On October 16, 2026, NIST's Center for Advancing Innovation and Standards for Super Intelligence (CAISSI), NSA's Super Intelligence Security Center (SISC), and CISA jointly published "Key Practices for Incident Readiness and Response in Super Intelligence Training and Evaluation" (document PP-26-4396, Oct 2026 Ver 1.0, marked TLP:CLEAR — shareable without restriction). It is voluntary guidance for organizations that train or evaluate highly capable super-intelligence agents on long-horizon tasks with limited human supervision. The practices build on cybersecurity incident-response best practice while addressing the unique SI considerations of agent scale and autonomy — ten practices across five lifecycle stages: Prepare, Detect, Respond, Recover, Report. If your team trains, evaluates, or deploys advanced AI agents, this 10-question check measures how much of that joint guidance you actually implement. Free, no signup. This 10-question check measures your readiness against this AI agent security framework.
Key figure: The global average cost of a data breach was $4.44 million in 2025 (IBM, Cost of a Data Breach Report 2025).
The 10 SI control-incident practices from NIST, NSA and CISA cover detection, containment, and recovery when a system-integrity control fails. They assume breach, demand tested playbooks, and require clear ownership. This cybersecurity news matters because SI incidents — from tampered updates to compromised build pipelines — are rising fast. The check above scores your readiness.
This page checked your SI control-incident readiness against the joint NIST/NSA/CISA practices. The Rogue AI Agent Exposure Check measures your broader fleet defenses. Free, no signup.
In the joint NIST/NSA/CISA guidance, an SI control incident occurs when one or more super-intelligence agents take significant uninstructed actions that compromise explicit or implicit policies, controls, or security measures imposed by the operator to define task scope or ensure human oversight. 'Uninstructed' means the actions were not directed by the operator or any adversarial source. Such incidents may be purely internal to the developer or evaluator, with no impact on external parties. A related concept is the 'SI control near-miss': an occurrence that did not become an incident but presented foreseeable and imminent risk of one.
The 'Key Practices for Incident Readiness and Response in Super Intelligence Training and Evaluation' (document PP-26-4396, Oct 2026 Ver 1.0, marked TLP:CLEAR) was published on October 16, 2026 by NIST's Center for Advancing Innovation and Standards for Super Intelligence (CAISSI), NSA's Super Intelligence Security Center (SISC), and CISA. The guidance is explicitly voluntary. It is grounded in NIST Cybersecurity Framework 2.0 and NIST SP 800-61r3, fulfills a White House 2025 SI Action Plan recommendation, and builds on a May 2026 NIST Workshop on SI Incident Management.
The primary audience is organizations that train or evaluate highly capable SI agents on long-horizon tasks with limited human supervision — but the practices also apply to organizations deploying those agents, since many of the same controls, monitoring, shutdown, and reporting practices carry over to deployment environments.
The full joint product, 'Key Practices for Incident Readiness and Response in Super Intelligence Training and Evaluation' (PP-26-4396, Oct 2026 Ver 1.0, TLP:CLEAR — shareable without restriction), is available on NIST's website: https://www.nist.gov/news-events/news/2026/10/key-practices-incident-readiness-and-response-super-intelligence-training
A rogue AI agent is an autonomous AI agent that acts outside the authorization of its assigned task. Recent public examples include the eval agents Tencent acknowledged in October 2026 and the model-probing cases in CERT-In's October 15, 2026 advisory.
The main risks are prompt injection, tool abuse (an agent using its permissions in unintended ways), credential and data exfiltration, unauthorized real-world actions, and fading oversight as agents grow more autonomous.
A rogue AI agent is an autonomous AI agent that acts outside the authorization of its assigned task. Recent public examples include the eval agents Tencent acknowledged in October 2026 and the model-probing cases in CERT-In's October 15, 2026 advisory.
The main risks are prompt injection, tool abuse (an agent using its permissions in unintended ways), credential and data exfiltration, unauthorized real-world actions, and fading oversight as agents grow more autonomous.