The Day After an AI Crisis Is Too Late to Build the Response

Executives at major AI companies are reportedly planning for what could follow a catastrophic AI event. Axios describes scenarios involving cyberattacks that disrupt financial services, internet access, electricity, or water. OpenAI says its preparedness exercises cover a range of potential scenarios and do not treat those events as inevitable. Read the original reporting.

The reporting raises a practical question for every organization deploying consequential AI: If an AI incident occurred today, could the organization determine what happened, limit further harm, and give affected people timely information they can use?

Preparing for that moment requires more than a crisis communications exercise. Organizations need to know which AI systems could affect critical operations, what those systems are permitted to do, who has authority to restrict them, and what evidence will support each response decision.

In practical terms, AI incident readiness means establishing system ownership and operating boundaries before an event, then giving responders a consistent process for investigation, impact assessment, containment, coordination, documentation, and remediation.

What should organizations do before a major AI incident?

Organizations should prepare for AI incidents by:

• Maintaining an inventory of AI systems, models, agents, and vendors
• Documenting each system’s intended use and operating boundaries
• Assigning clear human ownership and response authority
• Identifying the evidence needed to investigate unexpected behavior
• Establishing escalation, containment, and communication procedures
• Rehearsing realistic scenarios with cross-functional teams
• Feeding incident findings back into policies, controls, and monitoring

These capabilities strengthen incident management as part of end-to-end AI governance. Governance spans the AI lifecycle, including how incidents are identified, assessed, and addressed. Effective incident management depends on clear ownership, defined responsibilities, and actionable response processes.

Test a realistic AI incident scenario

A major AI incident could have many causes. An agent might exceed its authorized task. A malicious actor might misuse a model to accelerate an attack. A vendor’s system might disrupt a customer workflow. An AI-generated decision might create privacy, fairness, safety, financial, or operational concerns.

Each scenario raises different questions about ownership, evidence, containment, impact, and potential obligations.

An effective exercise should require teams to answer practical questions:

• Which AI systems and vendors could affect a critical service?
• Who owns each system and the associated business process?
• Who can restrict an agent’s tools, permissions, or access?
• What records would show the system’s instructions, outputs, and actions?
• How would the organization identify affected people, customers, or outside systems?
• Who determines whether legal, regulatory, contractual, or communication obligations apply?
• Who contacts affected parties, and when?

The exercise has succeeded when it reveals a specific readiness gap, assigns an owner, and produces a measurable corrective action.

Use AI risk management to establish a baseline

Incident responders cannot identify abnormal behavior without a clear record of what normal, approved behavior looks like.

That baseline should identify the AI system and its owner, its intended use, the data and tools it can access, applicable policies and requirements, required human oversight, and the conditions under which its activity should be restricted or stopped. It should also account for third-party models and vendors that may affect the organization’s operations.

Radar AI Risk helps teams inventory AI systems, assess risk against applicable requirements and internal policies, apply guardrails, and maintain traceable documentation as systems change. During an incident, that governance record helps responders compare actual behavior with the system’s approved purpose and operating boundaries.

For an AI agent connected to a financial or operational workflow, responders should be able to determine:

• Which tools and data the agent could access
• Which actions it could take independently
• Which decisions required human approval
• What records captured its activity
• Whether it operated outside an established control

A team must understand those boundaries before it can determine what crossing them means.

Build a repeatable AI incident response process

An AI incident may surface through a monitoring alert, vendor disclosure, employee report, customer complaint, or affected third party. Those signals need to enter one coordinated process.

Radar AI Incident Management gives teams a structured way to capture AI-specific facts, collect supporting evidence, assess potential harm and business impact, apply regulatory intelligence, coordinate cross-functional action, and document the rationale behind decisions.

A defensible incident record should distinguish among:

• Established facts
• Working assumptions
• Unanswered questions
• Evidence collected
• Decisions and approvals
• Containment and corrective actions
• Potential obligations
• Final outcomes

This structure becomes more important as an incident expands. Leaders need reliable facts. Affected parties need timely, useful information. Investigators need preserved evidence. Remediation teams need a clear connection between the incident and the controls, permissions, or workflows that must change.

Rehearse the first 24 hours

Public attention may focus on the day after a major event. Operationally, however, organizations need to rehearse the first minutes and hours.

Choose a plausible incident involving an AI system the organization uses today. Test whether the response team can:

1. Identify the system, owner, model, vendor, and affected workflow.
2. Preserve prompts, outputs, logs, tool calls, approvals, and other relevant evidence.
3. Restrict further activity without unnecessarily disrupting unaffected operations.
4. Determine which people, data, systems, and decisions may have been affected.
5. Bring privacy, legal, compliance, security, product, communications, and business leaders into the response when appropriate.
6. Assign decisions and document the evidence behind them.
7. Communicate what is known, what remains uncertain, and what happens next.

The goal is not to predict every possible AI failure. The goal is to make the response process dependable when facts are incomplete, and consequences are still developing.

Turn incident findings into stronger controls

AI incident response should not end when immediate activity is contained.

Teams should use the incident record to determine whether policies, permissions, monitoring, human oversight, vendor requirements, or system assessments need to change. Corrective actions should have owners, deadlines, and documented completion criteria.

This feedback loop connects incident management with ongoing AI governance. It helps the organization learn from actual behavior instead of relying only on anticipated risks.

The NIST AI Risk Management Framework supports this lifecycle approach. Its Manage function addresses monitoring, incident response, recovery, communication, and continual improvement.

Prepare to produce defensible answers

Radar AI Risk establishes what an AI system is intended to do and is permitted to do. Radar AI Incident Management provides a repeatable process for investigating and responding when actual behavior tests those expectations.

The day after a major AI incident will demand answers. Operational preparation determines whether an organization can produce timely, evidence-backed, defensible answers.

See how Radar AI Risk and Radar AI Incident Management connect AI governance with a structured, defensible response process. Request a demo.