Artificial intelligence (AI) is making its way into security operations quickly, but many practitioners are still struggling to turn early experimentation into consistent operational value. This is because SOCs are adopting AI without an intentional approach to operational integration. Some teams treat it as a shortcut for broken processes. Others attempt to apply machine learning to problems that are not well defined.
Findings from our 2025 SANS SOC Survey reinforce that disconnect. A significant portion of organizations are already experimenting with AI, yet 40 percent of SOCs use AI or ML tools without making them a defined part of operations, and 42 percent rely on AI/ML tools “out of the box” with no customization at all. The result is a familiar pattern. AI is present inside the SOC but not operationalized. Analysts use it informally, often with mixed reliability, while leadership has not yet established a consistent model for where AI belongs, how its output should be validated, or which workflows are mature enough to benefit from augmentation.
AI can realistically improve SOC capability, maturity, process repeatability, as well as staff capacity and satisfaction. It only works when teams narrow the scope of the problem, validate their logic, and treat the output with the same rigor they expect from any engineering effort. The opportunity isn’t in creating new categories of work, but in refining the ones that already exist and enabling testing, development, and experimentation for expansion of existing capabilities. When AI is applied to a specific, well-bounded task and paired with a clear review process, its impact becomes both more predictable and more useful.
Here are five areas where AI can provide reliable support for your SOC.
1. Detection Engineering
Detection engineering is fundamentally about building a high-quality alert that can be placed into a SIEM, an MDR pipeline, or another operational system. To be viable, the logic needs to be developed, tested, refined, and operationalized with a level of confidence that leaves little room for ambiguity. This is where AI tends to be ineffectively applied.
Unless it’s the targeted outcome, don’t assume AI will fix deficiencies in DevSecOps or resolve issues in the alerting pipeline. AI can be useful when applied to a well-defined problem that can support ongoing operational validation and tuning. One clear example from the SANS SEC595: Applied Data Science and AI/ML for Cybersecurity course is a machine learning exercise that examines the first eight bytes of a packet’s stream to determine whether traffic reconstructs as DNS. If the reconstruction does not match anything previously seen for DNS, the system raises a high-fidelity alert. The value comes from the precision of the task and the quality of the training process, not from broad automation. The anticipated implementation is to inspect all flows on UDP/53 (and TCP/53) and assess the reconstruction loss from a machine…
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