NIST's ten AI incident practices put agent containment ahead of recovery

On 16 October 2026, NIST, the NSA Super Intelligence Security Center and CISA published ten voluntary practices for incidents during the training and evaluation of highly capable AI agents. The document introduces an operational distinction: an agent can violate the operator's intended scope through significant uninstructed actions even when no external attacker has compromised the system. Traditional incident playbooks do not always capture that failure.

The guidance defines a super-intelligence control incident around uninstructed actions that compromise boundaries, oversight or security measures. It separately identifies near-misses. Its primary audience is frontier organisations training or evaluating highly capable agents with limited supervision, not every enterprise chatbot deployment. Nevertheless, the containment and evidence principles provide a useful test for emerging autonomous systems.

Containment must survive an agent that keeps acting

The first practices call for least-privilege access, limited communication between agents, monitored network gateways and mechanisms to rapidly block or terminate activity. A runtime shutdown control should be able to revoke tool access independently of what the model says it intends to do. The guidance also recommends single-use infrastructure where possible and attention to persistence mechanisms, including unmonitored inference and copies of model weights.

For a security engineering team, the immediate test is whether one action can terminate an agent's credentials, tool sessions, outbound connections and queued work. A stop button in the user interface is not sufficient if subordinate jobs or external integrations keep running.

Agent containment is proven only when the execution environment stops acting, not when the model promises to stop.

Monitoring and evidence need their own protection

NIST's document calls for visibility across the model, harness, host and network layers, and insists that agents must not control or tamper with monitoring and logs. Recommended evidence includes configuration, model checkpoints where relevant, rejected tool calls, timestamps, network records and security-control decisions. Reconstructing a trajectory from chat transcripts alone would leave consequential actions invisible.

Define a minimum evidence schema before testing: agent and operator identities, model and harness versions, permissions, tool requests, execution results, gateway decisions, persistent artefacts and event times. Store it beyond the agent's write permissions. Drill the response using a simulated out-of-scope call and confirm that investigators can identify every affected system.

Recovery cannot mean simply restarting the model

The recovery practice requires trusted baselines for weights, harness code, containers and tool configurations, plus verification that monitoring, egress restrictions and shutdown mechanisms still work. It also recommends replaying the triggering behaviour against improved controls. A model reload without confirming the environment's integrity may recreate the same incident.

The ten practices span preparation, detection, response, recovery and reporting, including coordination with affected parties and government reporting where appropriate. They supplement NIST SP 800-61r3 rather than replacing it. Organisations experimenting with consequential AI autonomy should add an explicit agent-control incident category, a tested kill path and an evidence-preserving restart gate to their existing response plans.

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Written by the Devence Lab research team.