Unlocking AI’s True Potential in Modern SOC Workflows
Artificial intelligence (AI) is rapidly becoming an indispensable tool within security operations centers (SOCs) worldwide. Yet, despite widespread experimentation, many organizations grapple with transforming early AI initiatives into tangible, consistent operational value. This struggle often stems from a fundamental oversight: adopting AI without a deliberate, integrated approach to its operationalization.
Too often, AI is mistakenly viewed as a quick fix for existing broken processes or applied to ill-defined problems. The findings from the 2025 SANS SOC Survey underscore this critical disconnect. While a significant portion of organizations are indeed experimenting with AI, a staggering 40 percent of SOCs utilize AI or machine learning (ML) tools without formally integrating them into their defined operations. Furthermore, 42 percent rely on these tools “out of the box” with no customization whatsoever.
The predictable outcome is a familiar pattern: AI exists within the SOC, but it remains largely unoperationalized. Analysts might use it informally, often with inconsistent reliability, while leadership has yet to establish a clear framework for AI’s role, how its output should be validated, or which workflows are mature enough to truly benefit from its augmentation.
Strategic Integration: The Path to Real Value
When approached strategically, AI can profoundly enhance SOC capabilities, elevate maturity levels, improve process repeatability, and significantly boost staff capacity and satisfaction. The key lies in a disciplined approach: narrowing the scope of the problem, rigorously validating the underlying logic, and treating AI’s output with the same engineering rigor expected from any critical system.
The true opportunity isn’t in inventing entirely new categories of work, but rather in refining existing ones. AI excels when it enables robust testing, development, and experimentation, allowing for the expansion and enhancement of current capabilities. When AI is applied to a specific, well-bounded task and paired with a clear, defined review process, its impact becomes both more predictable and demonstrably useful.
Here are two critical areas where AI can provide reliable, impactful support for your SOC:
1. Precision in Detection Engineering
Detection engineering is the art and science of crafting high-quality alerts for deployment into SIEMs, MDR pipelines, or other operational systems. For these alerts to be viable, their logic must be meticulously developed, tested, refined, and operationalized with an unwavering level of confidence. This is precisely where AI is often misapplied.
It’s crucial not to assume AI will magically rectify deficiencies in DevSecOps practices or resolve inherent issues within the alerting pipeline, unless that is the precisely targeted outcome. AI’s utility shines brightest when applied to a well-defined problem that supports continuous operational validation and tuning.
Consider a compelling example from the SANS SEC595: Applied Data Science and AI/ML for Cybersecurity course: a machine learning exercise designed to analyze the first eight bytes of a packet’s stream to determine if the traffic reconstructs as DNS. If the reconstruction deviates from established DNS patterns, the system generates a high-fidelity alert. The immense value here stems from the task’s precision and the quality of the training process, not from broad, untargeted automation.
The envisioned implementation involves inspecting all flows on UDP/53 (and TCP/53) and assessing the reconstruction loss using a machine learning-tuned autoencoder. Streams that violate predefined thresholds are flagged as anomalous. This granular, AI-engineered detection demonstrates a truly implementable solution. By focusing on the first eight bytes and comparing them against learned DNS patterns, we create a clear, testable classification problem. When these bytes don’t conform to typical DNS structures, an alert is triggered. AI proves effective here due to its narrow scope and objective evaluation criteria. It can often outperform heuristic, rule-driven detections by learning to accurately encode and decode what is “familiar.” Anything unfamiliar (in this context, non-DNS traffic masquerading as DNS) cannot be properly encoded or decoded.
What AI cannot do, however, is compensate for vaguely defined alerting problems or substitute for a missing engineering discipline.
2. Empowering Threat Hunting Expeditions
Threat hunting is frequently, and somewhat inaccurately, portrayed as a domain where AI might autonomously “discover” threats. This perspective fundamentally misunderstands the workflow’s true purpose. Hunting is not production detection engineering; rather, it serves as the research and development arm of the SOC. It’s where analysts explore nascent ideas, test hypotheses, and evaluate signals that aren’t yet robust enough for an operationalized detection.
This exploratory capability is vital because the vulnerability and threat landscape is in constant flux. Security operations must continuously adapt to the inherent volatility and uncertainty of the information assurance universe. AI fits seamlessly into this context precisely because the work is exploratory.
Analysts can leverage AI to pilot new approaches, compare intricate patterns, or quickly assess whether a particular hypothesis warrants deeper investigation. It significantly accelerates the early stages of analysis, but it does not, and should not, dictate what ultimately matters. The AI model functions as a powerful tool, not the ultimate authority.
Crucially, hunting directly feeds into detection engineering. AI can assist in generating candidate logic or highlighting unusual patterns, but the ultimate responsibility for interpreting the environment and deciding the significance of a signal rests squarely with human analysts. If they cannot effectively evaluate AI output or explain why a certain pattern is anomalous, the value of the AI is diminished.
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