4 ways to prepare your SOC for agentic AI

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Mature SOCs will also require clear ownership of AI governance and agent oversight. That includes roles that have oversight over model risk evaluation, prompt and policy management, continuous performance validation, and even red teaming the agents themselves, Seker says. “You don’t need a massive new team, but you do need clear accountability for how autonomous decisions are made, tested, and constrained.”

Another emerging need is analysts with deep fluency in data management. An AI-driven SOC will require professionals who understand how information should be classified, protected, normalized, and monitored to ensure reliable conclusions. “With 64% of organizations planning to add AI-powered solutions to their security stack in the next year, it is critical for professionals to cross-skill in AI,” Carignan says. “Cybersecurity professionals must become fluent in AI and data, developing a deeper understanding of data classification, governance, and model behavior.” Cross-skills in data science, machine learning, and cybersecurity enable analysts to critically evaluate AI outputs, tune models for security use cases, and adapt defenses as threats evolve, making them indispensable in an AI-augmented SOC.

Frank Dickson, an analyst at IDC, urged organizations to think of this capability as similar to a data architect role. “The key to getting value from AI is having data located in a place where you can get to it, having it formatted in a homogeneous way so you can do analysis on it, and then manage the data,” he says. “The success of your AI initiative is going to be tied to the effectiveness of your ability to get data. A data architect manages that.”

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