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11/07/2026 8:37 pm
TL;DR: Identity analytics turns IAM telemetry into actionable evidence for access control, audit reporting, and anomaly detection, according to Soffid, with real-time processing, drill-down dashboards, and visibility across employees, privileged accounts, third parties, automated systems, and bots. The practical shift is from collecting identity data to using it continuously for governance decisions.
NHIMG editorial — based on content published by Soffid: Identity Analytics: gestión y análisis de datos aplicados al control de accesos
By the numbers:
- Only 5.7% of organisations have full visibility into their service accounts.
Questions worth separating out
Q: How should security teams use identity analytics to improve IAM governance?
A: Use identity analytics to turn access records into governed evidence, not just reports.
Q: Why do IAM programmes struggle without identity analytics?
A: IAM programmes often collect enough data but still lack the context needed to act on it.
Q: What breaks when access data is not classified by identity type?
A: Analysts lose the ability to distinguish routine user access from high-risk activity by service accounts, third parties, or bots.
Practitioner guidance
- Map identity data sources to governance decisions Identify which events, entitlements, and access records feed access reviews, audit evidence, and risk triage.
- Classify identities by actor type before building dashboards Separate employees, privileged accounts, third parties, automated systems, and bots so alerts reflect the risk profile of each identity type.
- Use behavioural thresholds for review, not only static entitlements Add timing, frequency, and scope checks to entitlement reporting so reviewers can spot access that looks valid on paper but abnormal in practice.
What's in the full article
Soffid's full article covers the operational detail this post intentionally leaves for the source:
- The specific dashboard types and visual filters used for identity analysis in the Soffid module
- How the platform presents access, audit, and risk views for different identity categories
- The implementation detail behind real-time processing, drill-down filters, and report generation
- How the module is integrated into broader IAM, PAM, and IGA workflows
👉 Read Soffid's article on identity analytics for IAM access control →
Identity analytics for IAM teams: what changes when access data becomes evidence?
Explore further
11/07/2026 9:05 pm
Identity analytics is becoming the evidence layer for IAM, not a reporting add-on. The article is right to frame this as a BI problem because many organisations already have identity data but cannot operationalise it for governance. That shift matters across IGA, PAM, and NHI programmes, where the question is no longer whether data exists but whether it can drive access decisions. Practitioners should treat analytics as a control-enablement layer, not a dashboard project.
A few things that frame the scale:
- Only 5.7% of organisations have full visibility into their service accounts, according to Ultimate Guide to NHIs.
- 96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools.
A question worth separating out:
Q: How can organisations prove that identity analytics is actually helping?
A: Measure whether analytics reduces time to produce audit evidence, improves access review quality, and surfaces exceptions earlier. If the dashboards do not change decisions or shorten investigations, the programme is producing visibility without governance value.
👉 Read our full editorial: Identity analytics for IAM turns access data into evidence
