This curriculum reflects the scope typically addressed across a full consulting engagement or multi-phase internal transformation initiative.
Module 1: Integrating Disaster Recovery into AI Governance under ISO/IEC 42001:2023
- Map disaster recovery requirements to AI management system (AIMS) governance roles, including AI oversight committees and data stewardship functions.
- Define decision rights for activating AI disaster recovery protocols across legal, compliance, and operational domains.
- Assess trade-offs between AI model availability and regulatory compliance during recovery scenarios involving personal or sensitive data.
- Establish escalation pathways for AI system failures that impact safety, fairness, or transparency as defined in ISO/IEC 42001:2023 Clause 5.
- Align disaster recovery planning with risk assessment outputs from AIMS risk identification processes (Clause 6.1).
- Integrate AI-specific recovery objectives into enterprise-wide business continuity governance frameworks.
- Design audit trails for AI system recovery events to support internal and external accountability requirements.
- Specify governance thresholds for declaring AI system outages as material incidents requiring board-level reporting.
Module 2: Risk Assessment for AI System Disruptions
- Conduct scenario-based threat modeling for AI system failures, including data poisoning, model drift, and infrastructure loss.
- Quantify impact levels of dataset unavailability on AI performance, fairness, and decision accuracy using operational metrics.
- Identify single points of failure in AI data pipelines, model serving infrastructure, and third-party dependencies.
- Apply ISO/IEC 42001:2023 risk criteria to prioritize recovery efforts based on societal, financial, and operational consequences.
- Assess cascading risks when AI-supported business processes fail during disaster recovery transitions.
- Document risk treatment plans that include preventive, detective, and corrective controls for AI dataset integrity.
- Evaluate threat exposure from adversarial attacks on training or inference environments during recovery operations.
- Integrate AI disruption risks into enterprise risk registers with defined ownership and review cycles.
Module 3: Designing AI Dataset Resilience Architecture
- Define replication strategies for critical AI training and validation datasets across geographically dispersed storage systems.
- Implement version-controlled dataset repositories with rollback capabilities for pre-disruption states.
- Select storage solutions based on recovery time objectives (RTO) and recovery point objectives (RPO) for AI workloads.
- Design access controls and encryption for backup datasets to prevent unauthorized modification or exfiltration.
- Validate dataset integrity post-recovery using cryptographic hashing and statistical consistency checks.
- Architect metadata preservation mechanisms to maintain data lineage and provenance during restoration.
- Balance cost, performance, and redundancy in dataset storage by evaluating cloud, hybrid, and on-premise trade-offs.
- Establish automated monitoring for dataset corruption or degradation signals in backup environments.
Module 4: Model and System Recovery Protocols
- Develop standardized runbooks for restoring AI models, including dependency validation and environment reconfiguration.
- Define criteria for model revalidation post-recovery, including performance benchmarks and bias testing.
- Implement containerized AI environments to enable rapid redeployment across recovery sites.
- Test failover mechanisms for real-time inference systems under latency and throughput constraints.
- Manage version skew between recovered models and live data schemas through compatibility checks.
- Document dependencies between AI models, APIs, and supporting services to prevent partial recovery failures.
- Establish rollback procedures if recovered models exhibit anomalous behavior in production.
- Coordinate model recovery with downstream consumers to minimize disruption to automated decision systems.
Module 5: Data Provenance and Lineage in Recovery Scenarios
- Reconstruct data lineage paths for AI datasets to verify integrity after partial or corrupted recovery.
- Validate that recovered datasets reflect approved versions and have not been altered during outage periods.
- Implement automated tools to detect gaps or inconsistencies in data provenance records post-recovery.
- Ensure traceability from raw data to model output is preserved for auditability and regulatory compliance.
- Assess risks of using incomplete lineage information during recovery decision-making.
- Define retention policies for lineage metadata that align with disaster recovery retention requirements.
- Integrate lineage validation into pre-deployment checks for recovered AI systems.
- Train incident response teams to interpret data provenance diagrams during recovery triage.
Module 6: Testing, Validation, and Drills for AI Disaster Recovery
- Design tabletop exercises that simulate AI dataset loss and evaluate decision-making under time pressure.
- Conduct full-scale recovery drills for AI systems with measurement of RTO and RPO attainment.
- Validate model outputs from recovered systems against known benchmarks and statistical baselines.
- Identify failure modes in recovery processes, such as incorrect dataset version restoration or misconfigured pipelines.
- Measure team response effectiveness using mean time to detect (MTTD) and mean time to recover (MTTR) metrics.
- Update recovery plans based on lessons learned from test outcomes and near-miss incidents.
- Test cross-border data transfer compliance during recovery involving international data centers.
- Ensure third-party AI vendors participate in joint recovery testing with defined SLAs.
Module 7: Stakeholder Communication and Decision Escalation
- Develop communication protocols for notifying regulators, customers, and partners of AI system outages.
- Define thresholds for public disclosure of AI disruptions based on impact severity and duration.
- Prepare executive briefing templates that summarize AI recovery status, risks, and mitigation actions.
- Coordinate messaging across legal, PR, and technical teams to maintain consistency during crises.
- Establish decision escalation paths for halting AI operations during uncertain recovery states.
- Document stakeholder expectations for AI availability and incorporate them into recovery SLAs.
- Manage user trust by communicating recovery progress without overpromising system stability.
- Train incident commanders to make time-critical decisions on AI system reactivation.
Module 8: Continuous Improvement and Audit Readiness
- Integrate AI disaster recovery performance data into management review meetings per ISO/IEC 42001:2023 Clause 9.3.
- Conduct root cause analyses for recovery delays or failures and implement corrective actions.
- Update recovery plans in response to changes in AI system architecture, data sources, or regulatory requirements.
- Prepare documentation packages for internal and external audits of AI disaster recovery controls.
- Benchmark recovery capabilities against industry standards and peer organizations.
- Monitor emerging threats to AI infrastructure and adjust recovery strategies accordingly.
- Track key performance indicators such as recovery success rate, plan accuracy, and test frequency.
- Ensure version control and change management for all disaster recovery documentation and runbooks.
Module 9: Third-Party and Supply Chain Resilience
- Assess disaster recovery capabilities of AI cloud providers and data vendors through contractual audits.
- Negotiate SLAs that specify recovery time and data integrity commitments for external AI services.
- Map interdependencies between internal AI systems and third-party APIs or models.
- Develop contingency plans for switching to alternative data sources or models during prolonged outages.
- Verify that third parties maintain encryption and access controls aligned with organizational recovery policies.
- Conduct joint recovery testing with critical AI service providers on an annual basis.
- Monitor vendor financial and operational health as a precursor to supply chain failure risks.
- Enforce right-to-audit clauses for AI-related disaster recovery controls in vendor contracts.
Module 10: Strategic Alignment and Executive Oversight
- Align AI disaster recovery objectives with organizational resilience strategy and risk appetite.
- Present business impact analyses to executives to justify investment in AI recovery infrastructure.
- Integrate AI recovery metrics into enterprise dashboards for real-time board-level visibility.
- Balance cost of redundancy against potential losses from AI system downtime.
- Ensure AI disaster recovery planning reflects evolving AI use cases and strategic priorities.
- Define executive decision criteria for decommissioning or rebuilding AI systems post-disaster.
- Evaluate long-term shifts in AI architecture, such as edge deployment, on recovery planning.
- Establish periodic review cycles for AI disaster recovery strategy as part of corporate governance.
