They often compare developer tracing and security telemetry as if they were the same control. Developer observability is useful for debugging, but it is not automatically resistant to tampering. Security teams should ask what happens if the agent process is compromised and whether the telemetry still survives.
Why This Matters for Security Teams
Observability is often adopted for troubleshooting speed, but security teams care about a different outcome: trustworthy evidence under attack. The mistake is assuming that tracing, metrics, and logs are inherently security-grade just because they are plentiful. The NIST Cybersecurity Framework 2.0 emphasises that detection and response depend on resilient visibility, not just data volume. If the telemetry pipeline can be altered, silenced, or flooded, the organisation may have a false sense of coverage.
That distinction matters across cloud, endpoint, and application environments. Developer observability tools are usually designed to help engineers understand performance and failures. Security telemetry must also support investigation, reconstruction, and containment when a host, container, or account is already under stress. The operational question is not whether an agent can emit data during healthy operation, but whether the signals remain useful when an attacker has gained execution or privilege. In practice, many security teams encounter observability gaps only after an incident has already corrupted the very logs they relied on to prove what happened.
How It Works in Practice
Security observability should be designed as a control plane, not a convenience feature. That means separating collection, transport, storage, and analysis so compromise in one layer does not automatically destroy the rest. Good practice is to treat telemetry like other high-value security data: restrict write access, authenticate sources, preserve time integrity, and maintain an independent path for critical alerts. Where possible, the platform should support immutable retention, out-of-band forwarding, and segregation between production workloads and the systems that receive their evidence.
Teams usually get the best results when they decide in advance which questions the telemetry must answer during an incident. For example:
- Can the system show who accessed what, and when, without relying on the same host being investigated?
- Can logs be exported to a separate trust domain before a compromised process can tamper with them?
- Can detection rules still run if the original workload is degraded or intentionally overloaded?
- Can analysts compare application traces with identity, endpoint, and cloud control-plane events?
That last point is important because observability becomes far more useful when correlated with identity and access data. A process-level trace may show a failed request, but identity telemetry may show the credential or token that made the request possible. The most effective designs also align with CISA Cross-Sector Cybersecurity Performance Goals, because the issue is not how much data is collected but whether the organisation can maintain minimum visibility under adverse conditions. MITRE ATT&CK is useful here because it helps teams map which adversary techniques target logging, monitoring, and recovery processes themselves.
In practical terms, the architecture should assume that the agent, sidecar, or collector may fail, be disabled, or be manipulated. Security teams should therefore validate whether alerts are generated from independent signals, whether service-to-service identity is strong enough to prevent spoofed telemetry, and whether the downstream store is resilient to burst traffic and deletion attempts. These controls tend to break down when telemetry is concentrated in the same cluster, account, or administrative boundary as the workload because the attacker can reach both the system and the evidence with a single compromise.
Common Variations and Edge Cases
Tighter telemetry controls often increase engineering overhead, requiring organisations to balance investigative confidence against latency, cost, and operational complexity. That tradeoff becomes sharper in high-throughput systems, short-lived containers, and multi-tenant platforms where full fidelity logging can create storage pressure or performance concerns.
Best practice is evolving for agentic AI systems and automated workflows, where observability must cover both the model output and the actions taken with that output. In those environments, security teams should track prompts, tool calls, policy decisions, and identity context, while recognising that no universal standard exists yet for how much of that data must be retained. The key question is whether the record is sufficient to explain a harmful action after the fact, not whether every intermediate token was captured.
Edge cases also appear in regulated environments. In financial services or sectors subject to strict resilience requirements, telemetry may need stronger retention, segregation, and auditability to support incident reporting and forensics. In privacy-sensitive environments, teams must avoid collecting more personal data than they can justify, then use ISO/IEC 27001-style governance to document retention and access decisions. The practical goal is a defensible balance: enough observability to investigate compromise, but not so much operational drag that teams disable the controls in day-to-day use.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
MITRE ATT&CK, OWASP Agentic AI Top 10 and CSA MAESTRO 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 |
|---|---|---|
| NIST CSF 2.0 | DE.CM | Observability overhead maps to continuous monitoring and detection quality. |
| MITRE ATT&CK | T1562 | Attackers often disable or impair logging and security tooling. |
| OWASP Agentic AI Top 10 | Agentic workflows need action-level logging and tool-use traceability. | |
| NIST AI RMF | AI systems require governance around traceability and monitoring outcomes. | |
| CSA MAESTRO | Agentic AI security needs separation of duties and resilient oversight. |
Build monitoring that still detects malicious activity when a workload is stressed or compromised.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on August 20, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
