Should teams use AI compression differently for humans, NHIs, and autonomous systems?

Why This Matters for Security Teams

AI compression changes the shape of identity evidence: fewer visible prompts, shorter logs, and less human review between intent and execution. That is tolerable for humans if training, approval, and monitoring stay intact. It is much riskier for NHIs and autonomous systems, because compression can erase the very artefacts needed to prove scope, provenance, and accountability. NHI governance already struggles with visibility, as the Ultimate Guide to NHIs notes that only 5.7% of organisations have full visibility into service accounts.

For autonomous systems, the issue is not just access size but runtime drift. Guidance from the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework points toward stronger governance when system behaviour is adaptive. If compression removes review points, the control model has to become more explicit, not more relaxed. In practice, many security teams discover that compression hid excessive privileges only after an agent has already exercised them.

How It Works in Practice

Teams should apply different compression rules by identity type. For humans, compression can reduce friction if it preserves review, training, and step-up verification. For NHIs, compression should never obscure workload identity, secret rotation, or traceable execution. For autonomous systems, the safest pattern is to compress presentation but expand runtime control: short-lived credentials, intent-based authorisation, and policy checks at each tool call.

That means replacing static role assumptions with request-time decisions. Current guidance suggests using workload identity as the anchor, then issuing just-in-time credentials only for the task at hand. When possible, make the policy engine decide based on context, not a pre-baked role alone. The emerging pattern is consistent with CSA MAESTRO agentic AI threat modeling framework and the NIST AI Risk Management Framework, both of which emphasize context, accountability, and continuous oversight.

Practically, that often looks like this:

• Use ephemeral secrets or tokens with short TTLs for agents, not long-lived static keys.
• Bind identity to the workload, such as through SPIFFE/SPIRE or OIDC-backed workload tokens.
• Evaluate tool access at runtime with policy-as-code, not only at deployment time.
• Keep immutable logs of what the agent intended, what it invoked, and what data it touched.
• Reserve human review for higher-risk actions, not every routine step.

That approach fits the reality described in OWASP NHI Top 10 and the Ultimate Guide to NHIs, where excessive privilege and poor secret hygiene are already common failure modes. These controls tend to break down in highly dynamic multi-agent environments because one agent can chain tools, hand off context, and outgrow the permissions originally intended for the task.

Common Variations and Edge Cases

Tighter compression often increases operational overhead, requiring organisations to balance speed against traceability. That tradeoff is manageable for humans, but it becomes harder with autonomous systems that can alter their actions at runtime. There is no universal standard for this yet, so best practice is evolving around risk tiering: low-risk summaries for humans, strict provenance for NHIs, and runtime governance for agents.

One common edge case is a mixed workflow where a human starts the task and an agent finishes it. In that case, the control boundary should follow the highest-risk actor, not the most convenient one. Another edge case is third-party or embedded agents, where compression can hide the actual owner of the workload. In those environments, the 80% of organisations reporting agent actions beyond intended scope, from the AI Agents: The New Attack Surface report, is a reminder that intent drift is already operational, not theoretical.

Where teams need a practical rule: compress the interface, not the governance. Human workflows can accept streamlined approval, NHIs need explicit scoping and rotation, and autonomous systems need continuous authorisation and revocation paths. That aligns with the risk posture described by the OWASP Top 10 for Agentic Applications 2026 and the NIST AI Risk Management Framework.

Related resources from NHI Mgmt Group

• How should teams govern AI agents that use MCP?
• How should security teams govern AI agents that use OAuth access?
• How should security teams limit the risk from AI agents that have access to production systems?
• How should security teams use AI in secret scanning without creating new blind spots?