AI Incident Response Playbooks for Compliance Teams

Table of Contents

1. Why AI Incident Response Playbooks Matter for Compliance
2. Understanding the Compliance Landscape
3. Core Components of an AI Incident Response Playbook
4. Building Detection and Classification Controls
5. Evidence Preservation and Chain of Custody
6. Escalation Workflows and Governance
7. Containment Strategies for AI Systems
8. Post-Incident Analysis and Remediation
9. Audit Readiness and Documentation
10. Implementation Roadmap

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Why AI Incident Response Playbooks Matter for Compliance

AI systems don’t fail in isolation. When a machine learning model produces biased outputs, when a language model hallucination reaches a customer, or when an autonomous agent makes an unexpected decision, the impact cascades across your organisation, your customers, and your audit scope.

Compliance teams—especially those preparing for SOC 2, ISO 27001, or industry-specific audits—now face a new class of incident that traditional incident response playbooks weren’t designed to handle. These aren’t just security breaches. They’re operational failures, data quality issues, model drift, adversarial inputs, and emergent behaviours that sit at the intersection of technology, business risk, and regulatory obligation.

The stakes are real. A security audit examiner will ask: What happens when your AI system fails? How do you detect it? Who gets notified? What evidence do you preserve? How do you prevent it happening again? If your answer is “we haven’t thought about that,” you’ve just identified a control gap that auditors will flag as a finding.

AI incident response playbooks solve this by giving your compliance team, engineering team, and leadership a shared language and repeatable process for detecting, responding to, and learning from AI-specific incidents. They also create the audit trail and documentation that auditors expect to see.

This guide walks you through the practical steps we use at PADISO when building AI incident response playbooks for customers preparing for compliance audits. We focus on what auditors actually look for, the controls that matter, and the evidence patterns that demonstrate you take AI risk seriously.

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Understanding the Compliance Landscape

What Auditors Expect to See

When an auditor reviews your AI incident response capability, they’re assessing three things:

First, governance. Do you have a defined process? Is it documented? Does leadership understand the process? Are roles and responsibilities clear? Auditors want to see that incident response isn’t ad hoc—it’s intentional and owned.

Second, operational readiness. Can your team actually execute the playbook? Do you have the tools, training, and communication channels in place? Auditors will ask whether your incident response team has tested the playbook recently. If you can’t point to a tabletop exercise or a real incident that was handled according to the playbook, auditors will question whether the playbook is real or just a document.

Third, evidence and learning. When an incident occurs, do you preserve the evidence? Can you show auditors what went wrong and what you changed to prevent it? Auditors expect to see a log of incidents, the investigation findings, and the remediation actions taken.

AI systems introduce a fourth dimension: explainability and traceability. Traditional security incidents are about “someone accessed something they shouldn’t.” AI incidents are often about “the system made a decision we didn’t expect.” Auditors increasingly expect you to be able to explain what the AI system did, why it did it, and whether that decision was correct. This requires controls that traditional incident response playbooks don’t address.

Regulatory Frameworks and Standards

Multiple frameworks now address AI incident response, and they overlap in useful ways:

NIST’s AI Risk Management Framework (AI RMF 1.0) provides a governance-first approach to identifying, measuring, managing, and governing AI risks. It maps to SOC 2 trust service criteria and is increasingly referenced in auditor guidance. The framework emphasises that incident response is part of the broader AI risk management lifecycle, not separate from it.

ISO/IEC 27035-1: Information security incident management sets the standard for incident management procedures and governance. If you’re pursuing ISO 27001 compliance, your AI incident response playbook needs to align with this standard. The key requirement is that your incident management process covers all types of incidents, including those involving AI systems.

NIST’s Computer Security Incident Handling Guide (SP 800-61 Rev. 2) remains the foundational reference for incident response phases: preparation, detection, analysis, containment, eradication, recovery, and lessons learned. This structure applies to AI incidents as well, though the details differ.

For regulated industries—financial services, healthcare, insurance—your regulator may have specific expectations. In Australia, APRA’s CPS 234 guidance on technology risk management now explicitly addresses AI systems. ASIC’s RG 271 guidance on algorithmic decision-making includes expectations around testing, monitoring, and incident response. When you’re building an AI incident response playbook, align it with your regulator’s expectations from the start.

The common thread across all these frameworks: incident response is a control, not a feature. Auditors are checking whether you have one, whether it works, and whether you’re actually using it.

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Core Components of an AI Incident Response Playbook

The Five-Phase Structure

A practical AI incident response playbook follows five phases:

Phase 1: Preparation. Before an incident occurs, you’ve defined what constitutes an AI incident, trained your team, configured monitoring, and established communication channels. This is where most organisations fall short. Preparation isn’t glamorous, but it’s where auditors spend most of their time.

Phase 2: Detection and Classification. An alert fires, a customer complains, or a team member notices something odd. Your playbook defines what gets reported, to whom, and how quickly. Classification determines whether this is a security incident, a data quality issue, a model performance degradation, or something else entirely.

Phase 3: Analysis and Investigation. You gather evidence, understand what happened, assess impact, and determine root cause. This is where the audit trail matters most. Auditors will ask you to walk through a recent incident and show them the investigation notes, the evidence preserved, and the decisions made.

Phase 4: Containment and Remediation. You stop the harm, restore normal operation, and implement fixes. For AI systems, containment might mean rolling back a model, disabling a feature flag, or adding a human review step. Remediation might mean retraining the model, fixing data quality issues, or changing the system design.

Phase 5: Post-Incident Review and Prevention. You analyse what went wrong, document lessons learned, and implement changes to prevent recurrence. This is where compliance teams show auditors that you’re actually learning from incidents.

Each phase has specific outputs, owners, and success criteria. Your playbook documents all of this.

Defining What Counts as an AI Incident

Not every model prediction that’s wrong is an incident. Not every customer complaint requires an incident response. Your playbook needs clear criteria for what triggers the incident response process.

An AI incident typically involves one or more of these:

• Unexpected system behaviour. The AI system produces outputs that violate its specification, business logic, or regulatory requirements. This includes hallucinations, adversarial outputs, or decisions that don’t align with documented policies.
• Data quality degradation. The training data, inference data, or reference data used by the AI system becomes corrupted, stale, or biased. This can cause the model to make incorrect decisions.
• Model performance degradation. The model’s accuracy, precision, recall, or other performance metrics fall below acceptable thresholds. This might indicate model drift, data drift, or a change in the input distribution.
• Unauthorised access or modification. Someone accesses the model, training data, or inference pipeline without authorisation, or modifies them without approval.
• Compliance violation. The AI system violates a regulatory requirement, contractual obligation, or internal policy. This includes bias violations, explainability failures, or decisions that should have triggered a human review.
• Security incident involving AI systems. A traditional security incident (e.g., a breach, a denial of service, a privilege escalation) affects an AI system or its data.

Your playbook should list these categories and provide examples relevant to your organisation. This ensures that when an incident occurs, your team knows whether to trigger the playbook or handle it through a different process.

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Building Detection and Classification Controls

Monitoring and Alerting for AI Systems

You can’t respond to incidents you don’t detect. Detection requires monitoring across multiple dimensions:

Performance monitoring. Track model accuracy, precision, recall, F1 score, and other metrics relevant to your use case. Set thresholds that trigger alerts when performance degrades. For example, if your fraud detection model’s precision drops below 85%, that’s an alert. If your recommendation engine’s click-through rate drops 20% week-over-week, that’s an alert.

The challenge: performance monitoring requires a ground truth signal. For some models, you get this signal immediately (e.g., a customer clicks or doesn’t click). For others, the signal arrives days or weeks later (e.g., whether a loan defaulted). Your playbook needs to account for this lag and define what “degradation” means in your specific context.

Data quality monitoring. Track the quality of data flowing into your model. This includes schema validation (are the expected columns present?), distribution monitoring (has the input distribution changed?), and anomaly detection (are there unexpected values?). Tools like Great Expectations or custom monitoring pipelines can flag data quality issues before they affect model predictions.

Explainability and interpretability monitoring. For regulated use cases, you need to monitor whether the model’s explanations are consistent, reliable, and trustworthy. This might involve tracking SHAP values, attention weights, or other explainability metrics. If the model’s explanations suddenly become inconsistent, that’s a signal of a potential problem.

Fairness and bias monitoring. Track demographic parity, equalized odds, or other fairness metrics relevant to your use case. If the model’s performance varies significantly across demographic groups, or if this variation changes over time, that’s an alert. This is especially important for regulated industries where bias is a compliance concern.

Operational monitoring. Track latency, throughput, error rates, and other operational metrics. If the model serving pipeline suddenly has high latency or high error rates, that affects your ability to use the model at all.

Security monitoring. Monitor for unauthorised access to the model, training data, or inference pipeline. This includes monitoring API calls, data access logs, and model serving logs for suspicious patterns.

Your playbook should specify which metrics you monitor, what thresholds trigger alerts, and who gets notified when an alert fires. This prevents alert fatigue (too many false alarms) and ensures that real incidents get attention.

Classification and Triage

Once an alert fires or an incident is reported, your team needs to classify it quickly. Classification determines the severity, the response team, and the urgency.

A simple classification framework:

• Critical. The AI system is down or producing severely incorrect outputs. Impact is immediate and widespread. Examples: a model serving pipeline is down, a model is producing outputs that violate regulatory requirements, a security breach affects the model or its data.
• High. The AI system is degraded or producing incorrect outputs for a subset of users. Impact is significant but not immediate. Examples: model performance has degraded 15%, a data quality issue affects 10% of inferences, an unauthorised user accessed model logs.
• Medium. The AI system is showing early signs of problems. Impact is limited. Examples: model performance has degraded 5%, a data quality issue affects 1% of inferences, a monitoring alert indicates potential drift.
• Low. The AI system is functioning normally but there’s a potential issue to investigate. Examples: a monitoring alert fires but manual inspection shows no problem, a customer complaint about a single prediction.

Your playbook defines the response time for each severity level. Critical incidents might require a response within 15 minutes. High incidents might require a response within 2 hours. Medium incidents might require a response within 24 hours. Low incidents might be batched and reviewed weekly.

Classification also determines who investigates. Critical incidents might require your CTO, your head of data science, and your compliance lead. Low incidents might be handled by a junior engineer. Your playbook should specify the team composition for each severity level.

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Evidence Preservation and Chain of Custody

What Evidence Matters

When auditors review an incident, they want to see evidence that demonstrates what happened, who was involved, and what you did about it. For AI systems, the evidence is different from traditional security incidents.

Model artifacts. Preserve the model code, the model weights, the training data, and the inference data. If the incident involves a model producing unexpected outputs, you need to be able to reproduce those outputs later. This requires preserving the exact version of the code and the exact weights that were in production when the incident occurred.

Data snapshots. If the incident involves data quality issues, preserve a snapshot of the data at the time of the incident. This allows you to investigate what went wrong and whether the data quality issue affected other systems.

Logs and traces. Preserve application logs, model serving logs, data pipeline logs, and any other logs that might help you understand what happened. For AI systems, this includes logs of model predictions, feature values, and decision paths.

Monitoring data. Preserve the monitoring metrics and alerts that led to the incident being detected. This helps you understand whether your monitoring caught the incident early and whether the monitoring thresholds were appropriate.

Communication records. Preserve emails, Slack messages, and meeting notes related to the incident. This creates a record of who knew what and when, which is important for auditors.

Investigation notes. As your team investigates, document their findings, hypotheses, and decisions. This creates an audit trail of the investigation process.

Your playbook should specify what evidence to preserve, how long to preserve it, and where to store it. For compliance purposes, evidence should be stored in a way that prevents tampering and demonstrates chain of custody.

Chain of Custody and Audit Trail

Auditors care about chain of custody because they want to ensure that evidence hasn’t been tampered with or selectively deleted. Your playbook should define:

• Who can access the evidence. Typically, only the incident response team and compliance team should have access. Access should be logged.
• How evidence is stored. Evidence should be stored in a tamper-evident way, ideally with immutable storage (e.g., write-once storage, or a system where deletions are logged).
• How evidence is transferred. If evidence moves from one system to another, this transfer should be logged and documented.
• How evidence is retained. You should have a retention policy that specifies how long to keep evidence. For compliance purposes, this is typically at least 3 years, but your auditor might require longer.

In practice, this means:

• When an incident is detected, create an incident record in your incident management system (e.g., Jira, ServiceNow, or a custom system). This record becomes the source of truth for the incident.
• As your team investigates, all findings, decisions, and actions are logged in the incident record. This creates an audit trail.
• When you preserve evidence (e.g., a data snapshot, a model artifact), document where the evidence is stored and how to access it. Link this documentation to the incident record.
• When the incident is closed, archive the incident record and the evidence. Archive in a way that prevents deletion or modification.

This approach ensures that auditors can trace the incident from detection through resolution and see exactly what evidence was collected and what decisions were made.

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Escalation Workflows and Governance

Defining Escalation Paths

Not all incidents are equal. Some can be handled by a junior engineer with a quick fix. Others require involvement from leadership, legal, and external parties. Your playbook should define escalation paths based on incident severity and type.

A typical escalation structure:

• Tier 1 (Detection and Triage). A monitoring system or a team member detects the incident and classifies it. For low and medium severity incidents, Tier 1 might be sufficient to handle the incident.
• Tier 2 (Investigation and Containment). For high severity incidents, escalate to a dedicated incident response team. This team includes engineers, data scientists, and security specialists. They investigate the incident, determine root cause, and implement containment measures.
• Tier 3 (Executive and External Escalation). For critical incidents, escalate to executive leadership. This might include your CTO, your CEO, your general counsel, and your compliance officer. At this level, decisions are made about customer notification, regulatory reporting, and external communications.

Your playbook should specify the criteria for each escalation level and the communication protocol for each level. For example:

• Escalate to Tier 2 if: The incident affects more than 100 users, or the incident involves unauthorised access to sensitive data, or the incident violates a regulatory requirement.
• Escalate to Tier 3 if: The incident affects more than 10,000 users, or the incident involves a security breach, or the incident requires notification to regulators or customers.

Your playbook should also specify the communication protocol. Who gets notified? How quickly? Through what channel? For critical incidents, you might have a dedicated Slack channel, a conference bridge, and a daily standup. For lower severity incidents, you might have an email thread and a weekly review.

Roles and Responsibilities

Each role in the incident response process has specific responsibilities. Your playbook should define these clearly:

• Incident Commander. Owns the incident from detection through closure. Coordinates the response team, makes decisions about containment and remediation, and communicates status to leadership. For critical incidents, this might be your CTO. For lower severity incidents, this might be a senior engineer.
• Technical Lead. Leads the investigation and technical response. Determines root cause, proposes containment measures, and implements fixes.
• Data Science Lead. For incidents involving AI systems, leads the analysis of model behaviour, data quality, and model performance. Determines whether the model needs to be retrained, rolled back, or modified.
• Security Lead. Assesses whether the incident involves a security breach or unauthorised access. Determines whether external parties need to be notified.
• Compliance Lead. Assesses whether the incident violates a regulatory requirement. Determines whether the incident needs to be reported to regulators. Ensures that evidence is preserved and documented.
• Communications Lead. Manages external communications. Determines what to communicate to customers, partners, and the public. Coordinates timing and messaging.

Your playbook should specify who fills each role, what their responsibilities are, and what decision-making authority they have. This prevents confusion during an incident and ensures that decisions are made by the right people.

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Containment Strategies for AI Systems

Immediate Containment Actions

When an AI incident is detected, the first priority is to stop the harm. For AI systems, containment strategies differ from traditional security incidents.

Model rollback. If the incident involves a recently deployed model, rolling back to the previous version might be the fastest way to restore normal operation. Your playbook should document how to perform a rollback, how long it takes, and what data needs to be preserved for investigation.

Feature flag disable. If the AI system is accessed through a feature flag, disabling the flag immediately stops the AI system from being used. This is faster than a rollback and allows you to investigate while the system is offline.

Inference throttling. If the incident involves a model producing incorrect outputs for a subset of users, you might throttle inference for that subset. For example, if the model is producing biased outputs for a particular demographic group, you might route that group’s requests to a human reviewer instead of the model.

Human review layer. Add a human review step between the model and the user. This prevents incorrect outputs from reaching users while you investigate. For example, if your fraud detection model is producing too many false positives, you might require a human reviewer to approve high-risk decisions.

Data quarantine. If the incident involves corrupted or poisoned data, quarantine that data to prevent it from affecting the model or other systems. This might involve deleting the data, isolating it in a separate environment, or marking it as invalid.

Model retraining pause. If the incident involves model drift or data drift, pause any automated retraining until you’ve investigated and fixed the underlying issue. Continuing to retrain on bad data will only make the problem worse.

Your playbook should specify which containment actions apply to your AI systems and when to use each one. The goal is to make containment decisions quickly—typically within 15 minutes for critical incidents.

Assessing Business Impact

As you contain the incident, assess the business impact. This helps you prioritise response actions and communicate with stakeholders.

User impact. How many users are affected? Are they internal users, paying customers, or both? What is the impact on their experience or their business?

Data impact. What data is affected? Is it sensitive data (e.g., personally identifiable information, financial data)? Is it training data, inference data, or reference data? Could the data be used to harm users or violate their privacy?

Compliance impact. Does the incident violate a regulatory requirement? Does it trigger a notification requirement? Could it result in a regulatory fine or enforcement action?

Reputational impact. If the incident becomes public, what would be the impact on your brand and customer trust? Is this the kind of incident that would make headlines?

Financial impact. What is the cost of the incident? Include direct costs (e.g., investigation, remediation, notification) and indirect costs (e.g., lost revenue, customer churn, regulatory fines).

Your playbook should include a template for documenting business impact. This information is important for decision-making and for auditors who want to understand whether you’re taking incidents seriously.

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Post-Incident Analysis and Remediation

Root Cause Analysis

Once you’ve contained the incident, the next step is to understand what went wrong. Root cause analysis (RCA) is the process of identifying the underlying cause of the incident, not just the immediate trigger.

For AI systems, root cause analysis often reveals multiple contributing factors:

• Model factors. The model itself might have a flaw. This could be a bug in the code, a problem with the training process, or a fundamental limitation of the approach.
• Data factors. The training data, inference data, or reference data might be the problem. This could be missing data, incorrect data, stale data, or biased data.
• Process factors. The process for developing, testing, deploying, or monitoring the model might be the problem. This could be inadequate testing, inadequate monitoring, or inadequate change management.
• Infrastructure factors. The infrastructure for running the model might be the problem. This could be a hardware failure, a network issue, or a configuration error.
• Human factors. A person might have made a mistake. This could be a developer deploying an untested model, a data engineer corrupting the data, or a security team member granting excessive access.

Your playbook should include a structured RCA process. The SANS Incident Handler’s Handbook provides a useful framework: identify the facts, determine the sequence of events, identify the contributing factors, and identify the root cause.

For AI systems, RCA should also include analysis of the model’s decision-making process. Tools like SHAP, LIME, or attention visualisation can help you understand why the model made the decisions it did and whether those decisions were correct.

Remediation and Prevention

Once you’ve identified the root cause, the next step is to fix it and prevent it from happening again. Remediation might involve:

• Model retraining. If the root cause is model drift or data drift, retrain the model on fresh data.
• Data cleaning. If the root cause is data quality issues, clean the data and implement data quality controls.
• Code fixes. If the root cause is a bug in the model code, fix the bug and implement testing to prevent similar bugs.
• Process improvements. If the root cause is a process problem, improve the process. This might include adding automated testing, adding monitoring, or improving change management.
• Infrastructure improvements. If the root cause is an infrastructure problem, fix the infrastructure. This might include adding redundancy, improving monitoring, or upgrading hardware.

Your playbook should include a remediation template that documents:

• The root cause
• The remediation action
• Who is responsible for the remediation
• The deadline for the remediation
• How you’ll verify that the remediation is effective

For compliance purposes, the remediation template is important evidence that you’re actually learning from incidents and making changes to prevent recurrence.

Post-Incident Review

After the incident is resolved and remediation is complete, conduct a post-incident review (PIR). The goal is to identify lessons learned and make process improvements.

Your PIR should answer these questions:

• What was the incident? A brief summary of what happened.
• When was it detected? How long from when the incident occurred to when it was detected? Could detection have been faster?
• What was the impact? How many users, how much data, how much cost?
• What was the root cause? The underlying cause, not just the trigger.
• What was the remediation? What was done to fix the incident?
• What went well? What aspects of the incident response process worked well?
• What could be improved? What aspects of the incident response process could be better?
• What changes will we make? Specific action items to prevent recurrence.

Your playbook should require a PIR for all incidents above a certain severity level (e.g., high and critical). PIRs should be documented and shared with relevant teams. For compliance purposes, PIRs are important evidence that you’re continuously improving your incident response process.

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Audit Readiness and Documentation

Building Your Audit Evidence Library

Auditors will want to see evidence that your AI incident response playbook is real and that you’re actually using it. This means documenting everything:

• The playbook itself. A written document that describes your incident response process, roles, responsibilities, and escalation paths. The playbook should be version controlled and regularly reviewed.
• Incident records. For each incident, a record that documents the incident, the investigation, the remediation, and the lessons learned. Auditors will typically review 5-10 recent incidents in detail.
• Monitoring configuration. Documentation of what you monitor, what thresholds trigger alerts, and why those thresholds were chosen. This shows that you have proactive detection in place.
• Training records. Documentation that your incident response team has been trained on the playbook and has practiced executing it. This might include attendance records from training sessions or tabletop exercises.
• Communication logs. For critical incidents, logs of the communication that occurred during the incident response. This shows that escalation and decision-making happened as documented.
• Remediation tracking. Evidence that remediation actions were completed and that they were effective. This might include code reviews, testing results, or monitoring data showing that the issue has been resolved.

Your playbook should specify what documentation is required for each incident and how long to retain it. For compliance purposes, retain incident records for at least 3 years, though your auditor might require longer.

Aligning with Audit Frameworks

When you’re building your AI incident response playbook, align it with the audit frameworks relevant to your organisation. If you’re pursuing SOC 2 compliance, align with the SOC 2 trust service criteria. If you’re pursuing ISO 27001 compliance, align with ISO 27035-1.

For SOC 2, the relevant criteria are typically:

• CC7.2: The organisation detects, investigates, and responds to security incidents by executing a coordinated incident response process.
• CC7.3: The organisation identifies, develops, and implements activities to recover from identified security incidents.

Your AI incident response playbook should directly address these criteria. Document that you have a coordinated incident response process, that you detect incidents, that you investigate them, and that you recover from them.

For ISO 27001, the relevant control is:

• A.16.1: Event management and reporting. The organisation shall establish an event management process and report security events and weaknesses.

Your playbook should document how you manage and report AI-related security events.

When you’re working with an auditor, share your playbook early and ask for feedback. Auditors can often identify gaps or areas where your playbook doesn’t align with the audit framework. It’s better to identify these gaps before the audit than during it.

If you’re working with PADISO on your security audit, we can help you align your AI incident response playbook with the audit framework and build the evidence library that auditors expect to see. We’ve helped dozens of organisations move from “we don’t have an AI incident response process” to “we have a documented, tested, and audited AI incident response process” in 8-12 weeks.

Preparing for Auditor Questions

During the audit, auditors will ask detailed questions about your incident response process. Prepare for these questions by documenting your answers in advance:

• What is your definition of an AI incident? Have a clear definition and examples.
• How do you detect AI incidents? Describe your monitoring and alerting process.
• What is your escalation process? Describe the roles, responsibilities, and decision-making authority at each level.
• How do you preserve evidence? Describe your chain of custody process.
• Can you walk me through a recent incident? Have 2-3 recent incidents documented in detail. Auditors will typically ask you to walk through one of them step by step.
• How do you test your incident response process? Describe your tabletop exercises or past incidents where you executed the playbook.
• How do you prevent incidents from recurring? Describe your remediation and root cause analysis process.

Prepare written answers to these questions and practice walking through them with your team. This preparation will make the audit smoother and more likely to result in a successful outcome.

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Implementation Roadmap

Phase 1: Foundation (Weeks 1-4)

Week 1: Define the Scope

Start by defining what you’re protecting. Map your AI systems: which models are in production, what do they do, what data do they use, and who depends on them? Prioritise the models that have the highest business impact or the highest compliance risk. You don’t need to build an incident response playbook for every model immediately—start with the highest-priority models.

Document your compliance requirements. Are you pursuing SOC 2 compliance? ISO 27001? Do you have industry-specific requirements (e.g., APRA CPS 234 for financial services)? Understanding your compliance requirements will shape your playbook.

Week 2: Define Incident Categories

Working with your engineering, data science, and compliance teams, define what constitutes an AI incident for your organisation. Use the categories we outlined earlier (unexpected behaviour, data quality degradation, model performance degradation, unauthorised access, compliance violation, security incident) as a starting point. Add examples specific to your models.

Define severity levels. What makes an incident critical versus high versus medium? What’s the response time for each level?

Week 3: Draft the Playbook

Draft a first version of your incident response playbook. Include:

• Definition of AI incidents
• Severity levels and escalation criteria
• Roles and responsibilities
• Escalation paths and communication protocols
• Detection and monitoring requirements
• Containment actions
• Investigation and RCA process
• Remediation and prevention process
• Documentation and retention requirements

Use the templates and frameworks we’ve outlined in this guide. Don’t try to make it perfect—the goal is to get something down on paper that your team can review and iterate on.

Week 4: Review and Refine

Share the draft playbook with your engineering, data science, compliance, and security teams. Get their feedback. Refine the playbook based on their input. The goal is to build something that your team actually believes in and will actually use.

Phase 2: Build Monitoring and Detection (Weeks 5-8)

Week 5: Identify Monitoring Requirements

For each of your high-priority AI models, identify what you need to monitor. Use the monitoring dimensions we outlined earlier: performance, data quality, explainability, fairness, operations, and security. For each dimension, define the metrics, the thresholds, and the alerting mechanism.

Week 6: Implement Monitoring

Work with your engineering and data science teams to implement the monitoring. This might involve configuring existing monitoring tools, building custom monitoring pipelines, or integrating with third-party monitoring services. For regulated environments, ensure that monitoring is compliant with your regulatory requirements.

Week 7: Configure Alerting

Configure alerting so that when a threshold is breached, the right people are notified. Set up alert routing so that critical alerts go to your incident commander, high alerts go to the on-call engineer, and medium alerts are batched and reviewed daily.

Week 8: Test and Validate

Test your monitoring and alerting. Simulate incidents (e.g., deploy a model that performs poorly, corrupt some training data) and verify that your monitoring detects them and that alerts are sent to the right people. Iterate based on what you learn.

Phase 3: Train and Test (Weeks 9-12)

Week 9: Train Your Team

Conduct training sessions with your incident response team. Walk through the playbook, explain the roles and responsibilities, and practice executing the playbook. Use real examples from your organisation where possible.

Week 10: Conduct a Tabletop Exercise

Conduct a tabletop exercise where your team walks through a simulated incident from detection through closure. This doesn’t require any actual system changes—it’s just a discussion of what would happen if a certain incident occurred. Tabletop exercises are valuable for identifying gaps in your playbook and training your team.

Week 11: Refine Based on Exercise Feedback

Based on the tabletop exercise, refine your playbook. Update documentation, clarify roles and responsibilities, and address any gaps that were identified.

Week 12: Prepare Audit Evidence

Begin documenting your audit evidence. Create incident record templates, monitoring configuration documentation, training records, and communication logs. Prepare for auditor questions.

Phase 4: Continuous Improvement (Ongoing)

Quarterly Reviews

Review your incident response playbook quarterly. Have there been any changes to your AI systems, your compliance requirements, or your team? Update the playbook accordingly. Schedule a review meeting with your incident response team to discuss lessons learned and process improvements.

Annual Tabletop Exercises

Conduct at least one tabletop exercise per year. Use this to train new team members and to test your playbook. Each year, focus on a different type of incident so that you cover the full range of scenarios.

Incident Post-Mortems

Every time a real incident occurs, conduct a post-incident review. Document the lessons learned and implement action items to prevent recurrence. Share the findings with your team and update your playbook if needed.

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Key Takeaways

Building an AI incident response playbook is not a one-time project—it’s a continuous process of learning and improvement. Here’s what matters most:

Start with clarity. Define what constitutes an AI incident for your organisation. Define severity levels and escalation criteria. Get alignment from your engineering, data science, compliance, and security teams.

Invest in detection. You can’t respond to incidents you don’t detect. Build monitoring across performance, data quality, explainability, fairness, operations, and security. Set thresholds that trigger alerts. Test your monitoring regularly.

Document everything. Your playbook should be a living document that your team actually uses. Document roles, responsibilities, escalation paths, containment actions, and investigation processes. Update it based on real incidents and lessons learned.

Preserve evidence. When an incident occurs, preserve the evidence. Document the investigation, the root cause, and the remediation. This is what auditors will want to see.

Learn and improve. After each incident, conduct a post-incident review. Identify what went well and what could be better. Implement action items to prevent recurrence. Use real incidents as an opportunity to train your team and improve your process.

Align with auditors. If you’re pursuing SOC 2, ISO 27001, or other compliance certifications, align your playbook with the audit framework from the start. Share your playbook with your auditor early and get feedback. This will make the audit smoother and more likely to result in a successful outcome.

For organisations building AI systems at scale, an incident response playbook isn’t optional—it’s a foundational control that auditors expect to see. When you’re ready to move from “we should have a playbook” to “we have a documented, tested, and audited playbook,” PADISO can help.

We work with founders, CTOs, and compliance teams to build AI incident response playbooks that actually work. We’ve helped organisations across financial services, insurance, healthcare, and SaaS move from zero to audit-ready in 8-12 weeks. Our approach combines practical incident response expertise with deep knowledge of compliance frameworks like SOC 2 and ISO 27001.

If you’re in Sydney and building AI systems, our Sydney-based AI advisory team can help you build a playbook that fits your specific context. If you’re in regulated industries like financial services or insurance, we can help you align your playbook with your regulator’s expectations.

We can also help you with the broader compliance picture. If you’re preparing for a security audit and need fractional CTO support, our fractional CTO service can help you build the technical controls and governance that auditors expect. We use Vanta to automate evidence collection and streamline the audit process, so you can focus on building great AI products instead of chasing audit findings.

The incident response playbook is just one piece of the puzzle. But it’s a piece that matters—to your compliance audit, to your customers, and to your ability to ship AI systems with confidence.