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02/08/2026 12:00 pm
TL;DR: Security leaders are seeing AI-driven productivity fail when it is layered onto overloaded SOC workflows, inconsistent data schemas, and rising analyst burden, according to Anomali. The real constraint is not tool availability but workflow redesign, because speed only matters when it removes work instead of shifting it around.
NHIMG editorial — based on content published by Anomali: Optimizing Data and Analytics for Security Productivity at Scale
Questions worth separating out
Q: How should security teams improve productivity without adding more analyst workload?
A: They should start by removing work, not automating every step.
Q: Why do security productivity programmes fail even when new AI tools are deployed?
A: They fail because tools are often layered onto unchanged processes.
Q: What signals show that access analytics is actually working?
A: Access analytics is working when analysts can trace unusual access back to a user, device, and workflow context without manual reconciliation.
Practitioner guidance
- Standardise security event schemas before scaling AI analytics Define a common field model across identity, endpoint, cloud, and application logs so correlation and enrichment work on consistent inputs rather than vendor-specific formats.
- Redesign SOC workflows around decisions, not data volume Map each alert class to the human decision it should support or eliminate, then remove steps that do not change containment, escalation, or closure outcomes.
- Track time-to-decision as the primary productivity metric Measure how long it takes from first signal to confident triage, because shorter alert latency means little if analysts still need hours to validate the event.
What's in the full article
Anomali's full article covers the operational detail this post intentionally leaves for the source:
- The webinar discussion points on AI-driven productivity and the specific SOC friction points the speakers say are most common.
- The full breakdown of how noisy telemetry, schema inconsistency, and analyst overload interact in day-to-day operations.
- The concrete examples of modernisation sequencing and coexistence planning that the article uses to argue against big-bang replacement.
- The leadership signals discussed for measuring whether productivity work is actually reducing workload rather than shifting it.
👉 Read Anomali's analysis of why security productivity breaks at scale →
Security productivity gaps: what actually changes for SOC teams?
Explore further
02/08/2026 1:06 pm
Security productivity is a governance problem before it is a tooling problem. The article shows that organisations often buy analytic capability without redesigning the work that surrounds it. That pattern is familiar in identity programmes too, where access reviews, enrichment, and exception handling can consume more effort than the control saves. Practitioners should treat workflow design as the control surface, not the dashboard.
A question worth separating out:
Q: Who should remain accountable when AI reduces security team workload?
A: Accountability should remain with the security function that owns the control, not with the model that helped process the work. AI can reduce workload, but it does not replace the need for clear decision ownership, especially where identity, escalation, or incident response outcomes are affected.
👉 Read our full editorial: Security productivity breaks when data volume outpaces SOC workflows
