When AI Looks First: How Agentic Systems Are Reshaping Cybersecurity Operations | A Musing On the Future of Cybersecurity and Humanity with Sean Martin and TAPE3 | Read by TAPE3

Before a power crew rolls out to check a transformer, sensors on the grid have often already flagged the problem. Before your smart dishwasher starts its cycle, it might wait for off-peak energy rates. And in the world of autonomous vehicles, lightweight systems constantly scan road conditions before a decision ever reaches the car’s central processor.

These aren’t the heroes of their respective systems. They’re the scouts, the context-builders: automated agents that make the entire operation more efficient, timely, and scalable.

Cybersecurity is beginning to follow the same path.

In an era of relentless digital noise and limited human capacity, AI agents are being deployed to look first, think fast, and flag what matters before security teams ever engage. But these aren’t the cartoonish “AI firefighters” some might suggest. They’re logical engines operating at scale: pruning data, enriching signals, simulating outcomes, and preparing workflows with precision.

"AI agents are redefining how security teams operate, especially when time and talent are limited," says Kumar Saurabh, CEO of AirMDR. "These agents do more than filter noise. They interpret signals, build context, and prepare response actions before a human ever gets involved."

This shift from reactive firefighting to proactive triage is happening across cybersecurity domains. In detection, AI agents monitor user behavior and flag anomalies in real time, often initiating mitigation actions like isolating compromised devices before escalation is needed. In prevention, they simulate attacker behaviors and pressure-test systems, flagging unseen vulnerabilities and attack paths. In response, they compile investigation-ready case files that allow human analysts to jump straight into action.

"Low-latency, on-device AI agents can operate closer to the data source, better enabling anomaly detection, threat triaging, and mitigation in milliseconds," explains Shomron Jacob, Head of Applied Machine Learning and Platform at Iterate.ai. "This not only accelerates response but also frees up human analysts to focus on complex, high-impact investigations."

Fred Wilmot, Co-Founder and CEO of Detecteam, points out that agentic systems are advancing limited expertise by amplifying professionals in multiple ways. "Large foundation models are driving faster response, greater context and more continuous optimization in places like SOC process and tools, threat hunting, detection engineering and threat intelligence operationalization," Wilmot explains. "We’re seeing the dawn of a new way to understand data, behavior and process, while optimizing how we ask the question efficiently, confirm the answer is correct and improve the next answer from the data interaction our agents just had."

Still, real-world challenges persist. Costs for tokens and computing power can quickly outstrip the immediate benefit of agentic approaches at scale. Organizations leaning on smaller, customized models may see greater returns but must invest in AI engineering practices to truly realize this advantage. "Companies have to get comfortable with the time and energy required to produce incremental gains," Wilmot adds, "but the incentive to innovate from zero to one in minutes should outweigh the cost of standing still."

Analysts at Forrester have noted that while the buzz around so-called agentic AI is real, these systems are only as effective as the context and guardrails they operate within. The power of agentic systems lies in how well they stay grounded in real data, well-defined scopes, and human oversight. ¹ ²

While approaches differ, the business case is clear. AI agents can reduce toil, speed up analysis, and extend the reach of small teams. As Saurabh observes, AI agents that handle triage and enrichment in minutes can significantly reduce investigation times and allow analysts to focus on the incidents that truly require human judgment.

As organizations wrestle with a growing attack surface and shrinking response windows, the real value of AI agents might not lie in what they replace, but in what they prepare. Rob Allen, Chief Product Officer at ThreatLocker, points out, "AI can help you detect faster. But Zero Trust stops malware before it ever runs. It’s not about guessing smarter; it’s about not having to guess at all." While AI speeds detection and response, attackers are also using AI to evade defenses, making it vital to pair smart automation with architectures that deny threats by default and only allow what’s explicitly needed.

These agents are the eyes ahead, the hands that set the table, and increasingly the reason why the real work can begin faster and smarter than ever before.

References

1. Forrester. (2024, February 8). Cybersecurity’s latest buzzword has arrived: What agentic AI is — and isn’t. Forrester Blogs. https://www.forrester.com/blogs/cybersecuritys-latest-buzzword-has-arrived-what-agentic-ai-is-and-isnt/ (cc: Allie Mellen and Rowan Curran)

2. Forrester. (2024, March 13). The battle for grounding has begun. Forrester Blogs. https://www.forrester.com/blogs/the-battle-for-grounding-has-begun/ (cc: Ted Schadler)

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This story represents the results of an interactive collaboration between Human Cognition and Artificial Intelligence.

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Sean Martin is a life-long musician and the host of the Music Evolves Podcast; a career technologist, cybersecurity professional, and host of the Redefining CyberSecurity Podcast; and is also the co-host of both the Random and Unscripted Podcast and On Location Event Coverage Podcast. These shows are all part of ITSPmagazine—which he co-founded with his good friend Marco Ciappelli, to explore and discuss topics at The Intersection of Technology, Cybersecurity, and Society.™️

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