An Aggregate Feature is a derived signal built from patterns across multiple events rather than from a single observation. In security and identity analytics, these features often reveal behaviour that is invisible at message or request level, but they also require careful precomputation and governance.
Expanded Definition
Aggregate features are engineered signals that compress many events into a single analytical input, such as counts, ratios, rolling averages, uniqueness measures, or sequence summaries. In NHI and agentic AI security, they are used to expose patterns that isolated requests cannot show, including bursty token use, repeated failures, unusual fan-out, or time-based drift in service account behaviour.
Because these features are derived rather than observed directly, their meaning depends on the precomputation window, aggregation rule, and identity boundary chosen for analysis. Definitions vary across vendors, and no single standard governs this yet; in practice, teams should treat aggregate features as governed security telemetry, not as neutral data science artifacts. That distinction matters because a feature that is useful for anomaly detection can also embed bias, hide context, or overstate risk if the window is too wide or the subject is mixed across identities.
The most common misapplication is treating an aggregate feature as a complete explanation of behaviour, which occurs when analysts ignore the event-level evidence behind the score. For background on how NHI signals support governance, see the Ultimate Guide to NHIs and the NIST Cybersecurity Framework 2.0.
Examples and Use Cases
Implementing aggregate features rigorously often introduces latency, storage, and tuning overhead, requiring organisations to weigh better detection quality against more expensive pipelines and stricter governance.
- Service account burst analysis: total API calls per five-minute window can help identify compromised automation that suddenly fans out across cloud resources.
- Secret usage clustering: repeated token refreshes, failed authentications, and unusual geographic spread can be combined into one feature for abuse detection.
- Privilege escalation monitoring: a ratio of high-risk actions to normal workload actions can reveal an agent or workload that has shifted outside expected duties.
- Offboarding validation: aggregate counts of active connections, remaining token use, and post-revocation activity can show whether a non-human identity was fully retired.
- Detection tuning: rolling baselines over the Ultimate Guide to NHIs can help teams compare service-account behaviour before and after a control change.
These patterns fit naturally with telemetry models discussed in NIST Cybersecurity Framework 2.0, especially where continuous monitoring and anomaly detection depend on summarised identity behaviour rather than single-event inspection.
Why It Matters in NHI Security
Aggregate features matter because NHI abuse often looks ordinary at the request level. A stolen API key may make only valid calls, but a feature built from volume, cadence, and destination diversity can expose the deviation. This is especially important in environments where NHI risk is already high: NHI Mgmt Group reports that 97% of NHIs carry excessive privileges, which means a small behavioural shift can translate into major blast radius if the identity is not tightly governed.
Used well, aggregate features help security teams detect lateral movement, automation abuse, and credential replay earlier in the kill chain. Used poorly, they create blind spots when practitioners over-trust the score and skip lineage, retention, or review of the underlying events. That is why these features should be paired with identity governance, zero trust controls, and clear explainability expectations. The Ultimate Guide to NHIs is useful context for the lifecycle and visibility issues that make these summaries operationally relevant, while NIST Cybersecurity Framework 2.0 provides a governance lens for monitoring and response.
Organisations typically encounter the need for aggregate features only after a token, service account, or agent has already behaved “normally” enough to evade single-event alerts, at which point the concept becomes operationally unavoidable to address.
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-05 | Aggregate features support detection of abnormal NHI behavior across events. |
| NIST CSF 2.0 | DE.CM-7 | Continuous monitoring relies on summarized signals to spot anomalous identity activity. |
| NIST AI RMF | Feature engineering in AI systems needs measurement, traceability, and governance. |
Use aggregated telemetry to detect misuse patterns and validate NHI behavior against expected baselines.
Related resources from NHI Mgmt Group
- When does browser automation become a governance problem instead of a productivity feature?
- What is the difference between a SaaS feature and a security control?
- When does an AI agent become an NHI risk rather than a usability feature?
- When should security teams retire a feature flag or service credential?
Deepen Your Knowledge
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
