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02/08/2026 12:27 pm
TL;DR: Security teams often own SIEM, EDR, identity, and cloud tools but still cannot turn them into outcomes because analysts lack time to correlate context, according to Dropzone AI and the latest SANS SOC Survey. The ROI gap is now an operating-model problem, not a tooling problem, because automation and integration determine whether telemetry becomes decisions.
NHIMG editorial — based on content published by Dropzone AI: The ROI Gap in Cybersecurity, When Great Tools Go Unused
Questions worth separating out
Q: What breaks when a security team has tools but no time to operate them?
A: Detection fidelity becomes less useful because signals age in queues before anyone can act.
Q: Why do identity signals matter in AI-driven SOC investigations?
A: Identity signals matter because many security decisions depend on who acted, from where, with what access, and whether the behaviour fits the user's normal pattern.
Q: How do you know if SOC automation is actually improving security outcomes?
A: Look for shorter time from first signal to decision, fewer alerts requiring manual review, and more consistent conclusions across analysts.
Practitioner guidance
- Map investigation handoffs across the SOC Document every step from alert intake to final escalation, including which systems analysts must open, which evidence they must copy, and where decisions stall.
- Automate identity-rich enrichment first Prioritise enrichment for identity events such as login anomalies, role changes, MFA issues, and external sharing before broader alert classes.
- Measure correlation latency as a SOC KPI Track the time between first signal and a decision-ready case, not only mean time to detect or close.
What's in the full article
Dropzone AI's full article covers the operational detail this post intentionally leaves for the source:
- Examples of how its AI SOC analyst uses existing SIEM, EDR, identity, and cloud tools in a single investigation flow
- The specific evidence types it says it can pull, including process trees, cloud API calls, and file access patterns
- Operational examples of triage outputs, such as timelines, conclusions, and technical findings for analysts to review
- A vendor-side explanation of how it claims to reduce MTTC, false positives, and repetitive manual investigation work
👉 Read Dropzone AI's analysis of SOC tool ROI and AI analyst workflows →
AI SOC analysts and the security ROI gap: what changes now?
Explore further
02/08/2026 1:26 pm
The ROI gap is really a coordination gap. Security leaders often talk about tool sprawl, but the deeper issue is that evidence is distributed faster than analysts can assemble it. SIEM, EDR, cloud, and identity data all exist, yet they are not converted into a single operational picture quickly enough. That means the value of the stack is capped by human time, not tool capability. Practitioners should treat correlation latency as a measurable governance problem.
A few things that frame the scale:
- From our research: Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to The 2026 Infrastructure Identity Survey.
- Only 7% of security leaders admit they do not know how often their AI systems are making autonomous changes to infrastructure, according to The 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should teams govern AI-driven SOC response when identity signals are involved?
A: Treat identity telemetry as part of the case record, not a side input. If the platform can see service accounts, tokens, sign-ins, or privileged access but cannot preserve that context through response, the organisation loses traceability. That is especially important when NHI abuse and identity compromise are part of the detection story.
👉 Read our full editorial: AI SOC analysts can close the ROI gap in security operations
