Security Relevant Data is telemetry that has been curated for detection, investigation, and correlation rather than stored as raw noise. It usually includes the events, fields, and context that support security use cases. The point is to retain what matters for analysis while excluding data that adds cost without improving defensive value.
Expanded Definition
Security Relevant Data is not every event a platform can emit. It is the subset of telemetry that has operational value for detection, investigation, threat hunting, and correlation across systems. In practice, this includes identity events, authentication outcomes, privilege changes, API access patterns, control-plane activity, and context such as asset ownership or workload lineage. The purpose is to preserve evidence that improves defensive decisions while excluding high-volume noise that only increases storage and analysis cost.
For NHI programs, this concept sits between raw logging and curated security evidence. Definitions vary across vendors, but the underlying principle is consistent with NIST Cybersecurity Framework 2.0: keep data that can support continuous monitoring and incident response. NHIs create especially rich telemetry because their behaviour is machine-driven, repeatable, and often tied to automation pipelines, so correlation quality matters more than log volume.
Security Relevant Data is commonly misunderstood as “all logs kept for later,” when the real requirement is selective retention with clear investigative utility. The most common misapplication is collecting raw telemetry without curation, which occurs when teams treat storage as a substitute for detection design.
Examples and Use Cases
Implementing Security Relevant Data rigorously often introduces a retention and normalisation burden, requiring organisations to weigh better investigations against higher engineering and storage effort.
- Preserving service account login failures, token issuance events, and anomalous API calls to spot compromised NHIs before lateral movement begins.
- Retaining privilege escalation records and configuration changes so investigators can reconstruct who changed access and when.
- Correlating cloud audit logs with workload identity metadata to trace which automation job accessed a secrets manager or deployment pipeline.
- Filtering out repetitive heartbeat noise while keeping exception events that indicate policy drift, failed rotations, or suspicious credential use.
- Using the guidance in Ultimate Guide to NHIs — Key Research and Survey Results to prioritise telemetry around rotation failures, excessive privilege, and secrets exposure, then mapping that evidence to NIST Cybersecurity Framework 2.0 detection and response outcomes.
Teams often discover that the “right” data set is smaller than expected, but much more actionable when it is keyed to specific NHI abuse paths and incident questions.
Why It Matters in NHI Security
Security Relevant Data determines whether NHI defence is evidence-driven or guesswork. Without curated telemetry, service accounts, API keys, and agentic workflows can fail silently, and analysts are left with raw logs that are expensive to search and poor at explaining causality. This is especially important because NHI environments are frequently high-volume and high-change, where missing one correlated event can hide credential misuse, over-privilege, or a failed rotation.
NHIMG research shows that only 5.7% of organisations have full visibility into their service accounts, and 79% have experienced secrets leaks, with 77% of those incidents causing tangible damage, as reported in Ultimate Guide to NHIs — Key Research and Survey Results. Those numbers underline a simple point: if the telemetry is not curated for security relevance, investigations will miss the events that matter most. Security Relevant Data also supports control verification, because it lets defenders prove whether rotation, access restrictions, and monitoring are actually working. Organisations typically encounter the true cost of poor telemetry only after a compromise review or failed incident response, at which point Security Relevant Data 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 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST CSF 2.0, NIST AI RMF and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-06 | Covers logging and monitoring needs for NHI activity and misuse detection. |
| NIST CSF 2.0 | DE.CM | Defines continuous monitoring outcomes that rely on security-relevant telemetry. |
| NIST AI RMF | GV.2 | Relates to governance of data quality and traceability for AI-enabled systems. |
| NIST Zero Trust (SP 800-207) | Continuous diagnostics and mitigation | Zero trust depends on actionable telemetry for ongoing verification and response. |
| OWASP Agentic AI Top 10 | A4 | Agentic systems require auditability of tool use and execution traces. |
Curate NHI telemetry so logs support detection, investigation, and abuse correlation without unnecessary noise.
Related resources from NHI Mgmt Group
- Which standards are most relevant for AI data security governance?
- How should security teams unify identity across cloud and data center environments?
- What is the difference between summarising security data and prioritising security risk?
- How should security teams govern AI assistants that can access audit data?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on August 31, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
