-free. AI agents are fundamentally different from traditional software because they inherit context, permissions, and decision-making capabilities. Though you can leverage these platforms to create your own agents (not building them with traditional development), once they’re deployed in your environment they all still:

• Act on behalf of users
• Access enterprise data and systems
• Use dynamic inputs that can’t always be predicted
• Interact with unstructured content like documents, emails, and chats
• Learn (and sometimes remember) from past interactions

This means your enterprise has to consider not just what the agent can see, but what it can do. That lens changes how we think about both access controls, build time protections, and runtime enforcement.

Why Build-Time and Runtime Security Work Better Together

Securing AI agent build-time focuses on enforcing guardrails at the point of configuration, integration, and deployment. This is where AI Security Posture Management (AISPM) comes into play. By aligning policies with established frameworks organizations can define what “safe and compliant” looks like before the agent ever takes action. Think of things like identity scoping, prompt hygiene rules, data access restrictions, amongst other things - all governed by clear, enforceable policy. When done right, built-time becomes the foundation for AI adoption at scale, allowing organizations to move fast without losing control.

Now enter runtime security.

In order to truly have solid AI agent security in place, you need to address the dynamic nature of AI agents with runtime visibility and enforcement. Inspection and enforcement ensure AI agents don’t just behave securely in theory, but in practice. This includes detecting and responding to prompt injection attempts (both direct and indirect), privilege escalations, hidden instructions, and least privilege violations. When runtime insights are mapped across the attack chain, from recon to exfiltration, security teams can pinpoint threats whether they stem from external attackers, trusted insiders, or an overly curious AI agent.

In short, visibility without action isn’t enough. AI agent security requires the ability to monitor, understand, and action quickly.

Why “Agency” Should be a Security Priority

One of the trickiest aspects of securing AI agents is understanding their agency, their ability to make decisions and take action. Unlike static apps, AI agents can interpret instructions, reference previous interactions, and even execute on processes with multiple steps. That opens the door to new risks, especially if they have been targeted by an attacker.

In addition to the buildtime and runtime security measures, security teams need the ability to profile each AI agent, understanding its identity, access patterns, data exposure, connections, and behavior baseline. This type of profiling enables anomaly detection that’s deeply contextual, helping distinguish between normal automation and risky deviations.

Don’t Wait for AI Agents to Backfire

AI agents are already operating inside of your environment - most likely anyway. They’re not waiting for your security strategy to catch up.

But the good news is that you don’t need to boil the ocean. Start with visibility. Get clarity on who’s using agents, how they are being triggered, and what permissions they have. From here builds a strategy that brings together build-time and runtime security.