They often treat baselines as a detection feature instead of a governance model. A useful baseline must reflect identity relationships, context, and normal sequences across the enterprise. If it only scores isolated anomalies, it will miss coordinated activity that looks normal in each individual system.
Why This Matters for Security Teams
Behavioural security baselines are often treated as a tuning exercise for alerting, but that framing misses the governance problem. A baseline is only useful if it captures who is acting, what they are authorised to do, and how actions chain across systems. Without that context, teams end up normalising noisy telemetry while coordinated abuse still looks legitimate in each individual tool.
This is where identity sprawl and secret-heavy automation become dangerous. NHI Management Group notes in the Ultimate Guide to NHIs that NIST Cybersecurity Framework 2.0 thinking works best when detection, response, and governance are aligned rather than siloed. That matters because a baseline built on isolated anomalies cannot distinguish a valid service account from a compromised one if both are using expected APIs.
In practice, many security teams discover baseline failure only after an attacker has used normal-looking activity to move laterally, rather than through intentional design of behaviour-aware controls.
How It Works in Practice
A useful behavioural baseline is not a single score. It is a model of identity relationships, sequence, timing, and context across systems. For NHIs, that means tracking which workload talked to which service, under what trust conditions, with what secrets or tokens, and in what order. The Ultimate Guide to NHIs is clear that weak visibility and excessive privilege are common failure points, which is why baselines should be tied to identity inventory and privilege review, not just SIEM detection.
Good practice is to anchor the baseline to governed identity behaviour, then evaluate deviations at runtime. That usually includes:
- Mapping workloads, service accounts, API keys, and tokens to an owning system and business function.
- Defining normal call chains, not just normal logins, especially for automation and integrations.
- Using context such as source environment, time window, data sensitivity, and whether the action is expected for that job.
- Reviewing baselines when architecture changes, because new pipelines often make old “normal” behaviour obsolete.
NIST Cybersecurity Framework 2.0 supports this kind of continuous governance by treating identity, monitoring, and response as connected functions. The operational goal is to make baselines evidence-based and policy-driven, not just anomaly-scoring heuristics. These controls tend to break down in highly dynamic cloud environments with ephemeral workloads and loosely governed third-party integrations because the baseline changes faster than it can be reviewed.
Common Variations and Edge Cases
Tighter behavioural baselining often increases operational overhead, requiring organisations to balance stronger detection against review burden and false positives. That tradeoff becomes sharper when workloads are ephemeral, multi-tenant, or heavily automated, because short-lived identities can make “normal” difficult to pin down. Best practice is evolving, and there is no universal standard for how much behavioural drift should be tolerated before a baseline is retrained or a control is escalated.
Edge cases matter. A baseline built for a stable internal application may fail for:
- CI/CD pipelines that change frequently and generate bursty, time-boxed activity.
- Agentic or autonomous systems that chain tools in unpredictable sequences.
- Third-party OAuth connections where the true identity relationship is indirect.
- Shared service accounts where multiple processes collapse into one behavioural profile.
This is why security teams should treat behavioural baselines as a governance artefact that must be reviewed alongside access, rotation, and offboarding. If the baseline is not tied to ownership and intended use, it will drift into a noisy detector that alerts on harmless change while missing coordinated misuse that stays within expected thresholds.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-01 | Behaviour baselines depend on identity inventory and visibility into NHI relationships. |
| NIST CSF 2.0 | DE.CM-8 | Continuous monitoring is central to detecting abnormal behaviour across identity chains. |
| NIST AI RMF | AI RMF supports governance of context-aware behavioural models and their operational limits. |
Inventory all NHIs and bind baselines to ownership, purpose, and authenticated identity relationships.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on June 27, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
