A tailored course, built for your situation
Strategic AI Vendor Risk Assessment for Regulated Industries
Master compliance-grade AI procurement and governance for financial services, healthcare, and other regulated sectors.
$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.
Deploying AI vendors without robust risk controls creates compliance lag and operational blind spots in regulated environments.
The situation this course is for
Teams in regulated industries often move quickly to adopt AI tools, but struggle to align vendor due diligence with compliance frameworks. This leads to rework, audit findings, and governance gaps when board-level scrutiny increases.
Who this is for
Compliance officers, risk managers, technology leads, and product executives in financial services, healthcare, insurance, and other regulated sectors overseeing third-party AI solutions.
Who this is not for
This course is not for developers seeking AI model tuning or data scientists building in-house models. It is not for unregulated startups operating outside compliance frameworks.
What you walk away with
- Evaluate AI vendors through a compliance-first risk lens
- Map vendor capabilities to regulatory control requirements
- Build audit-ready due diligence packages for third-party AI
- Anticipate governance escalations before deployment
- Lead cross-functional AI vendor assessments with structured frameworks
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Risk in Regulated Contexts
Introduce core AI risk dimensions specific to compliance-heavy industries.
12 chapters in this module
- Defining regulated AI use cases
- Key regulatory touchpoints
- AI risk vs traditional IT risk
- Stakeholder mapping in governance
- Control framework alignment
- Third-party lifecycle basics
- Risk tolerance calibration
- Vendor due diligence thresholds
- Compliance escalation triggers
- Documentation standards
- Audit preparation fundamentals
- Governance maturity models
Module 2. Regulatory Landscape and Emerging Expectations
Survey current compliance expectations across major jurisdictions and sectors.
12 chapters in this module
- Global AI governance trends
- Sector-specific regulations
- Cross-border data implications
- Enforcement case studies
- Board-level reporting norms
- Emerging disclosure rules
- Model risk management updates
- Privacy and AI intersections
- Algorithmic accountability
- Ethical oversight structures
- Regulator engagement strategies
- Compliance horizon scanning
Module 3. AI Vendor Due Diligence Framework
Build a repeatable, audit-ready process for evaluating third-party AI providers.
12 chapters in this module
- Vendor classification schema
- Pre-RFP risk screening
- Request for information design
- Control evidence requirements
- Data handling assessments
- Model transparency demands
- Explainability benchmarks
- Security posture review
- Incident response readiness
- Sub-processor mapping
- Contractual risk clauses
- Exit strategy validation
Module 4. Control Mapping and Compliance Alignment
Translate regulatory requirements into actionable vendor assessment criteria.
12 chapters in this module
- Mapping controls to NIST AI RMF
- Aligning with ISO 42001
- GDPR and AI processing
- HIPAA considerations for AI
- GLBA and financial AI
- SOC 2 for AI vendors
- Custom control adaptation
- Gap analysis techniques
- Evidence collection workflows
- Control operating effectiveness
- Third-party attestation review
- Compliance reporting templates
Module 5. Model Risk Management Integration
Integrate vendor AI into existing model risk governance frameworks.
12 chapters in this module
- Model inventory inclusion
- Performance monitoring design
- Validation scope definition
- Oversight committee reporting
- Model change controls
- Drift detection protocols
- Bias and fairness testing
- Model documentation standards
- Version control tracking
- Model decommissioning
- Model lineage capture
- Independent review cycles
Module 6. Data Governance and Lifecycle Oversight
Ensure vendor AI use conforms to internal data policies and regulatory limits.
12 chapters in this module
- Data provenance tracking
- PII handling compliance
- Data retention policies
- Cross-border transfer rules
- Consent management
- Data minimization checks
- Purpose limitation enforcement
- Data quality validation
- Data lineage documentation
- Data access logging
- Data breach preparedness
- Data subject rights support
Module 7. Security and Resilience Assessment
Evaluate AI vendor cybersecurity readiness and operational resilience.
12 chapters in this module
- Infrastructure security review
- Penetration testing results
- Incident response planning
- Disaster recovery testing
- Access control rigor
- Zero trust implementation
- Threat modeling practices
- Vulnerability management
- Third-party security ratings
- Cyber insurance review
- Resilience benchmarking
- Red team exercise outcomes
Module 8. Ethical and Reputational Risk Management
Identify and mitigate ethical risks in vendor AI systems.
12 chapters in this module
- Bias detection frameworks
- Fairness audit protocols
- Transparency requirements
- Stakeholder impact analysis
- Reputational risk triggers
- Community feedback loops
- Ethics review boards
- Controversial use case flags
- Human oversight design
- Redress mechanisms
- Ethical AI certifications
- Public trust metrics
Module 9. Contractual and Legal Risk Mitigation
Structure agreements to enforce compliance and risk controls.
12 chapters in this module
- Risk-based SLA design
- Liability allocation
- Indemnification clauses
- Insurance requirements
- Audit rights negotiation
- Data ownership terms
- IP rights clarity
- Change control agreements
- Subcontractor restrictions
- Termination triggers
- Dispute resolution paths
- Compliance covenant drafting
Module 10. Ongoing Monitoring and Audit Readiness
Establish continuous oversight of AI vendors post-deployment.
12 chapters in this module
- Key risk indicator design
- Performance threshold alerts
- Compliance check-in cycles
- Audit evidence retention
- Regulatory change tracking
- Vendor change impact review
- Incident escalation paths
- Corrective action tracking
- Diligence file maintenance
- Board reporting cadence
- Third-party audit coordination
- Continuous improvement loops
Module 11. Cross-Functional Leadership in AI Risk
Lead AI vendor assessments with influence across legal, compliance, and tech teams.
12 chapters in this module
- Stakeholder alignment tactics
- Risk communication frameworks
- Executive briefing design
- Meeting facilitation techniques
- Conflict resolution strategies
- Vendor negotiation playbooks
- Influence without authority
- Change management integration
- Risk culture development
- Training rollout planning
- Leadership presence in audits
- Crisis communication readiness
Module 12. Implementation and Scaling Best Practices
Deploy and scale AI vendor risk frameworks across the organization.
12 chapters in this module
- Pilot program design
- Framework customization
- Tooling integration
- Team training rollout
- Metrics and KPIs
- Maturity progression
- Lessons from early adopters
- Scaling governance teams
- Automation opportunities
- Continuous learning integration
- Benchmarking against peers
- Future-proofing strategy
How this maps to your situation
- Preparing for AI vendor due diligence in a regulated environment
- Facing increased board scrutiny on third-party AI risk
- Building internal frameworks to standardize vendor assessments
- Responding to regulatory changes affecting AI deployments
Before vs. after
Before
Uncertain how to assess AI vendors against compliance requirements or lacking a standardized due diligence process.
After
Confidently lead AI vendor risk assessments with structured frameworks, audit-ready documentation, and cross-functional alignment.
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 4-6 hours per module, designed for professionals to complete at their own pace over 8-12 weeks.
If nothing changes
Organizations that delay robust AI vendor risk practices face increased audit findings, compliance penalties, and reputational damage as regulatory scrutiny intensifies.
How this compares to the alternatives
Unlike generic AI courses, this program focuses exclusively on third-party risk in regulated contexts, offering implementation-grade tools rather than conceptual overviews. Compared to consulting, it provides reusable frameworks at a fraction of the cost.
Frequently asked
Who is this course for?
Compliance officers, risk managers, technology leaders, and product executives in regulated industries overseeing third-party AI solutions.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate upon completion?
Yes, a certificate of completion is issued through the learning environment after finishing all modules.
$199 one-time. Approximately 4-6 hours per module, designed for professionals to complete at their own pace over 8-12 weeks..
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