Description

This curriculum spans the full lifecycle of AI governance in healthcare, equivalent to a multi-workshop program developed for an enterprise implementing AI oversight across clinical, security, and compliance functions, with depth matching an internal capability build for sustained ISO 27799 alignment.

Module 1: Establishing the Governance Framework for AI in Health Information Security

Define the scope of AI systems covered under the organization’s ISO 27799-aligned governance framework, including clinical decision support, predictive analytics, and robotic process automation.
Select governance roles and responsibilities for AI oversight, ensuring clear accountability across clinical, IT, compliance, and data science teams.
Map AI-specific risks to existing health information security controls in ISO 27799, identifying gaps where AI introduces novel threat vectors.
Develop a cross-functional AI governance board with authority to approve high-risk models and enforce compliance with clinical safety and data protection standards.
Integrate AI governance into existing enterprise risk management processes, ensuring AI-related incidents are reported and escalated through formal channels.
Establish criteria for classifying AI systems by risk level based on clinical impact, data sensitivity, and autonomy of decision-making.
Implement version-controlled documentation for AI governance policies, ensuring auditability and traceability across regulatory inspections.
Align AI governance timelines with organizational compliance cycles, including HIPAA audits, ISO certifications, and internal control reviews.

Module 2: Risk Assessment and AI-Specific Threat Modeling

Conduct threat modeling for AI pipelines, identifying attack surfaces in data ingestion, model training, and inference deployment layers.
Assess adversarial risks such as data poisoning, model inversion, and evasion attacks in clinical AI applications.
Quantify the impact of model drift on patient safety and data integrity, establishing thresholds for retraining and recalibration.
Integrate AI failure scenarios into existing business impact analyses, particularly for life-critical systems like sepsis prediction or radiotherapy planning.
Perform dependency analysis on third-party AI components, including pre-trained models and cloud-based inference APIs.
Document data provenance and labeling processes to evaluate risks of bias, misclassification, and data leakage.
Apply STRIDE or OCTAVE methodologies to AI workflows, tailoring threat categories to healthcare-specific contexts.
Validate risk assessment outputs with clinical stakeholders to ensure realistic impact scoring for false positives and negatives.

Module 3: Data Governance and Privacy in AI Systems

Implement data minimization techniques in AI training sets, ensuring only necessary PHI is processed and retained.
Establish data use agreements that specify permitted purposes for AI training, prohibiting secondary use without re-consent.
Deploy differential privacy or synthetic data generation where direct use of real patient data poses unacceptable privacy risks.
Enforce role-based access controls on AI training environments, restricting access to data scientists based on project necessity.
Conduct data lineage tracking from source systems to model inputs, enabling auditability and breach impact assessment.
Validate de-identification methods used in AI datasets against re-identification attack models and regulatory benchmarks.
Monitor data quality continuously in production AI systems, flagging anomalies that may indicate data corruption or bias.
Implement data retention schedules for AI training artifacts, including datasets, model weights, and logs, aligned with legal hold requirements.

Module 4: Model Development and Validation Controls

Enforce code and model versioning using Git or MLflow to ensure reproducibility of AI development pipelines.
Require dual approval for model deployment: one from data science and one from clinical validation authority.
Define validation protocols for AI models that include statistical performance, clinical utility, and fairness metrics.
Implement holdout testing datasets that are inaccessible during training and used solely for final validation.
Document model assumptions, limitations, and known failure modes in standardized model cards for clinical review.
Conduct bias testing across demographic groups using stratified performance analysis, particularly for race, gender, and age.
Prohibit the use of black-box models in high-stakes decisions unless accompanied by explainability reports and fallback protocols.
Require pre-deployment penetration testing of model APIs to detect injection, overflow, or unauthorized access vulnerabilities.

Module 5: Deployment and Operational Security of AI Systems

Enforce secure deployment practices using containerization and orchestration platforms with role-based access and network segmentation.
Implement mutual TLS authentication between AI inference services and client applications to prevent spoofing.
Deploy runtime monitoring to detect anomalous input patterns indicative of adversarial attacks or data drift.
Isolate AI inference workloads in dedicated virtual environments with no direct access to clinical databases.
Log all model inference requests and responses with full audit trails, including user identity, timestamp, and input parameters.
Configure automatic failover mechanisms for AI services to maintain clinical workflow continuity during outages.
Apply least-privilege principles to service accounts used by AI applications, limiting database and system access.
Integrate AI system health checks into centralized monitoring dashboards used by IT operations teams.

Module 6: Monitoring, Auditing, and Continuous Compliance

Establish automated dashboards to track model performance decay, alerting when accuracy or fairness metrics fall below thresholds.
Conduct quarterly audits of AI system logs to verify compliance with access control and data handling policies.
Perform retrospective analysis of AI-driven clinical decisions to identify unintended consequences or errors.
Integrate AI audit logs with SIEM systems to correlate events with broader security incidents.
Document and report model retraining events as change management activities requiring approval and testing.
Validate that AI-generated outputs are time-stamped and digitally signed to ensure non-repudiation.
Review third-party AI vendor compliance annually, including their adherence to ISO 27799 and data processing agreements.
Archive model versions and associated metadata for at least seven years to support regulatory and litigation inquiries.

Module 7: Incident Response and AI Failure Management

Define AI-specific incident categories, such as model poisoning, biased output, or incorrect clinical recommendations.
Integrate AI failure scenarios into organizational incident response playbooks with defined escalation paths.
Establish rollback procedures to revert to previous model versions or fallback rules-based systems during outages.
Assign incident ownership to a cross-functional team including clinical leads, data scientists, and security analysts.
Conduct post-incident reviews for AI failures, documenting root cause and required control improvements.
Notify affected patients and regulators when AI errors result in harm or privacy breaches, per legal requirements.
Preserve forensic evidence from AI systems, including input data, model state, and logs, for investigation purposes.
Test incident response plans annually through tabletop exercises involving AI failure simulations.

Module 8: Third-Party and Vendor Governance for AI Solutions

Require third-party AI vendors to provide model cards, security certifications, and penetration test reports before procurement.
Negotiate contractual clauses that mandate transparency into model updates, data handling, and breach notification timelines.
Conduct on-site assessments of vendor development environments when high-risk AI systems are involved.
Verify that vendor AI systems support integration with the organization’s identity and access management infrastructure.
Prohibit vendors from using customer data for model improvement without explicit, opt-in consent.
Enforce right-to-audit clauses allowing periodic review of vendor compliance with ISO 27799 controls.
Monitor vendor patching and update cycles to ensure timely remediation of known AI-related vulnerabilities.
Establish exit strategies for AI vendor contracts, including data retrieval, model transfer, and decommissioning plans.

Module 9: Regulatory Alignment and Certification Readiness

Map AI governance controls to specific clauses in ISO 27799, HIPAA, GDPR, and MDR for medical device-classified AI.
Prepare documentation packages for auditors, including risk assessments, model validation records, and access logs.
Classify AI systems under medical device regulations where applicable, triggering additional design and testing requirements.
Engage legal counsel to interpret evolving AI regulations such as the EU AI Act and their impact on health applications.
Conduct gap analyses between current AI practices and ISO 27799:2024 Annex B recommendations on AI.
Submit AI systems for certification by accredited bodies when required for market access or reimbursement.
Maintain a regulatory register that tracks AI-related compliance obligations across jurisdictions.
Update policies and controls in response to regulatory findings or enforcement actions involving AI systems.

Module 10: Governance Maturity and Continuous Improvement

Assess AI governance maturity using a staged model, from ad hoc to optimized, based on control consistency and automation.
Collect feedback from clinicians and data stewards on AI system usability, reliability, and trustworthiness.
Benchmark AI governance practices against peer institutions and industry frameworks like NIST AI RMF.
Invest in automated governance tooling, such as policy-as-code and continuous compliance monitoring platforms.
Rotate governance board membership periodically to prevent groupthink and introduce fresh perspectives.
Update training materials for staff annually to reflect new AI risks, controls, and incident patterns.
Publish internal governance metrics, such as time-to-remediate model drift or number of blocked unauthorized access attempts.
Conduct biannual reviews of the AI governance framework to incorporate lessons from incidents, audits, and technological changes.

AI Rules in ISO 27799