The article focuses on how security operations centers (SOCs) can move beyond early AI experimentation to make artificial intelligence a genuine, operational part of their cybersecurity workflows. Many SOCs are already using AI or machine learning tools, but a large portion do so without formal integration into defined processes. As a result, AI often sits on the sidelines — analysts use tools informally with mixed reliability, and leadership hasn’t established clear models for where and how AI should fit into daily operations.
For AI to be effective in SOCs, teams need to clarify the problem they want to solve, validate AI logic, and apply rigorous review standards just as they would for engineered systems. Rather than expecting AI to magically fix broad or ill‑defined issues, its value comes when applied to specific, bounded tasks with clear success criteria. Examples include using machine learning to create high‑fidelity detection logic that flags anomalous network traffic only when it clearly deviates from normal patterns.
The article outlines several areas where AI can bolster SOC workflows: detection engineering, where AI can help define and refine alert logic; threat hunting, where AI accelerates exploratory investigation; software development and analysis, aiding in generating or refining code; automation and orchestration, where AI drafts workflow logic but doesn’t trigger actions autonomously; and reporting and communication, where AI helps standardise clear, consistent summaries of security events and metrics.
Overall, it emphasises that humans remain accountable — analysts must interpret, validate, and ground AI outputs in real expertise. AI should augment rather than replace human judgment, with clear boundaries on where its assistance adds value. SOCs should categorise their use of AI tools as “takers,” “shapers,” or “makers” depending on how they customise and build on tools, and should continuously refine AI integration so it strengthens overall security operations.
