A tailored course, built for your situation
Operationally-Sound AI Vendor Risk Assessment for Regulated Industries
A structured, implementation-grade course for professionals managing AI vendor risk in compliance-driven environments.
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
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.
Difficulty aligning AI innovation with regulatory compliance and operational risk standards.
The situation this course is for
AI vendor solutions are advancing rapidly, but risk assessment practices often lag, creating friction between innovation teams and compliance officers. Without a common, operationally-sound framework, organizations face inconsistent evaluations, audit exposure, and delayed deployments.
Who this is for
Compliance officers, risk managers, technology governance leads, and procurement specialists in regulated industries who need to evaluate AI vendors with confidence and consistency.
Who this is not for
This is not for software developers building AI models or vendors marketing AI tools. It is also not for professionals in unregulated or low-compliance environments.
What you walk away with
- Apply a structured methodology to assess AI vendor risk across technical, legal, and operational domains
- Leverage standardized templates for due diligence questionnaires and RFPs
- Evaluate AI vendor compliance with regulatory frameworks such as GDPR, HIPAA, and SOC 2
- Integrate risk assessment outcomes into procurement and contract negotiation
- Build audit-ready documentation for internal and external reviewers
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk in Regulated Contexts
Introduce core concepts, regulatory drivers, and the business case for structured AI vendor assessment.
12 chapters in this module
- Defining AI vendor risk in regulated environments
- Key regulatory expectations across sectors
- The evolution of third-party AI risk management
- Distinguishing AI risk from traditional software risk
- Stakeholder roles in vendor evaluation
- Risk taxonomy for AI systems
- Mapping AI use cases to compliance domains
- Understanding model lifecycle implications
- Vendor transparency as a risk indicator
- Baseline expectations for documentation
- The role of explainability and auditability
- Establishing governance thresholds
Module 2. Regulatory Landscape and Compliance Alignment
Survey current compliance requirements affecting AI vendor selection and oversight.
12 chapters in this module
- Overview of GDPR implications for AI vendors
- HIPAA considerations in health-related AI
- Financial sector regulations and AI risk
- SOC 2 and vendor assurance expectations
- Cross-border data flow challenges
- Sector-specific AI guidance documents
- Enforcement trends and regulatory signals
- Aligning vendor assessments with audit requirements
- Compliance mapping techniques
- Licensing and intellectual property risk
- Ethical AI frameworks as compliance inputs
- Preparing for regulatory examinations
Module 3. Due Diligence Framework Design
Build a repeatable process for initiating and scoping AI vendor assessments.
12 chapters in this module
- Scoping AI vendor engagements
- Classifying risk levels by use case
- Developing risk-based assessment tiers
- Designing initial screening questionnaires
- Identifying critical control areas
- Stakeholder alignment strategies
- Resource planning for assessments
- Timeline management for procurement cycles
- Integrating legal and security teams
- Setting escalation thresholds
- Documenting assumptions and decisions
- Maintaining assessment consistency
Module 4. Technical Evaluation of AI Vendors
Assess the technical robustness and operational integrity of AI systems.
12 chapters in this module
- Model validation requirements
- Data provenance and lineage checks
- Bias and fairness assessment protocols
- Performance benchmarking standards
- Model monitoring and drift detection
- API security and integration risk
- Infrastructure resilience and uptime
- Access controls and authentication
- Encryption and data handling practices
- Incident response capabilities
- Third-party dependencies and sub-processors
- Disaster recovery and business continuity
Module 5. Legal and Contractual Risk Mitigation
Structure agreements that protect organizational interests and enforce compliance.
12 chapters in this module
- Key clauses for AI vendor contracts
- Liability and indemnification terms
- Warranties and representations
- Data ownership and usage rights
- Audit rights and transparency obligations
- Subcontractor and chain liability
- Termination and exit planning
- Insurance and financial safeguards
- Dispute resolution mechanisms
- Jurisdiction and governing law
- Compliance with export controls
- Renewal and pricing lock-in clauses
Module 6. Data Privacy and Protection Standards
Ensure AI vendors meet stringent data handling and privacy requirements.
12 chapters in this module
- Data minimization and purpose limitation
- Consent and lawful basis verification
- Anonymization and pseudonymization techniques
- Data retention and deletion policies
- Cross-border transfer mechanisms
- DPIA requirements for high-risk AI
- Vendor access to sensitive data
- Logging and access monitoring
- Privacy by design in AI systems
- Vendor responses to DSARs
- Data breach notification timelines
- Certifications and attestations
Module 7. Security and Cyber Risk Assessment
Evaluate cybersecurity posture and resilience of AI vendors.
12 chapters in this module
- Vendor security certifications review
- Penetration testing and red teaming
- Vulnerability disclosure practices
- Patch management and update cycles
- Zero-trust architecture alignment
- Identity and access management
- Network segmentation and isolation
- Threat modeling for AI systems
- Supply chain attack surface
- Incident response playbooks
- Security awareness training
- Third-party risk ratings
Module 8. Operational Resilience and Business Continuity
Assess the vendor's ability to sustain operations under stress.
12 chapters in this module
- Defining operational resilience for AI vendors
- Uptime and SLA expectations
- Disaster recovery planning
- Failover and redundancy mechanisms
- Capacity planning and scalability
- Monitoring and alerting systems
- Change management processes
- Vendor financial stability checks
- Geopolitical risk exposure
- Workforce continuity planning
- Single points of failure identification
- Resilience testing results review
Module 9. Ethical AI and Responsible Innovation
Incorporate ethical considerations into vendor evaluation.
12 chapters in this module
- Defining responsible AI principles
- Bias detection and mitigation strategies
- Fairness metrics and testing
- Transparency in model behavior
- Human oversight and escalation paths
- Stakeholder impact assessments
- AI use case acceptability thresholds
- Community and societal impact
- Whistleblower protections
- Ethics review board involvement
- Public trust and reputation risk
- Sustainability considerations
Module 10. Audit Readiness and Documentation
Prepare comprehensive, defensible records of AI vendor assessments.
12 chapters in this module
- Documentation standards for auditors
- Evidence collection strategies
- Version control and archiving
- Assessment report templates
- Risk rating documentation
- Stakeholder approval workflows
- Regulatory inspection preparation
- Internal audit coordination
- External validator engagement
- Remediation tracking
- Continuous monitoring logs
- Retention and retrieval policies
Module 11. Ongoing Monitoring and Reassessment
Establish processes for continuous oversight of AI vendors.
12 chapters in this module
- Post-onboarding monitoring plans
- Key risk indicators and thresholds
- Automated monitoring tools
- Quarterly review cadences
- Incident-triggered reassessments
- Vendor performance scorecards
- Regulatory change tracking
- Re-certification processes
- Contract compliance checks
- Relationship management strategies
- Exit preparedness
- Lessons learned integration
Module 12. Implementation and Integration
Deploy the assessment framework across the organization.
12 chapters in this module
- Change management for new processes
- Training stakeholders and reviewers
- Integrating with procurement systems
- Workflow automation opportunities
- Governance committee reporting
- Metrics for success and adoption
- Feedback loops and iteration
- Scaling across business units
- Vendor self-service portals
- Integration with GRC platforms
- Continuous improvement roadmap
- Case studies and lessons learned
How this maps to your situation
- Evaluating a new AI vendor for a high-compliance function
- Responding to internal audit findings on vendor oversight
- Scaling AI adoption while maintaining regulatory alignment
- Building a centralized AI vendor risk function
Before vs. after
Before
Uncertainty in evaluating AI vendors, inconsistent assessments, and reactive compliance efforts.
After
A structured, repeatable, and audit-ready approach to AI vendor risk that supports innovation while meeting regulatory expectations.
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 24 hours of self-paced learning, with implementation activities extending over 6, 8 weeks depending on organizational context.
If nothing changes
Without a standardized approach, organizations risk regulatory scrutiny, operational disruptions, and reputational damage from poorly assessed AI vendors.
How this compares to the alternatives
Unlike generic vendor risk courses, this program is tailored specifically to AI systems in regulated environments, offering implementation-grade tools and frameworks not available in open-source or vendor-provided materials.
Frequently asked
Who is this course for?
Compliance officers, risk managers, technology governance leads, and procurement specialists in regulated industries who need to evaluate AI vendors with confidence and consistency.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate of completion?
Yes, a digital certificate is awarded upon finishing all modules and assessments.
$199 one-time. Approximately 24 hours of self-paced learning, with implementation activities extending over 6, 8 weeks depending on organizational context..
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