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CMP1797 Master AI Incident Response for Compliance and Operations Leaders

$199.00
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The Executive Diagnostic and Governance Toolkit

AI Incident Response for Compliance and Operations Leaders

Score your own function red, amber or green, find out which part is weakest, and walk into the next budget round able to defend what you want to fix. Built for leaders reviewing aI security is becoming a critical control function, not just a technical safeguard. Upwind’s valuation and Horizon3’s attack simulation focus show that AI-enabled threats are now assumed to be inevitable and high-impact. This means compliance and operations leaders will be held accountable for AI-related breaches even if the tools are sourced externally. The immediate question: Run a tabletop exercise this week simulating an AI model leaking regulated data and identify where your team lacks visibility.

$199 one-time
30-day money-back guarantee Verified against latest insights, updated access provided within 24h

Each order is checked and updated against the latest insights before delivery. That is why access takes up to 24 hours rather than being instant.

What you walk out with
A scored, ranked picture of your own function, and a defensible answer to what to fix first.
1 You stop guessing where you stand.
You finish with a score, not an opinion: every part of your function rated red, amber or green, with the weakest ranked first. Evidence: a Quick Scan for the shape of it, then seven domain assessments of 30 scored questions each, 210 in all, rolled into one scorecard, plus a maturity radar and a current-versus-target gap analysis.
2 You can defend the decision.
You walk into the budget round with the gap named, the owner named and done defined, instead of a case built on instinct. Evidence: project charter, scope statement, RACI, requirements traceability and work breakdown structure, pre-filled in your domain's language.
3 The work actually moves.
The month after the decision is already built, so nothing stalls waiting for someone to design a form. Evidence: more than 60 project templates across all five PMBOK process groups, plus runbooks, SOPs, a KPI framework, audit checklists and a risk matrix. 55 to 65 files in total.
4 You use it the day it lands.
No blank templates to interpret. Every workbook opens with what it is, who uses it, when, how, a 1 to 5 scoring guide, what good looks like, and a worked example you delete and type over.
The Quick Scan is one sitting. You will know your weakest area before the day is out.
Nothing in it is generic project management: the build rejects any file that could belong to another course. Updated after you enrol, so it reflects where the work stands now. The 144-chapter course is included behind it, for the parts you want to go deeper on.
An AI model just leaked regulated customer data. You’re on the call. No one knows who owns the response.

The situation this is built for

AI systems are now embedded in core services, yet no clear ownership exists for when they fail. Compliance leaders are being held accountable for breaches even when models are third-party or cloud-hosted. Tabletop exercises are not standardized. Runbooks are missing. Decision authority is unclear. The regulator is watching, and your team lacks visibility into the data, model behavior, and response chain.

Who this is for

The IT, operations, compliance, or service management lead responsible for risk, continuity, and regulatory outcomes in AI-enabled environments

Who this is not for

This is not for data scientists, AI developers, or security engineers focused on model tuning or code-level defenses. This is for leaders who own accountability when AI systems fail.

What you walk away with

  • Establish clear ownership and decision pathways for AI incidents
  • Run effective tabletop exercises that expose response gaps
  • Build an auditable incident response framework for regulators
  • Integrate AI risk into existing service continuity and compliance programs
  • Produce documented artifacts that satisfy audit requirements

How this maps to your situation

  • You inherit responsibility for AI incidents with no playbook
  • You must prove readiness to auditors or regulators
  • Your team failed a recent tabletop exercise
  • An AI model incident nearly occurred last quarter

Before vs. after

Before
AI incidents are treated as technical glitches, response is ad hoc, and compliance exposure is unmanaged.
After
Your team runs coordinated, auditable responses to AI failures with documented ownership and clear decision authority.

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters)
  • Downloadable templates and worked examples for every module
  • Hand-built implementation playbook delivered alongside course access
  • 30-day money-back guarantee

Delivery and format

  • Course and learning environment access provisioned within 24 hours of purchase
  • Hand-built implementation playbook delivered alongside course access

Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.

Time investment: Approximately 3 hours per module, designed to be completed alongside regular work. Most learners finish in 6–8 weeks.

If nothing changes
Without structured AI incident response, your organization risks regulatory fines, reputational damage, and loss of customer trust when inevitable AI failures occur. You will be held accountable even if the model was not built in-house.

How this compares to the alternatives

Unlike generic cybersecurity courses, this program focuses exclusively on the governance, coordination, and compliance decisions required when AI systems fail. It does not teach coding or model defense. It teaches how to lead when the model leaks data, generates harmful content, or violates regulations.

Also included: the full course, for when you want the reasoning behind a finding (12 modules, 144 chapters)

Depth reference. The diagnostic and the templates stand on their own; this is what to read when you want the reasoning behind a finding.

Module 1. Defining AI Incident Response Ownership
Clarify who owns the response when AI systems fail, especially in hybrid or third-party environments.
12 chapters in this module
  1. Identify the legal and operational owner of AI incidents
  2. Map AI use cases to existing compliance frameworks
  3. Determine accountability for third-party model failures
  4. Establish the chain of command during an AI incident
  5. Classify AI incidents by regulatory impact and urgency
  6. Define the roles of compliance, IT, and legal teams
  7. Document incident ownership in service agreements
  8. Create a RACI matrix for AI incident response
  9. Align AI response ownership with existing GRC structure
  10. Assess current ownership clarity with a quick audit
  11. Interview stakeholders to uncover response blind spots
  12. Deliver a signed incident ownership charter
Module 2. Assessing AI System Exposure
Inventory and evaluate all AI systems in use, focusing on data sensitivity and regulatory exposure.
12 chapters in this module
  1. Conduct a census of all active AI models in production
  2. Classify AI systems by data type and processing purpose
  3. Identify models handling regulated personal information
  4. Map AI data flows from input to output
  5. Determine where model outputs are stored or shared
  6. Assess third-party AI vendor compliance posture
  7. Document model dependencies and API integrations
  8. Evaluate model retraining schedules and data sources
  9. Flag models with autonomous decision-making authority
  10. Score AI systems using a risk exposure matrix
  11. Prioritize high-risk models for response planning
  12. Produce a living AI system exposure register
Module 3. Designing AI Incident Scenarios
Develop realistic, high-impact scenarios to test readiness and expose gaps in detection and response.
12 chapters in this module
  1. Select incident types based on regulatory impact
  2. Build a scenario where AI leaks protected health data
  3. Simulate unauthorized model access via API abuse
  4. Create a case of AI-generated misinformation at scale
  5. Design a prompt injection attack on customer service bots
  6. Model data poisoning during model retraining
  7. Outline a denial-of-service scenario for AI inference
  8. Craft a scenario involving model bias in hiring
  9. Simulate AI hallucination in financial reporting
  10. Develop a supply chain attack on model weights
  11. Test response to model drift affecting compliance
  12. Run a red team exercise on AI access controls
Module 4. Running AI Tabletop Exercises
Lead structured simulations that reveal gaps in coordination, visibility, and decision-making.
12 chapters in this module
  1. Define objectives for the first tabletop exercise
  2. Invite cross-functional participants from legal and IT
  3. Set ground rules for safe-to-fail simulation
  4. Distribute scenario briefs 48 hours in advance
  5. Facilitate role-based response discussions
  6. Track decisions made under time pressure
  7. Capture communication breakdowns in real time
  8. Identify missing data sources during simulation
  9. Document escalation paths that failed to activate
  10. Debrief with a structured after-action review
  11. Produce a gap analysis from exercise findings
  12. Prioritize improvements for next quarter
Module 5. Building AI Incident Runbooks
Create step-by-step response guides tailored to specific AI failure modes and organizational roles.
12 chapters in this module
  1. Define the structure of an AI incident runbook
  2. Outline initial detection and triage procedures
  3. Specify data preservation requirements for AI logs
  4. List required forensic data from model providers
  5. Create escalation checklists for legal notification
  6. Define communication templates for internal teams
  7. Draft customer notification language for AI breaches
  8. Include model rollback and disable procedures
  9. Add steps to isolate compromised AI endpoints
  10. Integrate runbooks with existing ITSM workflows
  11. Version control and audit trail for runbook changes
  12. Validate runbook usability with a dry run
Module 6. Establishing AI Monitoring Baselines
Set measurable thresholds for normal AI behavior to detect anomalies and trigger response.
12 chapters in this module
  1. Identify key performance indicators for AI models
  2. Define normal input distribution for trained models
  3. Monitor for unexpected output patterns or bias
  4. Set thresholds for API call volume and frequency
  5. Track model confidence score degradation over time
  6. Log all prompt and response pairs for auditability
  7. Establish baseline for model retraining intervals
  8. Detect unauthorized access to model endpoints
  9. Monitor for data leakage in AI-generated text
  10. Flag anomalous user behavior in AI interfaces
  11. Integrate monitoring alerts with SIEM systems
  12. Document baseline metrics for regulatory review
Module 7. Integrating AI Risk into Compliance Programs
Embed AI incident readiness into existing audit, reporting, and control frameworks.
12 chapters in this module
  1. Map AI risks to SOC 2 control objectives
  2. Align AI incident logs with evidence requirements
  3. Update internal audit checklists to include AI
  4. Report AI exposure in board-level risk dashboards
  5. Include AI scenarios in annual compliance training
  6. Document AI controls for ISO 27001 certification
  7. Link AI incident response to GDPR breach reporting
  8. Add AI model inventory to asset compliance audits
  9. Require AI risk assessment in vendor onboarding
  10. Incorporate AI into business continuity testing
  11. Track AI control effectiveness in quarterly reviews
  12. Produce an annual AI risk statement for executives
Module 8. Managing Third-Party AI Risk
Ensure accountability and visibility when using external AI platforms or models.
12 chapters in this module
  1. Evaluate vendor incident response SLAs
  2. Demand access to model telemetry and logs
  3. Negotiate rights to audit third-party AI systems
  4. Define data ownership in AI service contracts
  5. Require breach notification timelines under 72 hours
  6. Assess model transparency and explainability
  7. Verify third-party model security certifications
  8. Test vendor runbooks through joint exercises
  9. Document fallback plans for vendor outages
  10. Track AI model versioning and update practices
  11. Enforce data retention policies in vendor agreements
  12. Maintain a register of all third-party AI dependencies
Module 9. Communicating During AI Incidents
Coordinate internal and external messaging to maintain trust and meet regulatory obligations.
12 chapters in this module
  1. Define a single source of truth for incident facts
  2. Assign a central communications lead for AI events
  3. Draft holding statements for public disclosure
  4. Coordinate messaging across legal, PR, and IT
  5. Train spokespeople on AI-specific terminology
  6. Create internal status update templates
  7. Establish customer communication escalation paths
  8. Prepare FAQs for AI incident scenarios
  9. Time communications to regulatory deadlines
  10. Log all external disclosures for audit purposes
  11. Review messaging with legal before release
  12. Conduct a comms post-mortem after resolution
Module 10. Conducting AI Incident Post-Mortems
Lead structured reviews that turn failures into improvements without blame.
12 chapters in this module
  1. Declare a safe space for post-incident learning
  2. Collect logs, decisions, and communications
  3. Interview responders within 72 hours of resolution
  4. Map the incident timeline minute by minute
  5. Identify the root cause of AI system failure
  6. Assess whether runbooks were followed correctly
  7. Document what worked and what failed
  8. Assign action items with owners and deadlines
  9. Update runbooks and training based on findings
  10. Share lessons learned across departments
  11. Archive post-mortem for auditor access
  12. Schedule a follow-up to verify fixes
Module 11. Scaling AI Response Across the Organization
Extend incident readiness from pilot teams to enterprise-wide practice.
12 chapters in this module
  1. Identify business units with high AI exposure
  2. Train local incident coordinators in each division
  3. Standardize runbooks across departments
  4. Create a central AI incident response registry
  5. Host quarterly cross-functional readiness drills
  6. Publish a global AI incident policy
  7. Integrate AI response into onboarding programs
  8. Measure response maturity with a scoring framework
  9. Recognize teams that improve AI readiness
  10. Report aggregate AI risk metrics to executives
  11. Update response playbooks based on new threats
  12. Conduct annual certification of AI response leads
Module 12. Sustaining AI Incident Readiness
Institutionalize continuous improvement and adapt to evolving AI threats and regulations.
12 chapters in this module
  1. Schedule biannual tabletop exercises for AI
  2. Review and update runbooks every quarter
  3. Track regulatory changes affecting AI use
  4. Refresh training materials with new scenarios
  5. Audit AI system inventory for completeness
  6. Benchmark response maturity against peers
  7. Update vendor contracts with new requirements
  8. Report AI incident readiness to the board
  9. Maintain a log of near-miss AI events
  10. Investigate false positives in AI monitoring
  11. Adjust baselines based on model retraining
  12. Celebrate milestones in AI risk reduction

Frequently asked

Who is this course for?
This course is for IT, operations, compliance, or service management leaders responsible for risk and response when AI systems fail.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover technical AI security tools?
No. This course focuses on leadership decisions, response coordination, and compliance artifacts, not technical tooling.
Can I use this for regulatory audits?
Yes. The templates and documented processes are designed to meet audit requirements for AI risk management.
What deliverables will I receive?
You will receive completed runbooks, incident logs, gap analyses, and a custom implementation playbook.
What formats do the templates come in?
The implementation playbook downloads as PDF and editable XLSX. The course reads in your learning environment and exports to PDF for offline use. The files are yours to keep.
Can I share this with my team?
The licence is per person. Team pricing opens from three seats: reply to the order confirmation with TEAM and we will set it up.
How quickly can I start?
The diagnostic is one sitting and the templates work straight out of the kit. Account access takes up to 24 hours rather than being instant, because every order is checked and updated against the latest sources before it is delivered.
$199 one-time. Approximately 3 hours per module, designed to be completed alongside regular work. Most learners finish in 6–8 weeks..

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

30-day money-back guarantee·Know your weakest area today·210 scored questions·Course included· Account access within 24 hours
30-day money-back guarantee, no questions asked.
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