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SEC3092 Institutionalizing Trustworthy AI Through Integrated Security and Compliance Controls

$199.00
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A tailored course, built for your situation

Institutionalizing Trustworthy AI Through Integrated Security and Compliance Controls

A step-by-step guide to embedding AI governance within operational resilience frameworks

$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.

12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Recurring rework in AI control mappings during audit cycles

The situation this course is for

Security leaders are expected to validate AI systems quickly, but end up rebuilding control evidence across frameworks due to lack of integration between AI governance and existing compliance infrastructure.

Who this is for

Global CISO overseeing product security and compliance integration, often acting as fractional advisor with AI research exposure

Who this is not for

Individual contributors focused only on technical AI model tuning or entry-level auditors without policy design experience

What you walk away with

  • Design AI security controls that satisfy multiple compliance demands from day one
  • Reduce audit-cycle rework by aligning AI initiatives with ISO 22301 resilience requirements
  • Produce sign-off-ready compliance packages for AI deployments in regulated environments
  • Strengthen cross-functional influence by delivering predictable, reusable implementation artefacts
  • Position AI governance as a force multiplier for existing security and compliance programs

The 12 modules (with all 144 chapters)

Module 1. Foundations of Trustworthy AI in Operational Resilience
Align AI governance objectives with ISO 22301 principles for continuity and risk tolerance.
12 chapters in this module
  1. Defining trustworthy AI within enterprise resilience frameworks
  2. Mapping AI risks to business continuity impact categories
  3. Establishing governance thresholds for automated decision systems
  4. Integrating AI incident response with existing DR protocols
  5. Linking model failure scenarios to RTO and RPO targets
  6. Developing AI-specific BIA criteria for critical functions
  7. Role definition for AI oversight within crisis management teams
  8. Creating AI continuity playbooks for high-availability systems
  9. Assessing third-party AI vendor resilience commitments
  10. Benchmarking AI resilience against peer institutions
  11. Documenting AI recovery dependencies in SoA statements
  12. Validating AI continuity controls through tabletop exercises
Module 2. AI Control Mapping Across ISO 22301 and Complementary Standards
Crosswalk AI governance requirements between ISO 22301, NIST CSF, and SOC 2.
12 chapters in this module
  1. Identifying overlapping control objectives for AI systems
  2. Mapping ISO 22301 clause 8.2 to AI operational readiness
  3. Aligning NIST CSF Protect function with AI model safeguards
  4. Integrating SOC 2 CC6.1 into AI data processing workflows
  5. Harmonizing audit evidence requirements across frameworks
  6. Building unified control statements for multi-standard compliance
  7. Leveraging ISO 22301 A.8.16 for AI access management
  8. Applying NIST SP 800-207 principles to AI system segmentation
  9. Connecting ISO 27701 privacy controls to AI data handling
  10. Using COBIT 5APO12.05 for AI change management oversight
  11. Designing control dashboards for executive visibility
  12. Maintaining versioned control mappings for AI system updates
Module 3. Embedding AI Governance into Business Impact Analysis
Incorporate AI dependencies and failure modes into BIA processes.
12 chapters in this module
  1. Identifying core business functions reliant on AI decisioning
  2. Assessing AI model drift impact on process accuracy
  3. Quantifying financial exposure from AI service outages
  4. Establishing MTTD and MTTR targets for AI components
  5. Documenting data pipeline resilience for training systems
  6. Evaluating fallback mechanisms for degraded AI performance
  7. Scoring AI system criticality using BC impact scales
  8. Interviewing process owners on AI dependency thresholds
  9. Mapping AI vendor SLAs to internal continuity requirements
  10. Validating BIA inputs with red team stress testing
  11. Updating BIA templates to include generative AI workloads
  12. Generating executive summaries for AI risk prioritization
Module 4. AI-Ready Continuity Strategy Development
Extend continuity strategies to cover AI infrastructure and model operations.
12 chapters in this module
  1. Defining recovery strategies for AI inference endpoints
  2. Designing failover approaches for real-time AI scoring
  3. Securing offline model execution capabilities
  4. Maintaining model version integrity during recovery
  5. Replicating AI training environments across regions
  6. Establishing warm standby for high-priority AI services
  7. Planning for data schema drift during AI system restoration
  8. Implementing model rollback procedures for corrupted versions
  9. Integrating AIOps alerts into incident escalation paths
  10. Creating AI-specific recovery playbooks with runbook automation
  11. Validating recovery time objectives with load testing
  12. Documenting strategy exceptions for experimental AI systems
Module 5. AI Incident Response and Crisis Management Integration
Adapt incident response plans to include AI-specific failure modes.
12 chapters in this module
  1. Classifying AI incidents using severity and impact criteria
  2. Defining escalation paths for model bias detection events
  3. Integrating model monitoring alerts into SIEM workflows
  4. Establishing war room protocols for AI service outages
  5. Coordinating legal and PR response for AI-generated content incidents
  6. Documenting root cause analysis methods for AI failures
  7. Conducting post-mortems on AI decision inaccuracies
  8. Updating IR playbooks with generative AI containment steps
  9. Testing AI incident scenarios in crisis simulation exercises
  10. Managing third-party AI vendor communication during outages
  11. Reporting AI incident metrics to executive leadership
  12. Maintaining IR readiness through quarterly AI tabletop drills
Module 6. AI Compliance Evidence Generation and Maintenance
Produce audit-ready evidence packages for AI systems across compliance frameworks.
12 chapters in this module
  1. Defining evidence requirements for AI model validation
  2. Documenting training data provenance and lineage
  3. Capturing model performance metrics over time
  4. Generating explainability reports for automated decisions
  5. Maintaining version-controlled model registries
  6. Creating audit trails for AI system modifications
  7. Storing human-in-the-loop review records
  8. Compiling fairness assessment results for regulatory submission
  9. Automating evidence collection through API integrations
  10. Validating evidence completeness against control mappings
  11. Preparing evidence packages for external auditor review
  12. Archiving AI compliance documentation per retention policies
Module 7. Third-Party AI Vendor Risk and Resilience Assessment
Evaluate and monitor external AI providers using ISO 22301 principles.
12 chapters in this module
  1. Assessing AI vendor business continuity planning maturity
  2. Reviewing AI model retraining SLAs and uptime guarantees
  3. Evaluating data protection controls in AI-as-a-service platforms
  4. Validating AI vendor incident response capabilities
  5. Conducting due diligence on open-source AI component risks
  6. Monitoring AI vendor compliance certifications continuously
  7. Establishing performance benchmarks for AI service levels
  8. Creating exit strategies for AI vendor contract termination
  9. Managing AI vendor lock-in through API standardization
  10. Auditing AI vendor change management processes
  11. Enforcing right-to-audit clauses for AI systems
  12. Maintaining vendor risk profiles with dynamic scoring
Module 8. AI System Change Management and Version Control
Implement structured change control for AI models and pipelines.
12 chapters in this module
  1. Defining change approval thresholds for AI models
  2. Establishing CAB processes for AI system modifications
  3. Documenting impact assessments for model updates
  4. Implementing pre-deployment validation checklists
  5. Creating rollback plans for failed AI deployments
  6. Maintaining version history for training datasets
  7. Tracking feature engineering changes in metadata logs
  8. Enforcing signed approvals for production promotions
  9. Integrating AI changes into existing ITIL change workflows
  10. Conducting post-implementation reviews for AI releases
  11. Managing technical debt in AI model pipelines
  12. Automating change audit trails for compliance reporting
Module 9. AI Training, Awareness, and Role Clarity Programs
Develop targeted education initiatives for AI resilience roles.
12 chapters in this module
  1. Identifying AI governance training needs by role
  2. Creating role-based awareness modules for developers
  3. Developing executive briefing materials on AI risks
  4. Delivering hands-on workshops for AI incident response
  5. Establishing AI ethics training for decision support systems
  6. Measuring training effectiveness through knowledge checks
  7. Maintaining training records for compliance audits
  8. Onboarding third-party teams on internal AI policies
  9. Refreshing training content quarterly with new case studies
  10. Integrating AI scenarios into security awareness campaigns
  11. Certifying personnel on AI control responsibilities
  12. Generating completion reports for regulatory submission
Module 10. AI Compliance Validation and Internal Audit Preparation
Prepare for internal and external review of AI governance controls.
12 chapters in this module
  1. Scheduling internal audits of AI system controls
  2. Conducting gap assessments against ISO 22301 requirements
  3. Remediating findings from AI control testing exercises
  4. Coordinating cross-functional evidence collection
  5. Conducting pre-audit readiness reviews for AI systems
  6. Responding to auditor inquiries about model validation
  7. Demonstrating control effectiveness through testing records
  8. Maintaining audit issue trackers with resolution timelines
  9. Preparing management responses to audit observations
  10. Presenting AI control maturity to oversight committees
  11. Incorporating audit feedback into AI governance refinement
  12. Building audit-proof documentation packages for AI deployments
Module 11. Continuous Monitoring and AI Control Optimization
Implement ongoing monitoring to maintain AI compliance effectiveness.
12 chapters in this module
  1. Defining KPIs for AI governance program health
  2. Establishing dashboards for real-time control visibility
  3. Monitoring model drift and performance degradation
  4. Tracking AI incident frequency and resolution times
  5. Conducting quarterly control self-assessments
  6. Analyzing compliance trend data for improvement areas
  7. Automating control testing through continuous assurance
  8. Integrating AI risk metrics into enterprise risk reports
  9. Benchmarking AI controls against industry standards
  10. Updating control design based on emerging threats
  11. Optimizing evidence collection to reduce audit burden
  12. Reporting AI compliance status to executive leadership
Module 12. Scaling AI Governance Across Organizational Units
Extend proven AI control frameworks to new business areas.
12 chapters in this module
  1. Creating AI governance playbooks for new departments
  2. Adapting control templates for different AI use cases
  3. Onboarding product teams to standardized AI review processes
  4. Establishing center of excellence for AI governance
  5. Developing enablement resources for AI practitioners
  6. Conducting maturity assessments for AI readiness
  7. Rolling out AI risk frameworks to international subsidiaries
  8. Harmonizing AI policies across regional legal requirements
  9. Integrating AI governance into product development lifecycles
  10. Scaling training programs for enterprise-wide adoption
  11. Measuring adoption and effectiveness across units
  12. Iterating governance model based on organizational feedback

How this maps to your situation

  • AI system in production requiring compliance validation
  • New AI initiative entering pilot phase with regulatory scrutiny
  • Cross-jurisdictional AI deployment needing unified control framework
  • Executive request for AI governance maturity assessment

Before vs. after

Before
AI governance handled as ad-hoc projects with fragmented controls and recurring audit rework
After
AI systems deployed with integrated, reusable compliance packages that pass review cycles predictably

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 90 minutes per module, designed for completion over six weeks with practical application between sessions.

If nothing changes
Without structured integration, AI initiatives will continue to create compliance bottlenecks, increase audit risk, and require disproportionate leadership attention during review cycles.

How this compares to the alternatives

Generic AI ethics courses lack implementation depth; vendor-specific training doesn't transfer across tools; internal initiatives often reinvent controls. This course provides framework-agnostic implementation patterns grounded in ISO 22301 with cross-standard applicability.

Frequently asked

Is this course focused on technical AI model development?
No, it's designed for security and compliance leaders implementing governance controls around AI systems, not building models.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this to non-ISO 22301 environments?
Yes, the control integration patterns work across NIST CSF, SOC 2, and other frameworks even if your org doesn't use ISO 22301.
$199 one-time. Approximately 90 minutes per module, designed for completion over six weeks with practical application between sessions..

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· 144 chapters· Hand-built playbook included· Account access within 24 hours
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