Look, you’re probably already interacting with AI agents, even if you don’t call them that. They’re the smart tools helping coders write faster, assistants managing your inbox, and automations whispering through your company’s backend. This isn’t just another software update; it’s a platform shift, akin to the internet itself or the dawn of cloud computing. And right now, the people tasked with keeping your digital world safe? They’re mostly playing catch-up.
That’s the stark reality hitting home with the rise of agentic AI. These aren’t passive tools; they’re autonomous actors, executing tasks, sifting through data, and making decisions—often with minimal oversight from the security teams. The industry’s default reaction has been to frame it as a simple policy problem: “Should we allow it? Restrict it? Monitor it?” But that misses the forest for the trees. The real, gnawing question is: do security professionals actually grasp what they’re dealing with?
And the answer, disturbingly, is mostly no.
This isn’t about blame; it’s about a fundamental truth in cybersecurity: You cannot secure what you do not understand. It’s as basic as trying to fix a leaky faucet without knowing how plumbing works. Remember when the cloud first exploded? Organizations that just slapped old security policies onto new, unfamiliar infrastructure ended up with digital ghost towns they couldn’t control. Cloud security became its own discipline because the tech demanded it. AI is doing the same thing, but on hyperdrive.
The immediate consequence? Business units, eager to use AI’s power, are often bypassing security teams. Not out of malice, but because a security team that can’t speak the language of AI engineering—can’t challenge designs, propose sensible controls, or even ask the right questions—simply isn’t a useful partner. This is the same dance we’ve seen with every major tech wave for decades. AI won’t be the exception.
The Three Flavors of Agentic Risk
The agentic AI world isn’t a monolith. It’s a spectrum, and the risks vary wildly. It’s worth dissecting these into three main categories:
First up are the general-purpose coding and productivity agents. Think GitHub Copilot or Claude Code. These are already woven into the fabric of your engineering workflows, approved or not. Understanding what data they can slurp up and how they interact with your sensitive codebases is now table stakes for security.
Then there are vendor-built agents powered by something called the Model Context Protocol (MCP). This is the crucial integration layer letting agents talk to external services. Nearly every major vendor is either deploying this or building it out. In practice, this means an agent managing your calendar or email can receive instructions from them and act. A cleverly disguised calendar invite, laden with hidden commands in its description? That’s a very real attack vector an agent could easily execute. This is a live attack surface crying out for deliberate configuration and rigorous security review.
Finally, and perhaps most fascinatingly, are custom agents built by individual users. For years, there was a moat between security pros and the code running in production. Most security folks aren’t deep programmers. Building custom tools was a barrier. That moat? It’s gone. With agentic AI, anyone in your organization can build functional tools—automations, workflows, agents with real system access—without writing a line of traditional code. This is undeniably powerful for security teams, accelerating everything from incident investigation to threat hunting. But that same power extends everywhere: marketing, finance, operations. Everyone can build agents. And most of those agents will likely go live before any security review. It’s a supply chain problem, just dressed in a new AI suit.
Why Security Teams Are Falling Behind
When security lags on a new tech frontier, the pattern is depressingly familiar. Initially, the business charges ahead. Developers deploy, departments adopt, and security is an afterthought, if consulted at all. Then, the vulnerabilities begin to stack up. The more powerful these agents become, the more access they need—broad permissions are what make them useful, after all. Access to calendars, communication platforms, internal databases—the juicy stuff.
The practical consequence of being behind on agentic AI goes beyond technical exposure. Security teams that cannot speak the language of AI engineering—that cannot challenge design decisions, propose workable controls, or ask informed questions—get bypassed.
This creates a dangerous feedback loop. As exposure grows, so does the potential for exploitation. And without a deep, hands-on understanding, security teams are left with blunt instruments trying to manage incredibly sophisticated, rapidly evolving threats. Building an agent yourself, tinkering with the tools your developers are already using—that’s where genuine understanding begins. And that understanding is the bedrock upon which effective defense is built.
This isn’t about halting progress; it’s about ensuring that progress doesn’t come at the cost of catastrophic security failures. It’s about empowering security to be a proactive partner, not a reactive speed bump. The future isn’t waiting for security to catch up. It’s here, and it’s building agents.
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Frequently Asked Questions
What is an agentic AI? An agentic AI is a type of artificial intelligence system designed to autonomously perceive its environment, make decisions, and take actions to achieve specific goals. Unlike traditional AI models that primarily process information, agentic AI can interact with the real world or digital systems.
How does agentic AI pose a security risk? Agentic AI can pose security risks due to their ability to execute actions with broad permissions, potential for misconfiguration, susceptibility to prompt injection attacks, and the lack of security oversight in their development and deployment, especially with custom-built agents.
Why are security teams struggling to keep up with agentic AI? Security teams often lag behind because they lack the deep, hands-on understanding of agentic AI technologies required to develop effective defense strategies. The rapid pace of AI development and its integration into business workflows, coupled with the ease of building custom agents, outpaces traditional security training and adoption cycles.
