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AIG7943 Govern AI Systems in Healthcare: Mastering NIST AI RMF and HIPAA Compliance

$199.00
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A tailored course, built for your situation

Govern AI Systems in Healthcare: Mastering NIST AI RMF and HIPAA Compliance

Build auditable, implementation-grade AI governance systems that stand up to regulator cycles and scale across AI product delivery

$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.
The AI governance package that passes internal review the first time

The situation this course is for

Technology leaders in healthcare face mounting pressure to prove AI systems are governed consistently, but evidence collection remains manual, reactive, and prone to last-minute fixes during audit cycles. Teams waste cycles reconciling model behavior with privacy impact assessments, control mappings, and vendor risk decisions, often under tight regulatory timelines.

Who this is for

Senior technology and privacy leaders in healthcare who own both AI system delivery and compliance outcomes , typically CPOs, CIOs, or dual-role executives overseeing infrastructure and data governance.

Who this is not for

This course is not for junior compliance analysts, standalone privacy consultants without tech ownership, or teams not currently deploying or governing AI systems in live environments.

What you walk away with

  • Design and deploy a repeatable AI governance validation cycle aligned with HIPAA and NIST AI RMF
  • Produce auditable artefacts for AI system risk decisions without last-minute rework
  • Map AI model behavior to privacy impact assessments and control requirements systematically
  • Reduce pre-audit preparation from weeks to under four days using pre-built templates
  • Lead AI governance discussions with technical depth and regulatory precision

The 12 modules (with all 144 chapters)

Module 1. Introduction to AI Governance in Regulated Healthcare
Establish the foundational requirements for governing AI systems where patient data and clinical impact intersect.
12 chapters in this module
  1. Understanding the convergence of AI innovation and healthcare compliance
  2. Key differences between traditional IT governance and AI system oversight
  3. The role of the technology leader in shaping AI accountability
  4. Regulatory touchpoints for AI in patient-facing applications
  5. Defining scope: what constitutes an AI system under HIPAA and NIST
  6. Common pitfalls in early-stage AI governance programs
  7. How AI risk profiles differ from standard software deployments
  8. The importance of documentation in regulatory scrutiny
  9. Building cross-functional alignment on AI governance expectations
  10. Establishing ownership between privacy, security, and engineering teams
  11. The lifecycle view of AI system governance
  12. Preparing for evolving regulatory expectations in AI
Module 2. Core Components of the NIST AI RMF
Break down the NIST AI Risk Management Framework into actionable components for implementation.
12 chapters in this module
  1. Overview of the four core functions of the NIST AI RMF
  2. Mapping Govern to organizational roles and responsibilities
  3. Integrating Map into system design and development workflows
  4. Applying Measure to quantify AI risks and performance
  5. Using Manage to operationalize risk decisions
  6. The role of transparency in AI RMF implementation
  7. How the AI RMF complements existing cybersecurity frameworks
  8. Aligning AI RMF stages with product development cycles
  9. Documenting AI risk decisions using NIST guidance
  10. Integrating third-party tools into the AI RMF workflow
  11. Training teams on AI RMF principles and application
  12. Reviewing and updating AI RMF implementation over time
Module 3. HIPAA Compliance Requirements for AI Systems
Translate HIPAA rules into specific controls and documentation requirements for AI deployments.
12 chapters in this module
  1. Understanding PHI in the context of AI training and inference
  2. HIPAA Security Rule applicability to machine learning models
  3. Ensuring administrative safeguards for AI system management
  4. Implementing technical safeguards for model access and data use
  5. Physical safeguards for AI infrastructure hosting patient data
  6. Business associate agreements for AI vendors and partners
  7. Conducting risk analyses specific to AI-enabled applications
  8. Documenting compliance efforts for audit readiness
  9. Handling data breaches involving AI systems under HIPAA
  10. Privacy Rule implications for AI-driven patient interactions
  11. Training requirements for staff working with AI and PHI
  12. Maintaining policies and procedures for AI-related HIPAA compliance
Module 4. Integrating NIST AI RMF with HIPAA Requirements
Bridge the gap between the NIST framework and HIPAA compliance through aligned controls and documentation.
12 chapters in this module
  1. Identifying overlapping requirements between NIST AI RMF and HIPAA
  2. Mapping AI RMF Govern function to HIPAA compliance responsibilities
  3. Using Map to align data flows with HIPAA data handling rules
  4. Applying Measure to assess AI risks against HIPAA risk analysis standards
  5. Operationalizing Manage through HIPAA-aligned risk mitigation
  6. Creating unified documentation that serves both frameworks
  7. Streamlining audit preparation with integrated artefacts
  8. Coordinating cross-functional teams across privacy and AI governance
  9. Developing a single source of truth for AI system compliance
  10. Leveraging automation to maintain alignment over time
  11. Training stakeholders on combined framework expectations
  12. Reviewing and updating the integrated approach annually
Module 5. Designing AI System Documentation for Audit Readiness
Create comprehensive, regulator-ready documentation packages for AI systems.
12 chapters in this module
  1. Defining the minimum viable AI governance package for audits
  2. Structuring system overviews for technical and compliance reviewers
  3. Documenting model purpose, scope, and intended use cases
  4. Recording data sources and preprocessing steps for transparency
  5. Describing model architecture and algorithmic approach clearly
  6. Capturing training data characteristics and potential biases
  7. Detailing validation and testing procedures used
  8. Including performance metrics and limitations disclosures
  9. Addressing explainability and interpretability methods
  10. Outlining human oversight and intervention mechanisms
  11. Summarizing risk mitigation strategies and controls
  12. Preparing appendices with supporting evidence and references
Module 6. Implementing AI Risk Assessments Aligned with HIPAA
Conduct risk assessments that meet HIPAA standards while addressing AI-specific threats.
12 chapters in this module
  1. Adapting HIPAA risk analysis methodology for AI systems
  2. Identifying AI-specific threats to confidentiality, integrity, and availability
  3. Assessing risks from model drift and data skew over time
  4. Evaluating adversarial attacks and data poisoning vulnerabilities
  5. Considering fairness and bias as compliance risks
  6. Determining likelihood and impact of AI-related incidents
  7. Documenting risk assessment findings in audit-ready format
  8. Prioritizing risks based on clinical and operational impact
  9. Linking risk decisions to existing security controls
  10. Involving clinical and technical stakeholders in risk evaluation
  11. Updating assessments with model retraining or updates
  12. Maintaining version-controlled records of all assessments
Module 7. Building AI Governance Workflows for Technology Teams
Embed governance into daily workflows without slowing innovation.
12 chapters in this module
  1. Integrating governance checkpoints into CI/CD pipelines
  2. Automating documentation generation during model development
  3. Creating templates for common AI governance artefacts
  4. Defining roles and approvals for AI system releases
  5. Establishing change management processes for model updates
  6. Setting up monitoring for post-deployment model behavior
  7. Implementing alerting for deviations from expected performance
  8. Using dashboards to track governance compliance status
  9. Conducting regular governance reviews with engineering teams
  10. Scaling governance practices across multiple AI projects
  11. Training developers on governance requirements and tools
  12. Iterating on workflows based on team feedback and audit results
Module 8. Vendor Management and Third-Party AI Solutions
Ensure third-party AI vendors comply with HIPAA and follow sound governance practices.
12 chapters in this module
  1. Assessing vendor AI governance maturity before procurement
  2. Including AI-specific requirements in procurement checklists
  3. Negotiating BAAs that cover AI model development and use
  4. Evaluating third-party model cards and technical documentation
  5. Validating vendor risk assessments and testing procedures
  6. Monitoring ongoing compliance of hosted AI solutions
  7. Auditing vendor practices for adherence to agreed standards
  8. Managing risks from API-based AI services and microservices
  9. Documenting due diligence for regulatory examinations
  10. Handling incident response coordination with vendors
  11. Establishing exit strategies and data portability plans
  12. Maintaining oversight of vendor model updates and changes
Module 9. AI Model Monitoring and Continuous Compliance
Maintain compliance and performance oversight throughout the AI system lifecycle.
12 chapters in this module
  1. Defining key performance indicators for AI system health
  2. Monitoring for model drift and data distribution shifts
  3. Tracking fairness metrics over time to detect bias emergence
  4. Logging model inputs, outputs, and decision pathways
  5. Implementing automated alerts for anomalous behavior
  6. Scheduling regular model retraining and validation
  7. Updating documentation to reflect system changes
  8. Conducting periodic compliance check-ins between audits
  9. Reviewing access logs and user interaction patterns
  10. Assessing impact of infrastructure changes on model behavior
  11. Maintaining version history for models and datasets
  12. Archiving retired models with complete documentation
Module 10. Preparing for Regulatory Review and Audit
Assemble and present AI governance evidence that satisfies auditors.
12 chapters in this module
  1. Anticipating common auditor questions about AI systems
  2. Organizing documentation into a logical audit package
  3. Preparing executive summaries for leadership review
  4. Conducting internal dry runs before official audits
  5. Training spokespeople on how to discuss AI governance
  6. Responding to auditor requests efficiently and completely
  7. Addressing findings and implementing corrective actions
  8. Demonstrating continuous improvement in governance practices
  9. Using feedback to strengthen future audit readiness
  10. Maintaining composure and clarity under regulatory scrutiny
  11. Coordinating cross-functional responses during audit periods
  12. Closing audit cycles with formal acknowledgment and records
Module 11. Scaling AI Governance Across the Organization
Expand governance practices from pilot projects to enterprise-wide deployment.
12 chapters in this module
  1. Assessing organizational readiness for scaled AI governance
  2. Developing a center of excellence for AI governance
  3. Creating reusable templates and playbooks for teams
  4. Establishing governance standards for all AI initiatives
  5. Providing training and support for decentralized teams
  6. Implementing centralized oversight with local flexibility
  7. Measuring adoption and effectiveness across units
  8. Sharing best practices and lessons learned company-wide
  9. Integrating governance into enterprise architecture standards
  10. Aligning AI governance with overall digital transformation
  11. Securing executive sponsorship for expansion efforts
  12. Iterating on the model based on organizational feedback
Module 12. Future-Proofing AI Governance Practices
Adapt governance approaches to keep pace with technological and regulatory changes.
12 chapters in this module
  1. Tracking emerging regulations affecting AI in healthcare
  2. Monitoring advancements in AI safety and evaluation methods
  3. Updating policies to reflect new technical capabilities
  4. Incorporating lessons from industry incidents and recalls
  5. Engaging with standards bodies and professional communities
  6. Participating in regulatory sandboxes and pilot programs
  7. Investing in staff development on evolving AI risks
  8. Balancing innovation speed with responsible governance
  9. Communicating governance evolution to internal stakeholders
  10. Demonstrating leadership in responsible AI adoption
  11. Positioning the organization as a trusted AI innovator
  12. Sustaining governance excellence through leadership change

How this maps to your situation

  • Audit preparation
  • AI system deployment
  • Vendor evaluation
  • Regulatory response

Before vs. after

Before
Manual, reactive AI governance efforts that require last-minute coordination between privacy, security, and engineering teams during audit cycles.
After
A repeatable, pre-structured validation cycle that produces regulator-ready AI governance packages in days, not weeks.

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 week over six weeks, designed for completion on weekends or focused blocks.

If nothing changes
Without a structured approach, AI governance remains ad hoc and vulnerable to audit findings, compliance gaps, and operational inefficiencies that slow innovation and expose the organization to regulatory scrutiny.

How this compares to the alternatives

Unlike generic compliance courses, this program delivers implementation-grade tooling and templates specifically for AI systems governed under HIPAA and NIST AI RMF , not theory, but actionable systems built for healthcare technology leaders.

Frequently asked

Is this course technical or policy-focused?
It bridges both, designed for leaders who must understand technical implementation and produce policy-grade documentation.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I share the templates with my team?
Yes, all downloadable materials are licensed for team use within your organization.
$199 one-time. Approximately 90 minutes per week over six weeks, designed for completion on weekends or focused blocks..

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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