Your AI hiring tool just rejected 5,000 qualified candidates because of a bug.
Your fraud detection AI just flagged your best customers as high-risk and locked them out of their accounts.
Your customer service chatbot just gave advice that violates regulations in three states.
What do you do in the next hour?
If you’re like most companies, you don’t know. Because your incident response plan was written for cybersecurity breaches, not AI failures.
And AI incidents are fundamentally different.
Why Your Cyber Incident Response Plan Doesn’t Cover AI
Most companies have detailed incident response plans for cybersecurity events. Data breaches. Ransomware. Unauthorized access. System outages.
These plans are good. They’re necessary. But they don’t cover AI incidents.
Because AI failures create different problems that require different responses.
Traditional IR focuses on:
Containing the breach. Preserving evidence. Notifying affected parties. Restoring systems.
AI incidents require:
Understanding what the AI did wrong. Identifying who was affected and how. Assessing legal and regulatory exposure. Determining if you need to notify regulators. Deciding whether to shut down the AI or let it keep running. Communicating with stakeholders about algorithmic failures.
Your cybersecurity team knows how to respond to a data breach. They don’t know how to respond when your AI makes 10,000 discriminatory decisions.
The Five Types of AI Incidents Companies Aren’t Prepared For
Let me show you what AI incidents actually look like.
Incident Type 1: Algorithmic Failures
Your AI system produces wrong outputs at scale.
Examples: An AI hiring tool rejects all qualified candidates due to a bug. Credit scoring AI approves high-risk borrowers and rejects low-risk ones. Pricing AI sets illegal prices that violate antitrust laws. Medical AI recommends dangerous treatments.
Why this is different: The harm isn’t that someone accessed your systems. It’s that your system made thousands of wrong decisions that affected real people. Some may have legal claims. Some may require notification. Some may trigger regulatory investigations.
Your cyber IR plan doesn’t tell you what to do.
Incident Type 2: Bias and Discrimination Incidents
Your AI produces outcomes that have disparate impacts on protected groups.
Examples: Hiring AI systematically rejects women or older candidates. Lending AI denies credit to minority applicants at higher rates. Ad targeting AI shows opportunities only to certain demographics. Housing AI flags protected groups as high-risk.
Why this is different: You may not realize it’s happening until someone complains or a regulator investigates. The evidence is in patterns across thousands of decisions. You need to analyze outcomes by demographic group. You may need to notify affected individuals. You might face EEOC complaints or civil rights lawsuits.
Your cyber IR plan has no playbook for this.
Incident Type 3: Training Data Breaches
Your AI training data is exposed, leaked, or stolen.
Examples: Training data containing PII is accessed by unauthorized parties. Customer data used to train AI is breached. Proprietary data used in AI models is stolen by competitors. Employee data used in HR AI is leaked.
Why this is different: This looks like a data breach, but it’s more complex. The data is embedded in AI models. You need to assess if the AI itself is compromised. You may need to retrain models. You need to evaluate if AI outputs are now tainted. You have to determine notification obligations.
Your cyber IR plan covers data breaches but not AI-specific complications.
Incident Type 4: Model Poisoning or Adversarial Attacks
Someone manipulates your AI to produce harmful outputs.
Examples: Attackers inject malicious data to bias your AI. Adversarial inputs cause your AI to misclassify critical decisions. Your AI is manipulated to bypass security controls. External actors corrupt your model through feedback loops.
Why this is different: The attack vector isn’t traditional hacking. It’s exploiting how AI learns and makes decisions. You need AI expertise to detect it. You need to assess how much damage was done. You may need to roll back to previous model versions.
Your cyber IR plan doesn’t cover AI-specific attack vectors.
Incident Type 5: Third-Party AI Failures
AI systems you don’t control but depend on fail or cause harm.
Examples: Your vendor’s AI experiences a major failure affecting your customers. The cloud AI services you use produce wrong results. API-based AI tools you’ve integrated cause compliance violations. SaaS platforms with embedded AI create liability for you.
Why this is different: You didn’t build the AI. You may not even know exactly how it works. But you’re still liable for the harm. You need to coordinate with vendors. You need to assess your contractual protections. You may need to switch providers immediately.
Your cyber IR plan assumes you control the systems that fail.
Why “Turn It Off and Investigate” Isn’t Enough
When a cybersecurity incident happens, the response is often: contain it immediately. Shut down affected systems. Preserve evidence. Investigate.
With AI incidents, it’s more complicated.
Can you even turn it off? If your AI is embedded in critical business processes, shutting it down may cause more harm than leaving it running. If you turn off your fraud detection AI, you expose yourself to fraud. If you turn off your medical AI, you can’t treat patients. If you turn off your supply chain AI, operations stop.
What if you can’t tell what went wrong? AI systems are often opaque. You may not be able to quickly determine what failed or why. Was it bad training data? A model bug? Adversarial input? Normal operation that happens to look wrong? You need time to investigate. But harm is happening while you investigate.
What if fixing it creates new problems? Rolling back to a previous AI version may reintroduce old bugs. Retraining the model may take weeks. Implementing human review may create bottlenecks. Switching to manual processes may not be feasible at scale.
What if the damage is already done? Unlike a data breach, where you can contain future access, AI may have already made thousands of bad decisions. Those decisions have already affected people. Turning off the AI doesn’t undo the harm.
This is why AI incident response requires a different playbook.
What AI Incident Response Actually Requires
When an AI incident happens, here’s what you actually need to do:
Hour 1: Initial Assessment
Contain the immediate harm. Stop the AI from making new decisions if possible. If not, implement human oversight for all new AI outputs. Document exactly what’s happening.
Identify the scope. How many decisions did the AI make? Who was affected? What time period? What was the nature of the failure?
Assemble your response team. You need technical experts (to assess the AI), legal counsel (to evaluate liability), compliance (to assess regulatory obligations), and business leaders (to make decisions about operations).
This is not just a tech problem or just a legal problem. It’s both.
Hours 2-24: Investigation and Legal Assessment
Technical investigation: What went wrong with the AI? Was it a bug, bad data, adversarial attack, or normal operation? Can you reproduce the failure? Do you have logs and evidence?
Legal assessment: What laws apply to this incident? Do you have notification obligations? What’s your potential liability? Are there regulatory reporting requirements? Could this trigger investigations or lawsuits?
Impact analysis: Who was harmed and how? Do affected individuals have legal claims? What’s the scope of potential damages? Are there patterns that suggest discrimination?
You need documentation of all of this. Because regulators and plaintiffs’ lawyers will ask.
Days 2-7: Remediation and Communication
Fix the AI or replace it. Depending on what went wrong, you may need to retrain the model, roll back to a previous version, implement additional controls, or switch to a different system entirely.
Notify affected parties. Depending on the incident type and applicable laws, you may need to notify affected individuals, regulators, business partners, or the public. Your notification should explain what happened, what harm occurred, and what you’re doing about it.
Document everything. Your investigation findings. Your remediation steps. Your decision-making process. This documentation is critical for regulatory defense and litigation.
Weeks 2-12: Ongoing Response
Monitor for recurrence. Is the problem actually fixed? Are you seeing similar patterns? Do you need additional controls?
Address regulatory inquiries. Expect questions from regulators. Be prepared to show what happened, what you did about it, and what you’re doing to prevent recurrence.
Prepare for litigation. If individuals were harmed, expect lawsuits. Your documentation and response will be critical to your defense.
The Mistakes Companies Make in the First 24 Hours
I’ve seen companies make the same mistakes when AI incidents happen:
Mistake 1: Treating it as just a tech problem. They let IT handle it without involving legal and compliance. Then they realize too late that there are regulatory obligations and legal exposure.
Mistake 2: Focusing only on fixing the AI. They spend all their time on technical remediation and ignore legal assessment, notification obligations, and documentation. Then they get hit with regulatory penalties for failing to report.
Mistake 3: Not preserving evidence. They make changes to the AI, delete logs, or overwrite data before documenting what happened. Then they can’t prove what went wrong or defend their actions.
Mistake 4: Delayed communication. They wait too long to notify affected parties or regulators because they want to “fully understand the problem first.” But many laws require prompt notification. Delay increases penalties.
Mistake 5: Inadequate documentation. They don’t document their investigation, decision-making, or remediation. Then, in litigation or regulatory proceedings, they can’t show they acted reasonably.
All of these mistakes are avoidable. But only if you have an AI-specific incident response plan before the incident happens.
What Your AI Incident Response Plan Must Include
Here’s what a real AI incident response plan looks like:
AI Incident Classification: Clear definitions of what constitutes an AI incident. Severity levels. Escalation triggers.
Response Team Structure: Who needs to be involved for each incident type. Technical, legal, compliance, business leadership, PR. Clear roles and responsibilities.
Technical Response Procedures: How to assess what went wrong. How to preserve evidence. When to shut down AI vs. implement additional controls. How to roll back or retrain models.
Legal Assessment Framework: What laws apply to different incident types. What notification obligations exist. How to assess liability. When to involve outside counsel.
Notification Protocols: Who needs to be notified and when. Affected individuals, regulators, business partners, the public. Template communications.
Documentation Requirements: What to document at each stage. Investigation findings, decision-making, remediation steps. Evidence preservation.
Communication Plans: Internal communication (who needs to know what and when). External communication (customers, partners, media, regulators). Message consistency.
Testing and Updates: Regular tabletop exercises. Plan updates as AI systems and regulations evolve.
Stop Waiting for an AI Incident to Create Your Plan
Most companies don’t think about AI incident response until they’re in the middle of an incident.
By then, it’s too late. You’re making critical decisions under pressure without a framework. You’re missing notification deadlines. You’re creating more legal exposure.
The companies that respond well to AI incidents are the ones that are prepared before they happen.
Schedule a 30-minute AI Incident Response Assessment where we’ll:
- Review your current incident response capabilities
- Identify gaps in your AI incident coverage
- Show you what an AI-specific IR plan looks like
- Give you templates and frameworks to build your plan
We’ve helped dozens of companies develop AI incident response plans that actually work when incidents happen.
Because the time to figure out AI incident response isn’t when your AI just harmed 10,000 people.
It’s now, while you have time to prepare.
Brian Alexander
Founding Partner, VisionAI+ Consulting Group
Former General Counsel, AI Engineering Firm
Certified ISO 42001 Lead Auditor
brian@visionaiconsult.com | 877-424-7853