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As artificial intelligence (AI) rapidly enters the realm of security operations centers (SOCs), organizations are grappling with the challenge of moving beyond early experimentation toward consistent, measurable impact. While AI promises to enhance detection, automation, threat hunting, and reporting, many SOCs are using it without a structured approach, leading to inconsistent results. The gap between potential and reality highlights the importance of intentional integration and clearly defined workflows.
AI in SOCs: Current Landscape
According to the 2025 SANS SOC Survey, AI adoption is widespread but often superficial. Around 40% of SOCs use AI or machine learning (ML) tools without fully integrating them into operational workflows. Similarly, 42% rely on out-of-the-box AI tools with no customization. Analysts experiment with AI informally, but leadership rarely establishes frameworks for validation, accountability, or workflow readiness. The result is a SOC where AI exists but isn’t fully operationalized.
AI has the potential to enhance SOC capabilities, improve process repeatability, increase staff efficiency, and boost analyst satisfaction—but only when applied to well-defined tasks with rigor. Success comes from narrowing problem scope, validating logic, and applying the same standards used in engineering practices. Instead of creating entirely new processes, AI should refine existing ones, enabling better testing, development, and expansion of capabilities.
Five Practical AI Applications in SOCs
1. Detection Engineering
Detection engineering requires precise logic for alerts in SIEM or MDR pipelines. AI is most effective here when applied to narrow, clearly defined tasks. For instance, machine learning models can analyze the first eight bytes of a packet stream to classify DNS traffic. When reconstruction doesn’t match expected patterns, high-fidelity alerts are triggered. The precision of AI here adds value, but it cannot compensate for vague problem definitions or poor engineering discipline.
2. Threat Hunting
Threat hunting is exploratory, research-driven work rather than production detection. AI accelerates early-stage analysis, helps generate candidate hypotheses, and identifies unusual patterns. Analysts remain responsible for interpretation and operational decisions, while AI serves as a speed and breadth enhancer rather than an authority. Proper use requires careful consideration of operational security and controlled data exposure.
3. Software Development and Analysis
SOCs rely heavily on scripting, automation, and code-based tools. AI can help draft code, refine scripts, and accelerate logic creation in Python, PowerShell, or SIEM queries. However, analysts must validate AI outputs; mistakes can arise if generated code is used without full understanding or testing. AI reduces mechanical overhead but doesn’t replace domain expertise. Teams should implement style guidelines, approved libraries, and dependency checks for AI-assisted development.
4. Automation and Orchestration
AI can streamline workflow design by drafting scaffolds, branching logic, and translating plain-language instructions into structured automation sequences. But humans must decide execution timing based on risk tolerance, sensitivity, and operational context. AI enhances workflow design but does not determine operational actions. Effective orchestration balances AI-generated logic with human oversight, ensuring predictable, explainable automation.
5. Reporting and Communication
Consistent reporting is essential yet remains largely manual—69% of SOCs still depend on mostly manual reporting processes. AI improves report clarity, structure, and comparability, allowing leadership to quickly interpret metrics and trends. Analysts save time previously spent formatting or rewriting outputs, enabling a focus on incident analysis rather than presentation. Standardized reporting enhances situational awareness and decision-making.
AI Adoption Models in SOCs
SOC AI adoption can be categorized as taker, shaper, or maker. Takers use tools as delivered; shapers customize tools for workflows; makers build new AI-driven solutions. Teams often combine these approaches across different tasks. Clear expectations, output validation, ongoing updates, and analyst accountability are critical regardless of the adoption model.
What Undercode Say:
The use of AI in SOCs is at a pivotal stage where promise often outpaces execution. One of the central challenges is human expectation versus AI capability. Analysts and leadership frequently assume AI can compensate for poor process definition or insufficient operational rigor, which rarely produces meaningful results. The 2025 SANS SOC Survey underlines that most organizations are still experimenting without embedding AI into repeatable workflows—a gap that risks wasted investment and analyst fatigue.
True operational value emerges when AI is applied to narrowly defined, measurable tasks. The DNS packet analysis example illustrates how AI excels when it is constrained by precise criteria, trained on relevant data, and coupled with systematic validation. This micro-level approach can be scaled to other operational tasks, provided that teams maintain a disciplined review process.
In threat hunting, AI’s role is exploratory, not decisive. By accelerating hypothesis testing and pattern comparison, it enhances the breadth of analyst insight but does not replace judgment. Organizations that treat AI as a research assistant rather than a final arbiter will gain the most from this technology.
Similarly, in software development, AI serves best as a drafting and refinement tool. It reduces repetitive workload but does not replace the expertise required to validate and operationalize outputs. SOCs that formalize coding standards, enforce dependency management, and maintain rigorous validation pipelines mitigate risk while benefiting from AI-assisted efficiency.
Automation and orchestration provide another lens. AI can design workflows and suggest logic sequences, but execution must remain human-controlled. Clear operational boundaries are crucial; otherwise, errors in automated processes can propagate rapidly across sensitive systems. Effective integration requires extensive testing, iterative feedback, and analyst responsiveness.
Reporting is a particularly underappreciated area where AI provides immediate operational uplift. Standardized, consistent reporting enables leadership to identify trends quickly, supports data-driven decision-making, and allows analysts to focus on actionable intelligence. Here, AI adds tangible value without introducing operational risk.
The “taker, shaper, maker” framework offers practical guidance for adoption strategy. Teams should aim to evolve from passive consumption of AI tools toward internal innovation, gradually building workflows and logic that reflect organizational context. The human-in-the-loop model ensures accountability, validates AI outputs, and safeguards operational integrity.
In essence, AI is not a panacea; it is a powerful amplifier of disciplined, well-defined security operations. Organizations that fail to define clear objectives, enforce validation, or provide human oversight risk underutilizing or misusing AI. Conversely, those that pair AI with precise tasks, controlled experimentation, and robust analyst engagement can elevate operational maturity, efficiency, and insight.
Fact Checker Results:
✅ 40% of SOCs use AI/ML tools without full operational integration.
✅ 42% rely on AI/ML out-of-the-box with no customization.
❌ AI alone cannot replace analyst judgment or poorly defined workflows.
Prediction:
AI adoption in SOCs will shift toward disciplined, narrow-scope use cases over the next three years. Organizations that combine human oversight with AI-driven efficiency will see measurable gains in detection accuracy, operational throughput, and analyst satisfaction. Conversely, SOCs that treat AI as a plug-and-play solution risk wasted resources and inconsistent security outcomes. 🚀
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