A tailored course, built for your situation
Operationally-Sound AI Vendor Risk Assessment for Regulated Industries
A 12-module implementation-grade course for business and technology leaders navigating AI procurement and compliance
$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.
AI vendor assessments are too often theoretical, delayed, or disconnected from operational systems, leaving teams exposed during audits and integration cycles.
The situation this course is for
Teams face mounting pressure to adopt AI quickly while remaining compliant, but standard risk frameworks lack specificity for vendor due diligence in highly regulated environments. The gap between policy intent and operational execution creates friction, rework, and strategic delays.
Who this is for
Compliance officers, risk architects, technology leads, and product stewards in financial services, healthcare, utilities, and other regulated sectors who need to implement repeatable, audit-ready AI vendor assessment processes.
Who this is not for
This course is not for entry-level learners, academic researchers, or professionals focused solely on non-regulated AI use cases.
What you walk away with
- Apply a structured, operationally-sound framework to assess AI vendors from technical, legal, and compliance angles
- Integrate risk assessment workflows into existing procurement and governance lifecycles
- Produce audit-ready documentation that aligns with current regulatory expectations
- Anticipate and mitigate common failure points in AI vendor onboarding and monitoring
- Lead cross-functional teams through standardized due diligence with confidence
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk in Regulated Contexts
Establish core definitions, regulatory touchpoints, and the business case for operational rigor.
12 chapters in this module
- Defining AI vendor risk in context
- Regulatory drivers shaping due diligence
- The cost of non-compliance: real-world examples
- Stakeholder mapping across legal, risk, and tech
- Vendor lifecycle stages and risk exposure
- Differentiating AI from traditional software risk
- Ethical procurement principles
- Establishing governance boundaries
- Risk tolerance frameworks
- Assessment maturity models
- Cross-industry regulatory patterns
- Building a business-aligned risk posture
Module 2. Legal and Contractual Risk Integration
Embed legal safeguards into procurement workflows and vendor agreements.
12 chapters in this module
- AI-specific clauses in vendor contracts
- Data ownership and licensing terms
- Liability for model drift and errors
- Indemnification strategies
- Jurisdictional compliance alignment
- Enforceability of performance SLAs
- Right-to-audit provisions
- Termination and exit rights
- Subprocessor transparency requirements
- IP rights and derivative model ownership
- Regulatory change clauses
- Dispute resolution pathways
Module 3. Technical Due Diligence Frameworks
Evaluate AI vendors’ infrastructure, model development, and operational transparency.
12 chapters in this module
- Model documentation standards (e.g., datasheets, model cards)
- Source code and algorithmic transparency
- Training data provenance and bias testing
- Model validation and testing protocols
- Explainability and interpretability benchmarks
- Cybersecurity posture of vendor platforms
- API security and data-in-transit standards
- Infrastructure resilience and uptime SLAs
- Third-party dependency reviews
- Model update and versioning controls
- Monitoring for model drift and degradation
- Incident response readiness
Module 4. Compliance Alignment Across Domains
Map assessments to GDPR, HIPAA, SOC 2, NIST, and other frameworks.
12 chapters in this module
- GDPR alignment for AI data processing
- HIPAA compliance in health-adjacent AI
- NIST AI Risk Management Framework integration
- SOC 2 Type II assessment relevance
- CCPA and privacy regulation mapping
- Sector-specific regulatory bodies
- Audit trail requirements
- Data residency and sovereignty rules
- Retention and deletion policies
- Cross-border data flow compliance
- Regulatory reporting obligations
- Compliance automation opportunities
Module 5. Operational Integration Patterns
Embed vendor assessments into procurement, onboarding, and monitoring workflows.
12 chapters in this module
- Procurement process integration
- Staged vendor onboarding gates
- Cross-functional workflow design
- Risk scoring and tiering models
- Automated checklist deployment
- Integration with IT asset management
- Vendor performance dashboards
- Continuous monitoring protocols
- Quarterly review cycles
- Change management for model updates
- Incident escalation workflows
- Offboarding and data extraction
Module 6. Bias, Fairness, and Ethical Audit Design
Build repeatable processes to detect and mitigate AI bias in vendor systems.
12 chapters in this module
- Defining fairness metrics
- Disparate impact testing methods
- Bias detection across demographic groups
- Audit design for algorithmic fairness
- Third-party fairness certification
- Transparency in model decisioning
- Bias mitigation techniques
- Human-in-the-loop requirements
- Redress mechanisms for users
- Ethical review board integration
- Bias reporting expectations
- Public accountability frameworks
Module 7. Data Governance and Lineage Verification
Ensure data provenance, quality, and governance alignment in vendor AI systems.
12 chapters in this module
- Data lineage tracking methods
- Training data documentation standards
- Data quality validation techniques
- Synthetic data use and disclosure
- Data labeling and annotation practices
- Data retention in model training
- Consent and licensing verification
- Data minimization compliance
- Data access and sharing controls
- Data poisoning risk mitigation
- Data versioning and auditability
- Vendor data governance maturity models
Module 8. Resilience and Continuity Planning
Assess vendor preparedness for outages, degradation, and service disruption.
12 chapters in this module
- Uptime and availability benchmarks
- Disaster recovery planning
- Failover and redundancy design
- Degraded mode functionality
- Vendor financial stability checks
- Service continuity SLAs
- Incident response timelines
- Crisis communication protocols
- Third-party dependency risks
- Geopolitical exposure mapping
- Supply chain transparency
- Exit strategy and data portability
Module 9. Performance Validation and Monitoring
Establish benchmarks and tools to validate AI system performance over time.
12 chapters in this module
- Performance KPIs for AI systems
- Accuracy, precision, recall tracking
- Latency and throughput monitoring
- Model drift detection thresholds
- Real-world performance vs. claims
- Benchmarking against baselines
- Independent validation techniques
- Ongoing model testing cycles
- Feedback loop integration
- User-reported error tracking
- Model retraining triggers
- Performance reporting dashboards
Module 10. Cross-Functional Collaboration Models
Align legal, compliance, risk, IT, and business units on assessment outcomes.
12 chapters in this module
- Stakeholder communication protocols
- Shared risk lexicons
- Cross-functional assessment teams
- Decision rights and escalation paths
- Risk committee integration
- Executive reporting formats
- Conflict resolution frameworks
- Vendor negotiation alignment
- Change approval workflows
- Knowledge transfer protocols
- Training for non-technical stakeholders
- Continuous improvement feedback
Module 11. Audit-Ready Documentation Systems
Produce defensible, structured records for internal and external audits.
12 chapters in this module
- Document retention policies
- Version-controlled assessment records
- Automated evidence collection
- Compliance mapping matrices
- Audit trail generation
- Stakeholder attestation workflows
- Secure document storage
- Access control for assessment data
- Third-party evidence validation
- Regulatory inquiry response templates
- Pre-audit readiness checklists
- Document lifecycle management
Module 12. Scaling and Institutionalizing Best Practices
Embed vendor risk assessment into enterprise-wide AI governance.
12 chapters in this module
- Center of excellence design
- Standardized assessment templates
- Training programs for assessors
- Metrics for program maturity
- Lessons learned integration
- Benchmarking against peers
- Continuous improvement cycles
- Policy update workflows
- Knowledge base development
- Vendor ecosystem segmentation
- Strategic risk prioritization
- Future-proofing for emerging regulation
How this maps to your situation
- Onboarding a new AI vendor under tight compliance deadlines
- Responding to an internal audit finding on vendor oversight
- Designing a centralized AI risk function
- Scaling AI adoption across regulated business units
Before vs. after
Before
Manual, inconsistent assessments that delay procurement and create compliance gaps
After
A repeatable, audit-ready process that accelerates safe AI adoption across regulated functions
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 3-4 hours per module, designed for implementation alongside active projects.
If nothing changes
Without a structured approach, teams risk delayed AI adoption, audit findings, regulatory scrutiny, and reputational exposure due to vendor-related incidents.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level compliance overviews, this course delivers implementation-grade frameworks tailored to regulated industry needs, with tools and templates ready for immediate use.
Frequently asked
Who is this course designed for?
Compliance officers, risk managers, technology leads, and product stewards in regulated industries who need to implement robust AI vendor risk assessments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical or policy-focused?
It bridges both, offering technical due diligence frameworks alongside policy and compliance integration strategies for regulated environments.
$199 one-time. Approximately 3-4 hours per module, designed for implementation alongside active projects..
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