The Tool Desk
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The practical distinction is between use and adoption. An analyst can try an AI or machine-learning feature without the SOC defining who owns it, what evidence is required, how exceptions are handled, or whether it improves incident work. A dependable SOC capability has those controls and measures in place.
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Why is AI adoption in the SOC so difficult?
Survey results show why a purchase or pilot does not automatically become operational capability. The SANS Institute’s 2026 SOC snapshot reports that 79% of SOCs use AI or machine-learning tools, while only 36% have integrated those tools into a defined workflow. The gap is not a contradiction: “use” can mean an individual analyst experimenting with a feature, whereas integration means the tool has a documented place in triage, investigation, response or reporting.
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| Evidence | What it indicates |
|---|---|
| 79% using AI/ML tools (SANS, 2026) | Tools are present or being tried in many SOCs. |
| 36% with AI/ML in a defined workflow (SANS, 2026) | Fewer SOCs have assigned ownership, process steps and measurable operating controls. |
| 44% citing a significant skills gap (SANS AI Survey, 2024) | Many teams lack the expertise or capacity to configure, validate and supervise AI. |
| 58% saying effectiveness depends heavily on training-data quality (SANS AI Survey, 2024) | Input quality and relevance are prerequisites, not afterthoughts. |
| 11% completely trusting AI for mission-critical tasks (Splunk State of Security, 2025) | High-consequence actions still require evidence and human judgment for most organizations. |
The SANS AI survey was conducted in March and April 2024 and published in September 2024. Its respondents included security professionals from technology, cybersecurity, banking and finance, and government, so the percentages describe that respondent group rather than every enterprise. Splunk’s State of Security 2025 survey covered 2,058 security leaders in nine countries, was fielded from October through December 2024 with Oxford Economics, and released on May 20, 2025. Both are useful indicators of operating concerns, not controlled tests of every SOC.
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Gap 1: Skills and capacity
The team must operate the system, not just buy it
AI introduces work that is easy to underestimate: selecting useful data, setting access controls, writing and testing prompts or detection logic, checking model changes, investigating errors, and documenting decisions. In the SANS 2024 AI survey, 44% of respondents identified a significant skills gap as a top integration challenge. The SANS 2024 SOC survey separately found staffing shortages to be the leading barrier in its SOC findings and described a mismatch between SOC responsibilities and available resources. Its publisher says the most common SOC size has remained two to ten people since the survey began in 2017. The SANS 2026 SOC page still lists shortages of skilled staff as a leading challenge.
That combination matters because AI does not remove the need for experienced analysts. A model can summarize alerts or suggest a query, but someone must decide whether the answer is relevant, spot a missing signal, and approve a response that could affect production systems. If the same small team is already handling incidents, compliance requests and tool maintenance, a pilot can become another queue rather than a capacity multiplier.
What to do about the capacity gap
- Name an operational owner. Assign responsibility for configuration, access, change review, incident escalation and retirement. “The SOC” is not an accountable owner.
- Separate platform skills from security judgment. A detection engineer may tune integrations while an incident lead approves response actions. Document where those roles overlap and where approval is required.
- Reserve time for validation. Include testing and error review in the pilot plan instead of treating them as work analysts will do after hours.
- Start with a bounded task. Alert enrichment, case summarization or query drafting is easier to supervise than autonomous containment. Expand only after the team can explain failures and recovery.
- Train for failure handling. Analysts need a procedure for missing context, contradictory evidence, unsafe recommendations and service outages, as well as instructions for normal use.
Gap 2: Data quality and integration
AI inherits the limits of its inputs
The SANS 2024 AI survey found that 58% of respondents recognized that AI effectiveness depends heavily on training-data quality and relevance. In the same survey, 42% reported difficulty trusting decisions because of a lack of transparency, and its FAQ identifies integration with existing systems as another major challenge. Those findings point to the environment around a model: whether it can reach the right telemetry, whether fields have consistent meanings, and whether its result appears where analysts actually work.
Splunk’s State of Security 2025 reported that 78% of respondents said their security tools were dispersed and disconnected, while 57% said data-management gaps caused lost investigation time. Those are survey responses from security leaders, not a measurement of every enterprise, but they explain why a technically capable model can still be slow or wrong in practice. An assistant that cannot correlate identity, endpoint, network and case history may produce a polished answer from an incomplete record.
KPMG’s 2024 SOC survey of 200 security leaders also supports treating data quality and false-positive fatigue as broader SOC problems; its results are not specific measures of AI adoption. The implication is not that every SOC must centralize all data or buy a particular platform. It is that the chosen use case must have reliable access to the records needed for its decision.
A practical data-and-integration check
- Map the decision. Write down the exact analyst question, such as “Is this login anomaly related to the open incident?” List the fields and historical context needed to answer it.
- Trace each source. Identify the system of record for identity, endpoint, network, cloud and case data. Record freshness, retention, field definitions and known gaps.
- Test representative cases. Include routine alerts, ambiguous cases and incidents with missing or contradictory fields. A clean demonstration case is not sufficient.
- Verify the handoff. Confirm that output can be attached to the existing SIEM, endpoint, case-management or identity workflow without manual copying that creates omissions.
- Control access. Limit the model to the minimum data and actions required. Log which records were retrieved and which recommendation was produced.
Gap 3: Trust and output reliability
Why a plausible answer is not proof
Analysts need a way to evaluate an output before acting on it. In the SANS 2024 AI survey, 61% wanted greater transparency and algorithm refinement to reduce false positives. Among organizations that experienced AI shortcomings, the survey FAQ reports 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 create a double cost: the analyst must review the bad result, and repeated bad results teach the team to discount future alerts. Opaque reasoning creates a different problem: even a correct recommendation may be unusable if no one can identify the evidence behind it. A model confidence score alone does not establish correctness; confidence can be high when the input is incomplete or the task is outside the model’s tested conditions.
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As Michael Fanning, CISO at Splunk, put it in the company’s May 2025 newsroom release: “Human oversight remains central to effective cybersecurity, and AI is used to enhance human capabilities to help where it truly matters: defending the organization.”
Build a reliability test before expanding scope
- Define the expected output. Specify whether the tool should classify, summarize, retrieve evidence, propose a query or recommend an action. Different outputs require different checks.
- Measure false positives and false negatives. Review a representative sample and record both unnecessary escalations and missed or delayed findings.
- Require evidence links or source records. An analyst should be able to see which events, identities, files or case notes support a recommendation.
- Use a human approval gate for consequential actions. Containment, credential changes, deletion and external notification should remain reversible and approved until performance is demonstrated.
- Retest after changes. Model updates, parser changes, new log sources and policy changes can alter behavior. Keep a fixed evaluation set and compare results over time.
Gap 4: Governance and workflow adoption
From experiment to accountable process
Governance answers questions that a model cannot answer for itself: who owns the service, what data it may use, which actions are prohibited, when a person must approve a recommendation, how an exception is recorded, and when the system should be disabled. Gartner’s June 26, 2024 abstract, based on 18 Information Risk Management Research Board members, said 13 organizations had no formal advanced-AI-in-SOC strategy and were developing or considering one. The sample is small and restricted to board members, so it should not be generalized to every enterprise, but it illustrates how quickly experimentation can outpace policy.
The SANS 2026 comparison between 79% tool use and 36% defined-workflow integration is a practical governance signal. A defined workflow has a trigger, an owner, required evidence, an approval or escalation path, a record of the decision and a measure of the result. Without those elements, “adoption” may be a collection of personal habits that disappears when an analyst changes teams.
What a governed workflow contains
A Government Accountability Office review of the Cybersecurity and Infrastructure Security Agency’s automated personally identifiable information-detection component describes concrete oversight practices: measuring false detections and processing time, assigning roles, recording data origins, evaluating data quality and accuracy, testing limitations, and monitoring performance. This is a specific DHS/CISA use case, not a universal enterprise SOC standard, but the practices are transferable as design questions.
- Purpose and boundary: State the task the system supports and the tasks it cannot perform.
- Roles: Identify the business owner, technical operator, reviewer, incident commander and escalation contact.
- Data record: Document source systems, retention, access permissions, transformations and known quality limits.
- Decision record: Preserve the input context, output, human decision and any override or rollback.
- Performance monitoring: Track accuracy, false positives, processing time, analyst rework and incident outcomes, then review trends after material changes.
How to integrate AI into an existing SOC workflow
The following sequence keeps a pilot attached to an operational outcome rather than to a product demonstration.
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- Choose one measurable bottleneck. Examples include repetitive alert enrichment, evidence collection or case summarization. Write the current process and its baseline time, backlog and error patterns.
- Set a risk tier. Decide whether the output is advisory, requires analyst approval, or may trigger a restricted automated action. Begin with reversible, advisory work when evidence is limited.
- Assign ownership and reviewers. Name the person accountable for service health and the role that approves changes or high-impact actions.
- Prepare the data path. Check source completeness, freshness, permissions and integration with the SIEM, endpoint, identity and case systems actually used by the team.
- Create an evaluation set. Include normal traffic, known incidents, edge cases and records with missing fields. Label the expected result or acceptable range of answers.
- Run in parallel with the current process. Let analysts compare AI output with the existing method without allowing an unvalidated recommendation to silently replace it.
- Review errors and workload. Record false positives, missed signals, rework, time saved or added, and reasons analysts accepted or rejected suggestions.
- Decide whether to expand, change or stop. Expansion should follow evidence of improved incident handling or analyst capacity, not usage volume alone. If the system cannot meet the defined requirements, narrow the task, repair the data path or retire the pilot.
What to measure after deployment
| Measure | Question it answers | Why it matters |
|---|---|---|
| False-positive and false-negative review | How often is the output wrong or needlessly escalated? | Reveals alert fatigue and missed risk that a satisfaction survey can hide. |
| Evidence completeness | Can an analyst trace the recommendation to relevant records? | Supports review, audit and correction of opaque outputs. |
| Analyst rework | How much editing, checking or duplicate data entry is required? | A tool that adds verification work may not improve capacity. |
| Processing and handling time | Does the workflow move cases faster without reducing quality? | Connects the pilot to the operational bottleneck it was meant to address. |
| Escalation and approval behavior | Are people following the defined human-review path? | Shows whether governance exists in practice, not just in a policy document. |
| Change impact | Does performance shift after model, parser or data-source changes? | Supports monitoring and safe rollback. |
How to compare AI or SOC options without relying on a demo
If an enterprise is comparing products, managed services or internal builds, use the same questions for each option. The following axes synthesize recurring concerns in the SANS, Splunk and GAO evidence; they do not establish a vendor ranking.
| Comparison axis | Questions to ask |
|---|---|
| Data access and quality | Which sources can it read, how current are they, how are missing fields handled, and can data origins be recorded? |
| Workflow compatibility | Does it work with the existing SIEM, endpoint, case-management and identity processes, or require a parallel console? |
| Transparency and errors | Can analysts inspect supporting evidence, measure false positives and retest after updates? |
| Human control | Are approval, escalation, rollback and emergency disablement explicit and logged? |
| Operational impact | What happens to analyst time, rework, backlog and incident handling quality on representative cases? |
| Ownership and auditability | Who operates it, reviews changes, monitors performance and answers for an incorrect action? |
Common ways SOC AI projects stall
- Counting licenses as adoption: A deployed feature is not a defined process with an owner and success measure.
- Starting with autonomous response: High-impact actions expose data and reliability weaknesses before the team has learned how the system fails.
- Using a clean sample: Testing only well-formed alerts hides missing context, parser errors and contradictory records.
- Ignoring analyst workload: A recommendation that requires extensive checking can increase fatigue even when its wording looks accurate.
- Leaving policy until later: Unclear data permissions and approval rules can force a promising pilot to stop when it reaches production.
- Failing to monitor change: New telemetry, model versions or workflow edits can alter results without an obvious outage.
The practical answer to “What is stopping AI from being used in security operations?”
The obstacle is rarely a single missing model. Enterprise SOCs must close a chain of gaps at the same time: enough skilled capacity to operate the system, reliable data connected to analyst tools, outputs that can be checked against evidence, and governance that assigns ownership and measures results. Survey snapshots from SANS, Splunk and Gartner show how common each concern is, while the GAO review of CISA’s PII-detection component illustrates the concrete controls needed to make an AI capability reviewable and reversible.
Start with one bounded workflow, keep a human approval point for consequential actions, and judge the result by false positives, evidence quality, analyst workload and incident outcomes. That is how AI use becomes an accountable SOC capability rather than another disconnected tool.
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