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AI is already present in many security operations centers, but presence is not the same as dependable adoption. The SANS Institute’s 2026 SOC survey reports that 79% of SOCs use AI or machine-learning tools, while only 36% have built those tools into defined workflows. The difference is explained by four connected gaps: skills and capacity, data quality and integration, trust in outputs, and governance that turns experiments into accountable operations.
AI use is not the same as AI adoption
An analyst asking a generative tool to summarize an alert is using AI. A SOC has adopted AI when a defined process specifies where the system gets data, what task it performs, who checks the result, what happens when it is wrong, and how performance is measured over time. That distinction matters because an isolated tool can look successful in a demonstration while remaining too unreliable, disconnected or labor-intensive for daily incident handling.
The SANS 2026 snapshot illustrates the gap: 79% of surveyed SOCs reported using AI or machine-learning tools, but only 36% reported integrating those tools into defined workflows. The figures describe that survey’s respondents, not every enterprise, yet they show why a purchase or pilot should not be counted as operational transformation.
The four obstacles below are a synthesis of survey findings, not a universal ranking or a proven causal chain. A SOC may encounter all four at once, and progress in one area does not automatically solve the others.
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Gap 1: Skills and capacity
AI does not remove the need for experienced analysts. It adds work: selecting an appropriate use case, preparing data, writing or tuning instructions, checking outputs, handling exceptions, and documenting changes. A team that is already short-staffed may have no protected time for those activities.
What the surveys show
In the SANS 2024 AI Survey, conducted in March and April 2024 and published in September, 44% of respondents identified a significant skills gap as a top challenge to AI integration. The SANS 2024 SOC Survey separately described staffing shortages as the leading barrier in its SOC findings and noted a mismatch between responsibilities and available resources. The SANS 2026 SOC page still lists shortages of skilled staff among the leading challenges.
The common SOC size in the 2024 SOC survey was 2–10 people, a pattern the publisher says has remained consistent since the survey began in 2017. In a team that small, assigning an engineer to evaluate models, maintain connectors and review outcomes can compete directly with alert triage and incident response.
Why a skills gap becomes an adoption gap
- Unclear ownership: No one is accountable for updating prompts, rules, permissions or evaluation sets when the environment changes.
- Review bottlenecks: Analysts must manually verify every generated summary or recommendation, so the tool adds a second queue instead of removing work.
- Uneven expertise: A team may know either security operations or machine-learning behavior, but not both well enough to diagnose failures.
- Change fatigue: Frequent tool changes, new data sources and new procedures can overwhelm a small SOC before benefits are established.
What to do about it
Start with one task that has a clear owner and a bounded risk, such as summarizing investigation notes or enriching an alert with approved context. Define the analyst skills required to review that output, then schedule that review as part of normal staffing rather than assuming it will happen in spare time. Record who can change the configuration, who approves a production release and who handles a failure. If nobody has capacity for those duties, the SOC is not ready for a broader deployment, regardless of how capable the model appears.
Gap 2: Data quality and integration
An AI system can only reason over the information it receives. Missing telemetry, stale asset records, inconsistent field names and duplicated events can produce a confident-looking answer that is operationally wrong. The system also has to reach the places where analysts work: the SIEM, endpoint tools, identity systems, ticketing or case management and approved knowledge stores.
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Evidence of the data problem
The SANS 2024 AI Survey found that 58% of respondents recognized that AI effectiveness depends heavily on the quality and relevance of training data. The same survey’s frequently asked questions identified integration with existing systems as another major challenge.
Splunk’s State of Security 2025 survey, fielded from October through December 2024 among 2,058 security leaders in nine countries with Oxford Economics, found that 78% said their security tools were dispersed and disconnected. Fifty-seven percent reported losing investigation time because of data-management gaps. Those are survey perceptions rather than measurements of every SOC, but they explain why an AI feature can fail even when its underlying model is sound: the surrounding environment does not provide a complete, consistent case history.
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Questions to answer before connecting an AI tool
- Which data sources are in scope, and how current are they?
- Are timestamps, user identifiers, host names and severity fields normalized across systems?
- Can the system distinguish an absent value from a negative finding?
- What permissions limit the data it can read, retain or send to another service?
- Can the generated result be written back to the case record with its source data and version?
Centralizing every log is not a prerequisite. The practical requirement is that the selected use case has access to the relevant, sufficiently reliable inputs and that analysts can trace an output back to those inputs. A narrow, well-mapped data set is often safer than a broad feed of poorly understood records.
Gap 3: Trust and output reliability
Security analysts must decide whether to escalate, contain or close an event. They therefore need more than a fluent explanation or a numerical confidence score. They need evidence, scope, known limitations and a way to test whether the output is accurate for their environment.
Why confidence erodes
In the SANS 2024 AI Survey, 61% of respondents wanted greater transparency and algorithm refinement to reduce false positives. Among organizations that experienced AI shortcomings, the survey’s FAQ reported false positives leading to alert fatigue. Splunk’s 2025 survey found that only 11% of respondents completely trusted AI for mission-critical tasks.
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False positives consume the same scarce analyst attention as genuine incidents. Opaque reasoning makes it difficult to challenge a recommendation, while an incomplete data set can cause a model to miss an attack without signaling that its evidence was insufficient. A model’s confidence value alone does not establish correctness; it is another output that must be validated.
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- Define the decision: State exactly what the AI output is allowed to recommend and what remains a human decision.
- Show evidence: Require links or references to the events, identities, assets and detections used to produce the result.
- Test against known cases: Use historical benign alerts, confirmed incidents and difficult borderline cases from the SOC’s own environment.
- Measure both errors: Track false positives and missed or incorrectly downgraded events, not just an aggregate accuracy claim.
- Measure analyst effort: Record review time, rework and escalation quality. A technically accurate result that takes longer to verify may not improve operations.
- Set a rollback rule: Pause or revert the workflow when error rates, latency or unexplained behavior exceed the agreed limit.
Human oversight is not a temporary concession. In a Cisco newsroom release, Michael Fanning, CISO at Splunk, stated: “Human oversight remains central to effective cybersecurity, and AI is used to enhance human capabilities to help where it truly matters: defending the organization.” The useful question is not whether a person is present somewhere in the process, but whether that person has enough evidence, time and authority to reject a bad output.
Gap 4: Governance and workflow adoption
A tool becomes dependable only when the SOC defines its boundaries. Governance assigns an owner, records data origins, sets approval and escalation rules, specifies retention and access controls, and establishes measures that can reveal improvement or degradation.
The strategy gap
A Gartner abstract dated June 26, 2024, summarized responses from 18 Information Risk Management Research Board members. Thirteen of the 18 organizations had no formal strategy for advanced AI in the SOC and were still developing or considering one. That small, restricted sample should not be generalized to every enterprise, but it demonstrates how experimentation can outpace policy.
The SANS 2026 figures show the operational consequence: many more SOCs report using AI or machine-learning tools than report integrating them into defined workflows. Without a named process, an analyst may use a tool differently from a colleague, making results difficult to audit or improve.
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Controls illustrated by a CISA use case
The U.S. Government Accountability Office’s review of an automated personally identifiable information (PII) detection component used by DHS’s Cybersecurity and Infrastructure Security Agency documents concrete oversight practices. CISA measured false detections and processing time, assigned roles, recorded data origins, evaluated data quality and accuracy, tested limitations and monitored performance. This is a specific government use case, not a universal enterprise standard, but the practices translate well to SOC pilots:
- name the business and technical owner;
- document what data the component receives and where it came from;
- test accuracy and known limitations before production use;
- measure processing time and error types;
- require human review for consequential actions;
- monitor performance after deployment and record changes.
How the four gaps reinforce one another
| Gap | Typical symptom | Operational consequence | Evidence to collect |
|---|---|---|---|
| Skills and capacity | No owner or insufficient review time | Pilots stall or create a second workload | Staff hours, review queue, ownership map |
| Data quality and integration | Missing, stale or disconnected context | Inconsistent investigations and manual copy-and-paste | Field completeness, freshness, connector failures |
| Trust and reliability | Opaque reasoning or excessive false positives | Alert fatigue and cautious non-use | False positives, misses, rework and analyst time |
| Governance and workflow | Experimentation without limits or measures | Inconsistent decisions and no accountable rollback | Approval records, audit trail, outcome metrics |
These gaps can form a loop. Poor data produces unreliable outputs; unreliable outputs increase review work; increased review work worsens staffing pressure; and staffing pressure encourages informal use without documented controls. Breaking the loop usually requires a narrow workflow with explicit ownership rather than a larger model or a wider data feed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A step-by-step way to integrate AI into an existing SOC
1. Choose a bounded, measurable task
Write the current process in detail: trigger, inputs, analyst actions, decision and hand-off. Select one point where assistance could reduce time or improve consistency. Avoid granting autonomous containment or access changes until the SOC has evidence that lower-risk tasks perform reliably.
2. Map the data and permissions
List every source the task needs, its refresh interval, field definitions and access owner. Remove irrelevant data, identify missing values and decide how the system should behave when an input is unavailable. Confirm that outputs can be attached to the existing case record without bypassing retention or access controls.
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Collect representative historical cases, including benign alerts, confirmed incidents and ambiguous examples. Measure the current analyst time and outcome quality before enabling AI. This baseline is necessary to tell whether a pilot actually improves operations.
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4. Define human approval and failure handling
Specify which outputs are advisory, which require a second analyst and which actions are prohibited without explicit approval. Document how to report an incorrect result, how to stop the workflow and who can revert the change.
5. Run a limited pilot
Use a small analyst group, a fixed period and a versioned configuration. Log prompts or rules, model versions, input sources, outputs, corrections and elapsed time. Compare results with the baseline rather than relying on a successful demonstration.
6. Review and decide
Assess false positives, missed events, review time, escalation quality and data failures. Continue only if the task meets the agreed thresholds and the SOC can staff its oversight. If not, narrow the scope, repair the data or stop the workflow; rollback is a valid outcome.
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How to compare AI options without relying on demos
When evaluating two or more tools, use the same questions for each rather than ranking vendors from marketing claims:
- Data access and quality: Can it use the relevant sources with traceable provenance and acceptable freshness?
- Workflow compatibility: Does it work with the SOC’s SIEM, endpoint, case-management and identity processes?
- Transparency: Can analysts inspect supporting evidence and measure false-positive behavior?
- Control: Are approval, escalation, permissions and rollback explicit?
- Operational impact: Does it reduce analyst time or improve incident handling against a baseline?
- Accountability: Are ownership, audit records, monitoring and change management available?
These comparison axes synthesize recurring SOC concerns; the cited surveys do not establish a controlled ranking of products.
What a realistic success measure looks like
Success is not the number of prompts issued or the percentage of analysts who tried a feature. A stronger assessment combines accuracy with operational cost: time to reach a sound decision, quality of escalation, false-positive and missed-event rates, data completeness, human override frequency and the effort required to maintain the workflow. Measure those outcomes for a defined use case and period, then revisit them as telemetry, threats and staffing change.
The central lesson is straightforward: AI can enhance a capable SOC, but it cannot substitute for the people, data discipline, validation and governance that make security decisions dependable. Closing the four gaps is what turns a tool that analysts occasionally use into an accountable part of daily operations.
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