Autonomous SOC (AI SOC) evaluation questionnaire

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All 12 vendors in this category.

• baselineRFIWhich specific SOC tasks does the AI actually perform end-to-end (alert triage, investigation/enrichment, containment action) versus merely summarizing or recommending for a human to execute — and what proportion of alerts are fully closed without human review today, with a customer reference?weight 3
  Answer key — what a strong answer shows

  Strong answers give a concrete, customer-referenced full-autonomy percentage rather than a blanket 'AI-powered SOC' claim; be skeptical of vendors who can't separate 'AI recommends' from 'AI executes.'

• baselineRFPDescribe the model architecture (LLM-based reasoning, deterministic playbooks, or a hybrid) and, critically, what guardrails prevent the AI from taking an incorrect autonomous action (e.g., isolating a critical production server on a false positive) — with a real incident where a guardrail actually stopped a bad action.weight 3
  Answer key — what a strong answer shows

  A credible vendor can describe a real case where the guardrail worked, not just that guardrails exist in theory; ask specifically what happens when the AI is uncertain — does it default to human escalation or to action?

• baselineRFIHow does the platform handle novel attack patterns outside its training data or existing playbooks — does it degrade gracefully to human escalation, or risk confidently taking a wrong autonomous action on something it hasn't seen before?weight 3
  Answer key — what a strong answer shows

  Graceful degradation to human escalation on genuinely novel patterns is a sign of a mature, safety-conscious design; overconfidence on out-of-distribution cases is a real operational risk worth probing directly.

• baselineRFIWhat is the measured false-positive suppression rate (alerts correctly closed as benign without human review) versus the false-negative rate (real threats incorrectly auto-closed), and how is the false-negative rate measured given it requires knowing about missed threats after the fact?weight 3
  Answer key — what a strong answer shows

  False-negative rate is inherently hard to measure (you only know what you eventually catch some other way); a credible vendor explains their measurement methodology rather than citing an unverifiable low number.

• baselineRFPDetail the audit trail for autonomous actions — is every AI decision (and its reasoning) logged in a way a human analyst or auditor can review after the fact, and can an analyst reverse/undo an autonomous containment action easily?weight 3
  Answer key — what a strong answer shows

  Full decision-reasoning audit trail and easy reversibility of autonomous actions are essential safety properties; a black-box action with no explainable reasoning trail is a real operational and compliance risk.

• baselineRFIHow does the platform integrate with existing SIEM/SOAR/ticketing rather than requiring a full rip-and-replace of the existing SOC stack, and can autonomy be dialed up incrementally (e.g., start with recommend-only, graduate to auto-close low-severity, then higher severities) as trust is established?weight 3
  Answer key — what a strong answer shows

  Incremental autonomy rollout (not day-one full automation) is a materially safer and more realistic adoption path; ask for a concrete example of how a real customer graduated their autonomy level over time.

• baselineRFPExplain data handling for the AI models — is customer telemetry used to train shared models across customers, kept fully isolated per-tenant, or processed by a third-party LLM API — and what are the resulting data-residency and confidentiality implications?weight 3
  Answer key — what a strong answer shows

  Cross-customer model training on shared telemetry raises real confidentiality questions security teams should ask about explicitly; a vendor should be precise about tenant isolation, not vague.

• baselineRFIWhat is the actual headcount/workload impact reported by reference customers — analyst time reallocated to higher-value work, reduced MTTR, or headcount reduction — with specific, named, verifiable figures rather than vendor-authored case studies alone?weight 3
  Answer key — what a strong answer shows

  Insist on speaking directly to a reference customer rather than accepting only a vendor-produced case study; this is an early, fast-moving category where marketing claims often run ahead of production reality.

• baselineRFPDescribe what happens during a platform outage or model-service degradation — does SOC coverage silently degrade with alerts going unprocessed, or is there a documented failover to human-only operation?weight 3
  Answer key — what a strong answer shows

  A documented, tested failover to human-only operation during an outage is essential; a platform with no plan for its own downtime creates a real coverage gap exactly when defenses might be needed most.

• baselineRFIWhat is the pricing model — per alert processed, per analyst seat replaced/augmented, or a flat platform tier — and how does cost compare to the equivalent human-analyst cost it's meant to offset, with a real customer-validated ROI figure?weight 3
  Answer key — what a strong answer shows

  Ask for a real, customer-validated cost comparison against equivalent human-analyst capacity, not just a platform price in isolation — the whole value proposition rests on this comparison being real.

• baselineRFPGiven autonomous containment/response actions could later be scrutinized in an audit or legal proceeding, is every AI decision's reasoning captured in a way that's genuinely defensible and explainable after the fact, or only a high-level action log with no interpretable rationale?weight 3
  Answer key — what a strong answer shows

  An autonomous action that can't be explained afterward is a real liability if it's later challenged — ask for a specific answer on decision-explainability, not just action logging.

• baselineRFIDetail historical trend reporting on autonomy-level progression — how has the proportion of alerts closed autonomously (versus escalated to a human) changed over the deployment lifetime, and is this trend tracked and reported to demonstrate growing trust and program value?weight 3
  Answer key — what a strong answer shows

  Trend-over-time autonomy-progression reporting is a distinct, meaningful metric for this category specifically — confirm this exists as a maintained, exportable report showing the trust-earning trajectory, not just a static current-state autonomy level.

• baselineRFIWho within the SOC organization has the authority to grant the AI additional autonomy (escalating from recommend-only to auto-close for higher-severity alert classes), and is there a documented, auditable approval process for expanding autonomous authority?weight 3
  Answer key — what a strong answer shows

  Expanding AI autonomy is a consequential decision that should have real governance — ask for a specific, documented approval process, not an informal or unilateral configuration change.

• baselineRFPHow does this platform relate to the customer's existing detection-engineering tooling — does the AI SOC platform author or tune its own detection logic, and if so, is that logic transparent/reviewable by the customer's own detection engineers, or an opaque black box?weight 3
  Answer key — what a strong answer shows

  If the AI platform is effectively authoring detection logic, that logic should be reviewable by the customer's own security engineers — a fully opaque detection layer creates real governance risk.

• baselineRFIHas the platform been red-teamed or adversarially tested specifically to see if an attacker could manipulate the AI into taking an incorrect autonomous action (e.g., crafting an attack designed to trigger a false-negative auto-close, or to trigger a disruptive auto-containment against a legitimate target)?weight 3
  Answer key — what a strong answer shows

  An AI system with autonomous action authority is itself a potential attack surface — ask for evidence of genuine adversarial testing against the AI's decision-making, not just traditional security testing of the platform's infrastructure.

• baselineRFIHow consistent is autonomous-action reliability across multi-cloud/hybrid environments — does the AI have equally reliable context and action capability across on-prem, AWS, Azure, and GCP, or meaningfully weaker autonomous capability in one environment?weight 3
  Answer key — what a strong answer shows

  Ask for an honest per-environment reliability breakdown; uneven autonomous-action reliability across environments is a real gap a vendor should disclose rather than obscure, especially given the higher stakes of autonomous (versus advisory) action.

• baselineRFPWhen the underlying AI model is updated or changes versions, does the vendor communicate this transparently and allow the customer to review/test behavior changes before they take effect in production, or can model updates silently change autonomous behavior with no customer visibility?weight 3
  Answer key — what a strong answer shows

  A silent model update that changes autonomous decision-making behavior is a real operational risk — ask for a specific change-management process around model updates, not just confirmation the model improves over time.

• baselineRFPContractor shall evaluate and implement emerging tools designated by the organization that utilize AIOps and behavioral analytics to automate incident containment and prevent attacks before they manifest, and support adoption of machine learning-based security analytics to identify patterns indicative of advanced persistent threats (APTs).weight 3

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