MANAGE-2.4: Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use.

MANAGE-2.4 is an operational control, not a policy aspiration. It asks a simple exam question: “If this AI starts behaving outside its intended use, can you stop it safely, fast, and with the right people accountable?” NIST’s framing is explicit: mechanisms must exist, they must be applied (meaning tested and used, not theoretical), and responsibilities must be assigned and understood. ¹

For a Compliance Officer, CCO, or GRC lead, the fastest path is to treat this like a specialized incident response and change-management requirement for AI-enabled workflows. You need (1) predefined decision authority, (2) observable triggers that indicate “inconsistent with intended use,” (3) technical capabilities to supersede/disengage/deactivate, and (4) routine exercises that prove the control works.

This page gives requirement-level implementation guidance you can put into a control narrative, operating procedure, and evidence plan. It’s written for teams deploying third-party AI, building in-house models, or embedding AI into customer-facing decisions where harm, compliance violations, or contractual breaches can occur if the system is allowed to run after drift or failure.

Regulatory text

Requirement (verbatim): “Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use.” ¹

Operator interpretation: You must (a) have concrete methods to override, pause, or shut down an AI system, (b) actually use and test those methods, and (c) define and socialize who can decide and who executes. “Inconsistent with intended use” must be defined for each AI system in a way that can be monitored and acted on. ¹

Plain-English interpretation (what the requirement is really asking)

If the AI system starts producing results outside what you approved it to do, you can’t wait for a quarterly model review. You need a clear “stop/go” capability:

• Supersede: replace AI output with an alternative (rules, human decision, simpler model, approved default).
• Disengage: turn off AI participation in the workflow while leaving the system running (feature flag off, route around).
• Deactivate: fully shut down access or inference (disable endpoints, revoke keys, stop jobs).

And you need named humans/roles who:

1. detect the issue, 2) decide to stop it, 3) perform the shutdown, 4) communicate, and 5) document the outcome.

Who it applies to

Entities: Any organization developing, deploying, or operating AI systems, including those sourced from a third party, that can affect customers, employees, regulated decisions, safety, security, or material business outcomes. ¹

Operational contexts where MANAGE-2.4 is most exam-relevant:

• AI used in regulated or high-impact decisions (eligibility, pricing, fraud actions, HR screening, healthcare workflows).
• AI embedded into customer communications (chatbots, email generation) where it can create legal/contractual exposure.
• AI controlling or advising operational processes (cyber triage, monitoring, access decisions).
• Third-party AI APIs where you do not control the model internals but you control routing, prompts, and integration behavior.

What you actually need to do (step-by-step)

Step 1: Define “intended use” and “inconsistent outcomes” per system

Create an AI Intended Use & Limits statement for each production AI system:

• Approved use cases and prohibited uses
• Decision boundaries (advisory vs automated)
• Required human review points
• Known failure modes (hallucination, bias drift, data drift, prompt injection, outage)
• Impact tier (business criticality, compliance exposure)

Then define inconsistency triggers that can be observed. Examples:

• Output violates content policy (e.g., disallowed advice, sensitive data exposure)
• Decision reversal rates spike (high appeals/complaints)
• Model confidence/uncertainty crosses thresholds (if available)
• Data drift or input anomaly flags
• Monitoring detects protected class disparity signals (where applicable) Keep triggers measurable and mapped to the system’s intended use, not generic “bad performance.”

Step 2: Build three shutdown modes (supersede, disengage, deactivate)

You want options, because full shutdown is not always safest.

Minimum mechanisms to implement:

• Feature flag / routing switch to bypass AI logic (disengage).
• Fallback decision path (supersede), such as:
  • human-only review queue,
  • deterministic business rules,
  • “safe default” output template.
• Hard stop controls (deactivate), such as:
  • disable inference endpoint,
  • revoke API keys/tokens,
  • block outbound calls to third-party AI,
  • stop scheduled jobs / pipelines.

Practical design note: Put the control as close to the point of execution as possible. If you only have a “process to request shutdown,” your time-to-stop will be gated by tickets and meetings.

Step 3: Assign responsibilities and decision rights (RACI that matches real life)

Create an AI Stop Authority RACI for each system:

• Trigger owner: who monitors and opens an event (ML Ops, Security, Product Ops, QA).
• Decision authority: who can approve disengage/deactivate (often Product Owner + Risk/Compliance; for urgent safety/security issues, Security may have unilateral authority).
• Executor: who flips the switch (SRE/Platform, ML Ops).
• Approver for re-enable: separate role to prevent “turn it back on” without review.

Document escalation paths for after-hours and incident severity mapping.

Step 4: Write the runbook (what happens in the first hour)

Create a short AI System Disengagement/Deactivation Runbook:

• How to validate the trigger (what logs/metrics to check)
• How to enact each shutdown mode (exact steps, commands, toggles)
• Communication steps (internal stakeholders, customer support scripts, legal/comms if needed)
• Data preservation steps (retain prompts/inputs/outputs and model version for investigation)
• Re-enable criteria and required sign-offs
• Post-incident review requirements and corrective actions

Tie this to your incident management process so it runs through the same machinery as outages and security events.

Step 5: Prove it works (applied, not theoretical)

NIST includes “in place and applied.” Treat this as a requirement to test and evidence the mechanism. ¹

Run controlled exercises:

• Tabletop: “Model starts giving prohibited advice”
• Technical drill: flip feature flag to bypass inference
• Third-party outage simulation: block API calls, confirm fallback works
• Monitoring drill: confirm alert routes to on-call and decision authority

Capture artifacts (see evidence section).

Step 6: Integrate third-party AI into stop controls and contracts

If a third party provides the AI (API/model/platform):

• Ensure you can disable calls without waiting on the provider.
• Maintain a provider offboarding plan (keys, endpoints, data deletion requests).
• Confirm your contract/SOW supports suspension for risk events and gives you access to logs needed for incident investigation. This is where many “we can shut it down” claims fail in practice: the integration is deeply embedded, and the business process has no fallback.

Step 7: Ongoing governance (keep it current)

Your “stop plan” rots quickly if it’s not part of change control:

• Update runbooks when endpoints, models, prompts, or routing logic change.
• Re-evaluate triggers when intended use expands.
• Review access rights to kill switches periodically, and remove stale admin access.

Required evidence and artifacts to retain

Auditors and internal reviewers will look for design evidence and operational proof. Keep:

• AI Intended Use & Limits statement ¹
• Trigger definitions and monitoring configuration (alerts, dashboards, thresholds)
• RACI and named role assignments (with delegated authority)
• Runbook for supersede/disengage/deactivate
• Access control evidence for kill switch authority (roles/groups, approvals)
• Change records showing mechanism implementation (tickets, PRs, release notes)
• Drill results: dates, scenario, actions taken, outcomes, gaps and remediation
• Incident records where mechanism was used (if applicable) and post-incident review

If you use Daydream to track controls and recurring evidence collection, map MANAGE-2.4 to a control owner, attach the runbook, and schedule recurring evidence prompts for drills and access reviews so your file stays audit-ready. ¹

Common exam/audit questions and hangups

Questions you should be ready to answer:

• “Show me how you can disable this AI in production. Who can do it?”
• “What constitutes ‘inconsistent with intended use’ for this system?”
• “How do you know the mechanism works? Show a drill or an incident.”
• “What is the fallback process if AI is off? Is it safe and compliant?”
• “Does your third-party AI provider arrangement allow rapid suspension and investigation?”

Hangups that stall teams:

• Over-reliance on model metrics without mapping to intended use harms.
• No clear decision authority, leading to “committee-driven outages.”
• A kill switch exists but is buried in engineering-only knowledge.

Frequent implementation mistakes (and how to avoid them)

1. Mistake: “We can shut it down” equals “we can stop harm.”
   Fix: Build supersede and disengage paths so the business can continue safely while you investigate.

2. Mistake: Triggers are subjective (“bad results”).
   Fix: Tie triggers to specific policy violations, drift indicators, complaint signals, or monitored outcome metrics relevant to the use case.

3. Mistake: One global runbook for all AI.
   Fix: Maintain a standardized template, but require system-specific steps, owners, and toggles.

4. Mistake: Third-party AI is treated as out of scope.
   Fix: Your obligation is about your deployment and outcomes. Put routing controls and contractual rights in place so you can stop use immediately.

Enforcement context and risk implications

No public enforcement cases were provided in the supplied source catalog for this requirement. Still, the risk is straightforward: if you cannot promptly stop an AI system when it produces noncompliant or harmful outcomes, you extend the duration and scale of impact. That increases incident severity, customer harm, regulatory scrutiny, and breach-of-contract exposure. MANAGE-2.4 reduces that exposure by requiring an engineered “off-ramp” with accountable operators. ¹

A practical 30/60/90-day execution plan

First 30 days (stabilize and define)

• Inventory production AI systems and rank by impact.
• For top systems, document intended use and “inconsistent outcomes” triggers.
• Assign a system owner and decision authority for shutdown actions.
• Confirm you have at least one disengage mechanism (feature flag or routing switch) for each high-impact system.

Days 31–60 (build mechanisms and runbooks)

• Implement deactivate controls (endpoint disablement, key revocation, network egress blocks where appropriate).
• Implement supersede fallback path (human queue or rules-based substitute).
• Write system-specific runbooks and integrate into incident management.
• Set up monitoring and alert routing to the accountable roles.

Days 61–90 (prove operation and harden)

• Run drills for each high-impact system; record outcomes and remediation.
• Validate third-party dependencies and shutdown paths, including offboarding steps.
• Tighten access controls to shutdown mechanisms and document approvals.
• Establish recurring evidence collection (drill cadence, access review cadence, trigger review cadence).

Frequently Asked Questions

What’s the difference between supersede, disengage, and deactivate in practice?

Supersede replaces AI output with an approved alternative. Disengage routes around the AI while leaving the rest of the service operating. Deactivate shuts off the AI capability itself, such as disabling an inference endpoint or revoking API keys. ¹

Do we need a “kill switch” even if the AI is only advisory?

Yes, if advisory output can still drive decisions or customer communications. Your mechanism may be a routing change or disabling a feature, but you still need defined authority and a documented process to stop the AI contribution when outcomes drift. ¹

How do we define “inconsistent with intended use” without overengineering metrics?

Start from the intended use statement and list concrete “must not happen” outcomes (policy violations, disallowed data exposure, prohibited recommendations). Add a small set of observable triggers tied to those outcomes, then refine after drills and incidents.

What if the AI is provided by a third party and we cannot “deactivate” their model?

You can still disengage and supersede by stopping calls to the third party and using a fallback process. Contractually, confirm you can suspend service quickly and preserve logs needed for investigation.

Who should have authority to deactivate an AI system?

Assign authority based on impact and urgency. Many organizations grant Security or SRE emergency authority for immediate risk, with Product/Risk/Compliance approval required to re-enable. Document this in a RACI and ensure on-call coverage.

What evidence is strongest for auditors?

A working mechanism demonstration (drill record), system-specific runbook, clear role assignments, and logs showing the control can be executed. Auditors respond well to “show me” evidence rather than policy-only artifacts. ¹

Footnotes

1. NIST AI RMF Core