A tailored course, built for your situation
Implementation-Focused AI Vendor Risk Assessment for Regulated Industries
A structured, actionable path to mastering AI vendor risk in compliance-heavy 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.
Deploying AI vendors without a clear risk assessment framework creates friction, delays, and compliance exposure.
The situation this course is for
Teams in regulated industries often move quickly to adopt AI solutions but lack standardized methods to assess vendor risk. This leads to inconsistent evaluations, rework during audits, and potential misalignment with compliance requirements. Without a structured approach, organizations risk inefficiency, reputational impact, and operational bottlenecks.
Who this is for
Business and technology professionals in regulated industries, compliance officers, risk managers, IT leaders, data governance leads, and product owners, who are responsible for evaluating or overseeing third-party AI solutions.
Who this is not for
This course is not for software developers building AI models from scratch or for individuals seeking theoretical overviews of AI ethics without implementation context.
What you walk away with
- Apply a repeatable framework to assess AI vendor risk across technical, legal, and operational domains
- Align vendor evaluations with current regulatory expectations in highly regulated environments
- Use practical templates to streamline due diligence and documentation
- Design contract language and SLAs that protect organizational interests
- Implement ongoing monitoring strategies for AI vendor performance and compliance
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk in Regulated Contexts
Establish core concepts, regulatory touchpoints, and risk categories unique to AI vendors.
12 chapters in this module
- Introduction to AI vendor ecosystems
- Regulatory landscape overview
- Key risk domains: privacy, bias, transparency
- Differences from traditional software procurement
- Stakeholder mapping in AI risk assessment
- Defining success in vendor governance
- Common failure patterns and root causes
- Risk tolerance and organizational appetite
- Case study: Healthcare AI procurement
- Case study: Financial services onboarding
- Emerging standards and frameworks
- Setting your assessment baseline
Module 2. Scoping the AI Vendor Engagement
Define boundaries, use cases, and risk thresholds before evaluation begins.
12 chapters in this module
- Identifying AI-powered components in vendor offerings
- Use case classification and impact scoring
- Data flow mapping fundamentals
- Determining system criticality
- Engagement size and complexity tiers
- Internal alignment checklist
- Pre-assessment stakeholder interviews
- Documenting assumptions and constraints
- Creating the scoping memo
- Version control for assessment artifacts
- Tools for collaborative scoping
- Avoiding scope creep in early stages
Module 3. Regulatory Alignment and Compliance Mapping
Map vendor capabilities to relevant regulations and internal policies.
12 chapters in this module
- GDPR and data protection implications
- HIPAA considerations for health-related AI
- Financial industry compliance touchpoints
- Sector-specific audit requirements
- Internal policy alignment checklist
- Regulatory change monitoring
- Evidence collection strategies
- Gap analysis methodology
- Working with legal and compliance teams
- Documenting compliance posture
- Third-party attestation review
- Preparing for regulatory inquiries
Module 4. Technical Due Diligence Framework
Evaluate the vendor's technical infrastructure, model practices, and security controls.
12 chapters in this module
- Model development lifecycle review
- Data provenance and training set evaluation
- Algorithmic transparency and explainability
- Security architecture assessment
- Penetration testing and red team results
- Infrastructure resilience and uptime
- Encryption and data handling practices
- API security and integration risks
- Incident response and breach notification
- Patch management and update cycles
- Vendor SOC 2 and ISO 27001 review
- Technical debt and scalability concerns
Module 5. Operational Risk and Business Continuity
Assess the vendor’s operational stability and contingency planning.
12 chapters in this module
- Vendor financial health indicators
- Team structure and key personnel
- Service level agreements and uptime
- Disaster recovery and backup processes
- Business continuity planning review
- Single points of failure analysis
- Subcontractor and supply chain risk
- Change management procedures
- Update and deprecation policies
- Support responsiveness and escalation
- Knowledge transfer and documentation
- Exit strategy and data portability
Module 6. Ethics, Bias, and Fairness Evaluation
Implement structured methods to identify and mitigate algorithmic bias.
12 chapters in this module
- Defining fairness in context
- Bias detection across demographic groups
- Pre-processing, in-model, and post-processing techniques
- Disparate impact analysis
- Third-party bias audit reports
- Ongoing fairness monitoring
- Stakeholder feedback loops
- Ethics review board alignment
- Transparency with end users
- Model card and datasheet review
- Bias mitigation playbooks
- Public accountability and disclosure
Module 7. Contractual Safeguards and Legal Guardrails
Build enforceable protections into procurement agreements.
12 chapters in this module
- Right-to-audit clauses
- Data ownership and licensing terms
- IP and model output rights
- Liability and indemnification
- Warranties and representations
- Termination and exit clauses
- Data deletion and return processes
- Subprocessor approval workflows
- Compliance certification obligations
- Insurance and financial backing
- Dispute resolution mechanisms
- Renewal and pricing lock-ins
Module 8. Vendor Onboarding and Integration Planning
Ensure smooth deployment with risk-aware integration practices.
12 chapters in this module
- Staged rollout strategies
- Pilot program design
- Integration with existing systems
- User training and change management
- Access control and identity management
- Monitoring and logging setup
- Performance benchmarking
- Feedback collection mechanisms
- Documentation requirements
- Handoff to operations teams
- Post-onboarding review process
- Lessons learned capture
Module 9. Ongoing Monitoring and Performance Tracking
Establish continuous oversight to detect emerging risks.
12 chapters in this module
- Key risk indicators (KRIs) for AI vendors
- Performance metric dashboards
- Automated alerting systems
- Quarterly review cadence
- Model drift and degradation detection
- User complaint analysis
- Regulatory change impact assessment
- Third-party audit follow-ups
- Compliance exception tracking
- Vendor self-assessment review
- Escalation pathways for issues
- Reporting to risk committees
Module 10. Incident Response and Remediation Planning
Prepare for and respond to AI-related incidents effectively.
12 chapters in this module
- Defining AI incident types
- Detection and triage protocols
- Cross-functional response team
- Communication plan for stakeholders
- Regulatory reporting obligations
- Root cause analysis techniques
- Remediation tracking system
- Public relations considerations
- Legal hold and evidence preservation
- Post-incident review process
- Updating risk assessments post-event
- Vendor accountability enforcement
Module 11. Scaling AI Vendor Risk Across the Portfolio
Extend individual assessments into enterprise-wide governance.
12 chapters in this module
- Risk tiering across vendor portfolio
- Centralized vendor inventory
- Standardized assessment templates
- Automated scoring engines
- Cross-departmental governance model
- Executive reporting dashboards
- Training for procurement teams
- Integration with GRC platforms
- Continuous improvement cycle
- Benchmarking against peers
- Resource allocation for oversight
- Long-term strategy alignment
Module 12. Future-Proofing and Strategic Adaptation
Anticipate changes in AI regulation, technology, and market expectations.
12 chapters in this module
- Tracking emerging AI legislation
- Scenario planning for regulatory shifts
- Technology horizon scanning
- Adaptive risk framework design
- Stakeholder engagement evolution
- Investor and board expectations
- Public trust and brand impact
- Sustainable AI practices
- Open-source vs. proprietary trade-offs
- Global expansion considerations
- Workforce implications and upskilling
- Final integration playbook review
How this maps to your situation
- Evaluating a new AI vendor for a high-impact project
- Responding to internal audit findings on vendor oversight
- Designing a centralized AI governance program
- Preparing for regulatory scrutiny on third-party AI use
Before vs. after
Before
Uncertain, inconsistent, or reactive approaches to AI vendor evaluation that rely on ad-hoc checklists and fragmented stakeholder input.
After
A confident, repeatable, and auditable process for assessing and managing AI vendor risk across the organization.
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 45, 60 hours total, designed for flexible, self-paced learning with actionable checkpoints.
If nothing changes
Without a structured approach, organizations face increased compliance friction, operational surprises, and reputational exposure, especially as AI oversight becomes a board-level priority.
How this compares to the alternatives
Unlike general AI ethics courses or high-level compliance overviews, this program delivers implementation-grade tools, real-world templates, and a step-by-step playbook tailored to regulated environments.
Frequently asked
Who is this course designed for?
Business and technology professionals in regulated industries who are responsible for evaluating, procuring, or governing third-party AI solutions.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate of completion?
Yes, a certificate is awarded upon finishing all modules and passing the final assessment.
$199 one-time. Approximately 45, 60 hours total, designed for flexible, self-paced learning with actionable checkpoints..
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