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02/08/2026 11:30 am
TL;DR: AI SOC tools that optimise for MTTR can miss critical context when they stop at the first answer, creating false negatives and shallow triage, according to Prophet. Investigative depth, not raw speed, becomes the decisive accuracy control when security teams need a complete picture of the asset, user, external entity, and action.
NHIMG editorial — based on content published by Prophet: Why Depth of Investigation is the Holy Grail of AI SOC Accuracy
Questions worth separating out
Q: What breaks when AI SOC tools stop at the first answer?
A: They create false negatives, because the system may close alerts before it has gathered the surrounding evidence needed to understand the event.
Q: Why does investigative depth matter more than MTTR in AI SOC?
A: MTTR only matters if the decision is correct.
Q: How do organisations know an AI SOC agent is working properly?
A: Look for evidence that the agent improves investigation quality, not just speed.
Practitioner guidance
- Define a context-closure threshold Specify the minimum evidence an AI SOC must collect before it can close, classify, or contain an alert.
- Require parallel enrichment before verdicts Make asset, user, domain, and action enrichment mandatory for alert classes that can affect production systems or identity trust.
- Measure false negatives against investigation depth Track how often closed alerts later reappear as incidents, and correlate those misses with incomplete context gathering.
What's in the full article
Prophet's full analysis covers the operational detail this post intentionally leaves for the source:
- How the vendor structures its AI SOC investigation workflow around alert enrichment and context gathering
- The specific questions the system asks across asset, user, external entity, and action context
- Implementation detail on how the investigative core is tuned to avoid shallow triage
- Product framing around deployment and operational workflow for teams evaluating the platform
👉 Read Prophet's analysis of AI SOC investigative depth and accuracy →
AI SOC depth versus speed: what security teams should prioritize?
Explore further
02/08/2026 12:45 pm
Investigative depth is the real control variable in AI SOC accuracy. Speed metrics such as MTTR are only useful if the underlying decision is right. When an AI system closes alerts before it has assembled the surrounding evidence, it converts automation into systematic blind spots. For practitioners, the standard should be evidence completeness, not ticket velocity.
A question worth separating out:
Q: Who is accountable when an AI SOC auto-closes the wrong case?
A: Accountability stays with the organisation that chose the workflow, not the automation layer. Human oversight, approval gates, and audit records need to show who could intervene, when escalation occurred, and why a decision was made. That is the difference between assisted operations and unmanaged delegation.
👉 Read our full editorial: AI SOC accuracy depends on investigative depth, not alert speed
