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09/07/2026 3:30 pm
TL;DR: False positives can be reduced by combining identity context, behavioral analytics, and unified risk scoring across more than 5,000 models, according to Gurucul, aiming to turn petabytes of noisy telemetry into higher-fidelity alerts and faster SOC decisions. The governance question is whether better correlation changes what identity data must be governed, not whether alert fatigue is real.
NHIMG editorial — based on content published by Gurucul: Neutralize the Noise With AI SIEM
By the numbers:
- Gurucul says its UEBA engine uses more than 5,000 machine learning models to identify anomalous behaviour.
- Gurucul says it scores risk across over 240 attributes on a normalized scale of 0 to 100.
Questions worth separating out
Q: How should security teams reduce SIEM noise without hiding real identity threats?
A: Start by correlating alerts to stable identity context before you tune thresholds.
A: They generate activity that is often frequent, automated, and legitimate, which makes malicious use harder to distinguish from normal operations.
Q: What do security teams get wrong about using risk scores in SIEM?
A: They often treat the score as the answer rather than the prioritisation layer.
Practitioner guidance
- Prioritise identity resolution in SIEM onboarding Map users, service accounts, privileged sessions, and workloads to the log sources that matter most, then remove sources that cannot be tied back to an accountable identity.
- Separate baselines by actor type Build different behavioural baselines for human users, service accounts, and privileged identities so routine automation does not pollute anomaly detection.
- Review scoring logic against blast radius Test whether your risk model actually elevates identities with access to critical systems, delegated privileges, and cross-cloud paths, not just the noisiest entities.
What's in the full article
Gurucul's full blog covers the implementation detail this post intentionally leaves for the source:
- How Gurucul describes its identity-centric correlation workflow across users, service accounts, and privileged activity.
- The platform's 5,000-model behavioural analytics approach and how it frames anomaly baselining.
- Details on unified risk scoring across 240 attributes and how analysts might tune it in practice.
- The vendor's own examples of incident summarisation and timeline narratives for SOC workflows.
👉 Read Gurucul's analysis of AI SIEM noise reduction and identity correlation →
AI SIEM noise reduction: what it means for identity teams?
Explore further
09/07/2026 3:50 pm
Identity noise is now an access-governance problem, not just a SOC tuning problem: once logs, alerts, and telemetry are saturated, teams lose the ability to distinguish benign identity churn from a credentialed threat path. That makes correlation quality part of identity governance, because the programme is only as strong as the identities it can reliably interpret. For practitioners, this means SIEM noise has become an identity visibility issue as much as a detection issue.
A few things that frame the scale:
- 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface, according to Ultimate Guide to NHIs.
- Only 5.7% of organisations have full visibility into their service accounts, which explains why identity-rich SIEM correlation remains uneven in practice.
A question worth separating out:
Q: How can teams tell whether AI-driven SIEM is actually improving investigation quality?
A: Look for shorter time to validate incidents, fewer repeated investigations of the same benign pattern, and better analyst confidence in the identity story behind each case. If the platform reduces volume but also removes important identity detail, the programme has traded noise for blindness, not improved detection.
👉 Read our full editorial: Identity-centric AI SIEM reduces noise, but not governance gaps
