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27/06/2026 1:39 pm
TL;DR: Identity threat detection can look inactive during a clean proof of value because its job is to monitor behavioural baselines, flag posture gaps, and surface cohort benchmarks rather than generate constant alerts, according to Abnormal AI. That means buyers should judge it by observable monitoring output, not incident volume.
NHIMG editorial — based on content published by Abnormal AI: Key Insights on why identity threat detection can look quiet during evaluations
By the numbers:
- 45% of organisations, otation is cited as the top cause of NHI-related attacks by 45% of organisations, followed by inadequate monitoring and logging (37%) and over-privileged accounts (37%).
Questions worth separating out
Q: How should security teams evaluate identity threat detection when no alerts appear?
A: Teams should judge ITDR by its baseline monitoring, behavioural deviation analysis, and posture findings, not by alert volume alone.
Q: Why do quiet PoV periods make identity tools seem less necessary?
A: Quiet proof of value windows create a visibility problem because buyers often equate security value with incident output.
Q: What do security teams get wrong about low-alert identity monitoring?
A: They often mistake low alert counts for low utility.
Practitioner guidance
- Define proof-of-value criteria around observable outputs Require baseline reports, cohort benchmarks, and posture findings as evaluation artefacts, not just alerts or incidents.
- Separate low-signal monitoring from incident response Assess whether the tool can show behavioural deviations without forcing an escalated alert.
- Map quiet-period evidence to identity risk ownership Use posture findings to show which identity controls, access paths, or account types still need hardening.
What's in the full article
Abnormal AI's full analysis covers the operational detail this post intentionally leaves for the source:
- Product and engineering examples of the specific identity telemetry the platform uses to prove it is active during quiet periods.
- More detailed discussion of how cohort benchmarks and behavioural baselines are surfaced in the product experience.
- The vendor's explanation of how posture findings are framed for proof-of-value conversations.
- Implementation context for teams that want to map those outputs to their own identity programme metrics.
👉 Read Abnormal AI's analysis of why identity threat detection can look silent →
Identity threat detection in quiet periods: how do teams prove value?
Explore further
27/06/2026 3:05 pm
Silence is not the same as absence in identity threat detection. Identity tools are often judged by alert volume, but that metric breaks down when the underlying risk is low-frequency and high-impact. The product can be doing its job by continuously testing the baseline without producing an escalated event. Practitioners should treat quiet telemetry as an operating state, not a failure state.
A few things that frame the scale:
- 1 in 4 organisations are already investing in dedicated NHI security capabilities, with an additional 60% planning to do so within the next twelve months, according to The State of Non-Human Identity Security.
- Only 1.5 out of 10 organisations are highly confident in their ability to secure NHIs, compared to nearly 1 in 4 for securing human identities, which shows why identity control validation remains uneven across programmes.
A question worth separating out:
Q: How do identity teams prove an ITDR platform is working before an incident occurs?
A: They should ask for evidence that the platform is measuring normal behaviour, flagging deviations, and exposing weak posture across the identity estate. That proof is stronger than a noisy dashboard because it shows the control is in place even when no adversary is active.
👉 Read our full editorial: Identity threat detection needs baseline proof, not alert volume
