Why AI in SOCs Is Stalling and How to Make It Actually Work
Artificial intelligence is rapidly entering security operations, yet many Security Operations Centers (SOCs) are still struggling to convert early AI experiments into real, repeatable value. The issue is not a lack of tools it’s the absence of a clear operational strategy.
Too often, AI is introduced as a quick fix for broken workflows or applied to problems that are not clearly defined. Instead of strengthening operations, it ends up existing on the sidelines: present, but not trusted, measured, or fully integrated.
Recent insights from the 2025 SANS SOC Survey highlight this disconnect. While many organizations have begun experimenting with AI, 40% of SOCs use AI or machine learning without formally embedding it into operations, and 42% rely entirely on out-of-the-box AI tools with no customization. The result is predictable. Analysts use AI inconsistently, leadership lacks a validation framework, and AI outputs are treated as optional rather than operationally reliable.
AI can improve SOC maturity, analyst efficiency, and process consistency but only when it is applied deliberately, scoped carefully, and reviewed with engineering level discipline. The real opportunity lies not in inventing new workflows, but in enhancing the ones that already exist.
Below are five SOC functions where AI can deliver dependable, measurable value when used correctly.
1. Precision Over Promises: AI in Detection Development
Detection engineering is about creating alerts that are accurate, testable, and reliable enough to be deployed in production systems such as SIEMs or MDR pipelines. This discipline demands clarity, repeatability, and confidence areas where AI is often misused.
AI should not be expected to compensate for weak alert pipelines or poor DevSecOps practices. Where it does work well is in narrowly scoped, well-defined problems that can be continuously validated.
A strong example comes from applied security data science training, where machine learning models analyze the initial bytes of network traffic to determine whether it behaves like legitimate DNS. When traffic reconstruction deviates from learned DNS patterns, the system raises a high-confidence alert. The effectiveness here comes from tight scope, clean data, and objective evaluation not broad automation.
This type of detection is successful because it is precise and testable. AI excels when it learns what “normal” looks like and flags deviations. What it cannot do is magically fix vague requirements or replace disciplined engineering.
2. Exploration, Not Automation: AI’s Role in Threat Hunting
Threat hunting is often misunderstood as a function where AI should autonomously uncover hidden attacks. In reality, hunting is a research-driven activity an R&D function of the SOC focused on testing ideas, exploring weak signals, and adapting to a constantly evolving threat landscape.
This makes it an ideal space for AI-assisted exploration. Analysts can use AI to accelerate hypothesis testing, surface unusual patterns, or assess whether a lead is worth deeper investigation. AI speeds up discovery, but it does not define what is important.
Importantly, threat hunting feeds detection engineering. AI may suggest candidate signals, but analysts must interpret the results and decide whether they are meaningful. If a team cannot explain why a pattern matters, the hunt has failed regardless of how advanced the model appears.
Care must also be taken to protect sensitive data. Hunting inputs should only be shared with authorized and approved systems, whether AI-driven or otherwise.
3. Accelerating Code—Without Losing Control
Modern SOCs are powered by code. From Python automation and PowerShell tooling to custom SIEM queries, analysts write and maintain scripts constantly. This makes AI a natural productivity enhancer for development and analysis tasks.
AI can quickly generate draft code, refactor existing logic, or help translate ideas into working scripts. Used correctly, it reduces repetitive effort and helps analysts reach a usable starting point faster.
However, AI does not understand operational context. If analysts lack domain depth, AI-generated code may appear correct while containing subtle and dangerous errors. This introduces a real risk: deploying logic that has not been fully understood or tested.
AI should assist with mechanics, not judgment. Teams remain responsible for validation, testing, and understanding the operational impact of every line of code. Establishing coding standards, approved libraries, and dependency rules and embedding those constraints into AI usage is essential.
4. Smarter Workflows, Clearer Boundaries in Automation
Automation is not new to SOCs, but AI is changing how orchestration workflows are designed. Instead of manually converting runbooks into automation logic, analysts can now use AI to draft workflow structures, branching logic, and platform-specific formats.
This can significantly reduce design time but it does not change the most important question in automation: when should an action execute automatically, and when should it require human review?
That decision depends on risk tolerance, environment sensitivity, and the potential impact of the action. AI should never be the authority that initiates security actions. People must remain accountable.
Whether using SOAR platforms or newer orchestration models, AI’s role is to assist in building and refining workflows not triggering them. Trust in automation grows only through testing, transparency, and consistent human oversight.
5. Turning Data Into Decisions Through Better Reporting
Reporting remains one of the most persistent pain points in security operations not due to lack of expertise, but because clear communication does not scale easily. The 2025 SANS SOC Survey shows that 69% of SOCs still rely on mostly manual reporting processes, limiting visibility and slowing decision-making.
AI offers a low-risk, high-impact improvement here. It can standardize structure, improve clarity, and help analysts convert raw notes into concise, consistent summaries. Instead of fragmented styles and overly technical narratives, AI enables reports that leadership can quickly understand and compare.
The real benefit is not polish it’s consistency. When reports follow a predictable format, trends become easier to spot, priorities clearer to set, and analysts reclaim time previously lost to formatting and rewriting.
From Consumption to Creation: How SOCs Actually Use AI
AI adoption in SOCs typically falls into three categories:
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Takers use AI tools exactly as delivered.
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Shapers customize and adapt tools to fit workflows.
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Makers build new capabilities, such as tightly scoped machine learning detections.
Most SOCs operate across all three. A team might consume vendor-provided detections, customize automation runbooks, and still manually produce reports. What matters is not the category but clarity.
Each workflow must define where AI fits, how outputs are validated, how often models are reviewed, and who remains accountable. AI should strengthen expertise, not replace responsibility.
As SOCs mature their AI strategies, success will come not from hype or scale but from precision, discipline, and thoughtful integration.
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