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19/08/2026 3:57 pm
TL;DR: AI agent observability only becomes security-grade when instrumentation is chosen for tamper resistance, not just richness, because kernel eBPF and cloud audit logs survive compromise while framework callbacks and self-reports do not, according to ARMO. That makes evidence quality, not telemetry volume, the decisive design choice for security teams.
NHIMG editorial — based on content published by ARMO: Runtime Observability for AI Agents: What to Instrument and Why
By the numbers:
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security.
- Systems with least-privileged AI access had a 17% incident rate vs 76% for over-privileged systems, making organisations failing to scope AI access properly 4.5x more likely to experience a security incident.
Questions worth separating out
Q: What breaks when AI agent observability relies only on in-process telemetry?
A: You lose evidence quality as soon as the workload can influence the collector.
Q: Why do local AI agents complicate identity and access management?
A: They can retain legitimate permissions while changing timing, prioritisation, and action sequence outside human presence.
Q: How do teams know if AI observability is actually working?
A: It is working when teams can show which change caused a quality shift, which dataset surfaced the issue, and whether the regression was contained before users were affected.
Practitioner guidance
- Define a trust hierarchy for all observability sources Classify each signal source as tamper-resistant, partially resistant, cooperative, or repudiable.
- Deploy kernel and audit-log collection before in-process telemetry Prioritise eBPF sensors on nodes and provider audit log ingestion from managed services before you expand framework callbacks or SDK spans.
- Correlate exercised identity with granted permissions Compare cloud IAM and Kubernetes RBAC entitlements with the service account tokens, API calls, and sessions the agent actually used.
What's in the full article
ARMO's full blog covers the implementation detail this post intentionally leaves for the source:
- Node-level deployment guidance for kernel eBPF collection across containerised AI workloads
- Practical examples of correlating framework events with syscall and network activity
- Provider audit-log coverage for managed AI services such as cloud model and vector platforms
👉 Read ARMO's analysis of runtime observability for AI agents and tamper-resistant signals →
AI agent observability: which signals can your security team trust?
Explore further
19/08/2026 4:35 pm
Tamper resistance is the defining requirement for AI agent observability. Rich telemetry is not automatically useful in a security investigation if the workload can suppress, spoof, or bypass it. AI agent programmes need evidence-grade signals first and semantic context second, because incident response depends on what survives compromise. The practical conclusion is to treat observability as a trust architecture, not a dashboard problem.
A question worth separating out:
Q: What should teams do when managed AI services sit outside their cluster boundary?
A: Ingest provider audit logs as first-class security evidence and correlate them with node-level signals from the rest of the environment. Managed services shrink kernel reach, so the provider logs become the boundary record for what happened inside that segment of the execution path.
👉 Read our full editorial: Runtime observability for AI agents needs tamper-resistant signals
