The Agentic SOC – From AI Theater to Real Defense

  • Moving beyond "AI theater" with measurable KPIs: Security teams must distinguish between genuine value and "productivity theater." Success requires defining concrete KPIs—such as cost improvement, risk reduction, and speed—to measure true ROI, rather than deploying AI tools without a clear strategic purpose.
  • Mitigate new autonomous risks: The shift to an agentic SOC introduces distinct threats, such as indirect prompt injection, and creates visibility gaps that traditional SIEM platforms are not built to handle. Organizations should shift from post-event observability to proactive control mechanisms, such as placing strict constraints on agent compute and communication.
  • Redefine the analyst’s role for speed at scale: As defensive timelines compress from days to seconds, the fundamental unit of work will evolve from alert handling to agent management. The human role is shifting from a manual processor to an architect, responsible for setting objectives, defining operational constraints, and overseeing the behavior of AI agents.

For security teams, AI has generated both more excitement and more confusion than any technology in the last decade. As threat actors experiment with AI to hone their attacks, defenders are trying to determine which AI investments will help them measurably reduce risk.

Matthew Farmer, Accenture’s Managing Director of Security Operations in EMEA, joined Recorded Future’s co-founder Christopher Ahlberg and CTO and co-founder Staffan Truvé in a recent discussion to discuss the agentic SOC and what it takes to move from “AI theater” to real defense. Read on to see the key highlights from the discussion.

Avoiding the "productivity theater" trap

While AI is demonstrably transforming investigation and decision-making layers in SecOps, there’s a significant risk that organizations are falling into what Farmer calls "AI productivity theater."

"We can all agree that there's great production value around a lot of AI capabilities and AI products," he said. "But there are also organizations that are really struggling to achieve any kind of return on investment on their AI.”

The panel noted that the difference between success and failure doesn’t necessarily have anything to do with being in a regulated or non-regulated industry. It’s more about the ability to move past the theater by defining concrete KPIs.

“A lot of what people want to achieve with AI, we can already achieve with existing machine learning or SOAR automation capabilities,” Farmer said. So rather than simply deploying an AI solution for the sake of being AI-enabled, organizations need to ask whether they’re solving for cost improvement, risk reduction, or speed. They need to understand their KPIs so they can measure their true ROI.

When it comes to bringing new AI solutions online, the panel noted that SOCs often face administrative, legal, and compliance limitations that eclipse any technical hurdles.

They also agreed that data quality and lack of context — “two sides of the same coin” according to Truvé — remain fundamental challenges.

Farmer noted that, “In the new world of tokenomics, it costs just as much money to troll through poor quality data as high-quality data.” It’s essential that security organizations feed only the best intelligence into their AI tools.

Assessing new risks, from democratization to agentic threats

Farmer said that security organizations used to ask a key question: “Do those [threat actors] with the capability have the motive, and do those with the motive have the capability?” We’re now in a world where non-capable threat actors can use AI to capably launch highly sophisticated attacks.

Threats are also becoming more structural. The panel highlighted "indirect prompt injection"—where agents are manipulated by the very instructions they read—as a new, distinct threat vector.

As companies deploy a digital workforce of AI agents, they should consider applying the same security principles of permissions, monitoring, and accountability to agents that they do to humans. But that may not be sufficient. "One big difference [between an agent and a human] is that an agent can spawn off a thousand clones of itself," Truvé said.

A critical challenge facing security teams is that the current observability space of SIEMs and traditional monitoring platforms isn’t built to track the internal state of an LLM.

"You can observe what ports they talk on, you can write that to a SIEM,” Ahlberg said. “But you’re not observing what’s happening inside the LLM.”

The panelists suggested that rather than relying solely on post-event observability, security teams should rethink how they control agents. Instead of setting up easily bypassed guardrails, security teams need to be better at constraining what each agent can do and ask for.

“You could imagine giving them a budget in terms of compute, communication, and delegation,” said Truvé. “These things run too fast. When you’re observing it, it’s already going to be too late.”

Preparing for the move to autonomous defense

According to the panelists, the shift toward autonomous defense is inevitable. "We can choose to go early, or we can choose to go late,” Farmer said. “But I think the decision is made for us."

However, it doesn’t need to take years to begin realizing big benefits from AI. To do so, security organizations should consider:

On that last point, Farmer said he thinks that as teams grow more resilient, they develop a better appetite for deploying automated solutions — and that in turn strengthens their overall security posture.

The future of defense: Intelligence and speed at scale

According to the panel, the most profound change moving forward won’t just be the technology—it’ll be the velocity coupled with intelligence required for defense. "In three years, the main difference will be speed," Truvé predicted. "Defensive timelines will compress from days to minutes or seconds."

Ensuring security will require organizations to move past traditional constraints as they simply won't have time to manually ingest, analyze, and move intelligence. Taking detection engineering as an example, Farmer noted, “If we have to deliver more detection rules faster, we have to break that linear model between volume, speed, and headcount.” Consequently, SOCs will rely increasingly on high-quality, timely intelligence to enable rapid, automated decision-making.

As this shift occurs, the fundamental unit of work for a security analyst will evolve from handling individual alerts to managing and overseeing the agents that process them. In this new era, the human will remain essential—not as a manual processor of alerts, but as the architect who sets objectives, defines constraints, and monitors the behavior of the agents defending the enterprise.

Watch the full webinar here.

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