A runtime behavioural baseline is the normal pattern of activity an identity, workload, or AI agent shows while operating. In identity security, it captures expected commands, network calls, data access, timing, and privilege use so deviations can be flagged as risk, compromise, or misuse during execution.
What Runtime Behavioural Baselines Capture
A runtime behavioural baseline is more than a snapshot of “normal” activity. It defines the recurring commands, API or network calls, data paths, timing patterns, and privilege use that an identity, workload, or AI agent exhibits while actually operating.
That makes the baseline useful for both detection and governance. If the observed behaviour shifts materially, the change can signal benign drift, a configuration change, or something more serious such as compromise, misuse, or delegated authority being abused.
Why Runtime Baselines Matter for Detection
Runtime baselines help defenders separate expected execution from suspicious deviation. In practice, that means comparing live behaviour against what should normally happen for a given workload, service account, or agent so uncommon actions can be prioritised for review.
They are especially valuable where static policy alone is not enough. A system may be correctly provisioned yet still behave dangerously at runtime, for example by calling unusual endpoints, touching unexpected data, or using privileges in patterns that the design never intended.
What Makes a Good Baseline
A useful baseline is specific enough to reflect real operational context, but stable enough to survive ordinary noise. It should account for job function, deployment stage, time of day, environment, and known change windows so that routine variation is not mistaken for malicious activity.
High-quality baselines are also evidence-driven. They are built from observed behaviour, then refined as the environment changes. For AI agents, that includes the tools they use, the sequence of actions they take, and the commands or requests that normally precede a legitimate outcome.
How to Use It in Security Operations
Runtime behavioural baselines are most effective when they feed alerting, triage, and response rather than sitting as a static reference document. Teams should use them to investigate anomalies, confirm whether an action is consistent with a role or task, and distinguish harmless variation from a true security event.
They also improve attribution. When an identity or agent behaves outside its usual profile, investigators gain a clearer picture of what changed, which actions were taken, and whether the activity aligns with expected operational purpose. That is why observability and audit-quality telemetry matter so much for runtime analysis, as reflected in the AI Agent Observability, Audit and Incident Response Guide.
Risk and Threat Considerations
Runtime baselines can fail if they are too broad, too stale, or too narrow. A weak baseline misses attacker behaviour that blends into normal operations, while an overfit baseline floods analysts with false positives and hides the signal in noise.
Failure mechanism: An attacker or misuse case succeeds by staying close to expected execution patterns while quietly changing destination systems, data targets, timing, or privilege use. A compromised workload or agent can therefore look ordinary at a glance while still creating real exposure.
Impact: The result can be delayed detection, missed lateral movement, unauthorised data access, or abuse of trusted automation. In runtime-heavy environments, especially containerised or agentic systems, a deviation that is not measured well often becomes a persistence path instead of an alert.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
MITRE ATT&CK addresses the attack and risk surface, while NIST SP 800-53 Rev 5 sets the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST SP 800-53 Rev 5 | AU-6 — Audit Record Review, Analysis, and Reporting | Runtime baselines depend on analysing telemetry to spot abnormal behaviour. |
| SI-4 — System Monitoring | Behavioural baselines support continuous monitoring for suspicious execution changes. | |
| Recommendation — Review audit and telemetry data for deviations from expected runtime behaviour. Monitor runtime activity continuously and alert on significant behavioural deviations. | ||
| MITRE ATT&CK | T1003 — OS Credential Dumping | Baseline deviation can reveal credential abuse during live compromise activity. |
| Recommendation — Map abnormal runtime behaviour to ATT&CK techniques and investigate credential-access indicators. | ||
Practitioner Guidance
Why practitioners should care: Baselines are only useful when they represent actual runtime behaviour, not aspirational policy. If you do not tie them to current telemetry, they become a historical document that misses the way systems really operate.
What to watch for: Changes in command patterns, new network destinations, altered timing, and unexpected privilege expansion usually deserve the fastest review. For infrastructure and containerised workloads, the runtime context described in NIST SP 800-190 Container Security is a useful companion reference for thinking about execution-time risk.
Practitioner takeaway: Treat the baseline as a living detection control, not a one-time modelling exercise. The more accurately it reflects real runtime behaviour, the more defensible your anomaly decisions become.