Gartner: 70% of SOCs Will Pilot AI Agents, But Only 15% Will See Results

The AI SOC agent adoption gap

Security operations centers are racing to deploy AI agents, but most will fail to see meaningful results. According to Gartner analysts Craig Lawson and Andrew Davies in their report "Validate the Promises of AI SOC Agents With These Key Questions," 70% of large SOCs will pilot AI agents to augment Tier 1 and Tier 2 operations by 2028, yet only 15% will achieve measurable improvements without structured evaluation.

The gap between adoption and outcomes reflects a market at the Peak of Inflated Expectations. Gartner placed AI SOC agents at the Innovation Trigger stage just last year with single-digit adoption. As of 2026, they've climbed to peak hype. Prophet Security's State of AI in Security Operations 2026 survey confirms the momentum: 40% of security teams now use AI daily, 56% are evaluating or piloting it, and only 4% have no adoption plans.

Why it matters

Security leaders face mounting pressure to adopt AI while vendors flood the market with solutions of wildly varying quality. Without a rigorous evaluation framework, organizations risk deploying tools that automate the wrong work, fail to integrate with existing stacks, or collapse under maintenance burden. The 15% success rate Gartner projects isn't inevitable—it's the cost of buying on feature lists rather than outcomes.

Seven evaluation criteria that separate signal from noise

Gartner's framework pushes buyers toward measurable operational outcomes rather than vendor promises. The first question: Is AI actually reducing your team's workload? Security teams should map bottlenecks before evaluating solutions, but survey data reveals an incomplete picture. Twenty-eight percent of alerts are never investigated, and up to 40% of organizations have disabled detection rules due to capacity constraints. Sixty percent of respondents reported that missed alerts later proved material, with 34% experiencing this three or more times in the past year.

On outcomes, Gartner recommends focusing on time to detect, time to respond, false positive reduction, and time to contain. Of teams using AI, 72% reported cutting investigation time by at least 25%—a promising baseline, but one that requires sustained production validation, not just proof-of-concept numbers.

Vendor durability matters more in this category than most. Seventy-two percent of AI users had attempted to build their own tooling, with 73% reporting investigation-time gains of at least 25%. However, 46% of those builds were eventually abandoned, never reached production, or were replaced by commercial products. Maintenance proved the breaking point: keeping pace with tool changes, absorbing new alert types, and maintaining accuracy as engineers moved on.

On autonomy, practitioners remain conservative. Fifty-seven percent require human review before closing any AI-generated alert. Forty-four percent allow AI to recommend actions that humans execute, 30% auto-execute low-risk actions, and 13% extend automation to medium-risk actions. No survey respondent grants full unsupervised autonomy.

Integration extends beyond native SIEM and EDR support. Gartner asks whether solutions require data centralization or can operate across distributed environments, and how they collaborate with AI-enabled tools in ITSM, XDR, and exposure management platforms that now ship their own models.

Transparency and auditability rank at the top of adoption barriers. Forty-four percent of respondents cited data privacy and model training concerns, while 41% flagged explainability. Analysts should be able to reconstruct every query the system ran, verify results in source tools, and defend decisions to auditors.

The reallocation question

Fifty-seven percent of respondents expect AI to shift SOC roles without changing headcount over the next two years, with 9% expecting growth. The reallocation moves analysts from tier-1 triage toward incident response, threat hunting, and detection engineering. Threat hunting shows particular promise: 38% of all respondents have surfaced malicious activity through proactive hunts that detection tools missed, climbing to 49% among teams that hunt weekly or more.

The evaluation framework and survey findings were first reported by Help Net Security, with data drawn from Gartner's 2026 research and Prophet Security's State of AI in Security Operations survey.