A tailored course, built for your situation
Practical AI Vendor Risk Assessment for Established Enterprises
Master enterprise-grade risk frameworks for AI procurement and integration
$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 adoption is accelerating, but inconsistent vendor evaluation creates hidden operational and reputational exposure.
The situation this course is for
Organizations are moving fast to adopt AI tools, but many lack standardized processes to assess third-party risk. This leads to fragmented compliance, integration delays, and governance gaps that only surface post-deployment.
Who this is for
Business and technology professionals in established enterprises responsible for AI procurement, risk management, compliance, data governance, or technology oversight.
Who this is not for
Startups in pre-product phase, individual developers, or teams using AI for non-enterprise use cases without formal governance requirements.
What you walk away with
- Apply a structured framework to evaluate AI vendor risk across 12 critical domains
- Build audit-compliant assessment workflows tailored to enterprise complexity
- Align legal, security, and operational teams around common evaluation criteria
- Reduce time-to-deployment by standardizing pre-contract due diligence
- Anticipate regulatory shifts through proactive control mapping and documentation
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk
Introduce core concepts, enterprise context, and the evolving regulatory landscape.
12 chapters in this module
- Defining AI vendor risk in enterprise settings
- Distinguishing AI from traditional software procurement
- Key stakeholders in AI governance
- Regulatory drivers shaping vendor assessment
- Industry benchmarks for due diligence
- Common misconceptions about AI risk
- The role of ethics in vendor selection
- Enterprise maturity models for AI adoption
- Mapping AI use cases to risk profiles
- Vendor lifecycle overview
- Internal alignment prerequisites
- Building the case for structured assessment
Module 2. Legal and Compliance Frameworks
Cover contractual, data protection, and jurisdictional considerations.
12 chapters in this module
- Data sovereignty requirements by region
- GDPR and equivalent standards in AI processing
- Intellectual property ownership clauses
- Liability allocation in AI failures
- Audit rights and transparency provisions
- Export control implications
- Subprocessor disclosure management
- Compliance with sector-specific regulations
- Managing cross-border data flows
- Standard contract terms for AI vendors
- Negotiation levers for risk mitigation
- Documentation for legal defensibility
Module 3. Technical Risk Assessment
Evaluate vendor architecture, model behavior, and integration safety.
12 chapters in this module
- Model provenance and training data transparency
- API security and authentication standards
- System reliability and uptime SLAs
- Model drift detection capabilities
- Explainability and interpretability standards
- Bias detection and fairness metrics
- Adversarial robustness testing
- Model versioning and update policies
- Integration with existing tech stack
- Scalability and performance benchmarks
- Disaster recovery and failover plans
- Logging and monitoring requirements
Module 4. Operational Due Diligence
Assess vendor operational maturity and support readiness.
12 chapters in this module
- Vendor organizational stability
- Support response time commitments
- Incident escalation procedures
- Change management processes
- Patch deployment frequency
- Business continuity planning
- Staffing and expertise validation
- Customer reference evaluation
- Onboarding and training quality
- Knowledge transfer mechanisms
- Performance reporting transparency
- Exit strategy and data portability
Module 5. Ethical and Social Impact
Incorporate ethical design, societal impact, and responsible AI principles.
12 chapters in this module
- Human oversight requirements
- Use case appropriateness assessment
- Potential for misuse or abuse
- Community impact considerations
- Diversity in development teams
- Transparency in model limitations
- Fairness across demographic groups
- Environmental impact of AI systems
- Stakeholder consultation practices
- Redress mechanisms for harm
- Public perception risks
- Ethics board or review process
Module 6. Security and Data Protection
Detail cybersecurity standards, data handling, and breach preparedness.
12 chapters in this module
- Data encryption in transit and at rest
- Access control and identity management
- Penetration testing results review
- Vulnerability disclosure policies
- Zero-trust architecture alignment
- SOC 2 and equivalent certifications
- Third-party security audits
- Data minimization practices
- Breach notification timelines
- Incident response coordination
- Credential management standards
- Logging and forensic readiness
Module 7. Financial and Business Stability
Evaluate vendor financial health and long-term viability.
12 chapters in this module
- Revenue model sustainability
- Funding stage and runway
- Customer concentration risk
- Profitability trends
- Key person dependency
- Market differentiation strength
- Churn rate analysis
- Growth trajectory assessment
- Burn rate and funding needs
- M&A risk and ownership changes
- Insurance coverage review
- Exit risk and sunset planning
Module 8. Regulatory Readiness
Prepare for current and emerging regulatory requirements.
12 chapters in this module
- EU AI Act compliance mapping
- U.S. federal AI guidance alignment
- Sector-specific rule anticipation
- Local ordinance considerations
- Regulatory sandbox participation
- Compliance certification pathways
- Oversight body engagement
- Enforcement precedent review
- Labeling and disclosure rules
- High-risk AI classification
- Third-party audit preparedness
- Regulatory change monitoring
Module 9. Cross-Functional Alignment
Coordinate legal, IT, security, procurement, and business units.
12 chapters in this module
- Stakeholder identification matrix
- Governance committee structure
- Decision rights and escalation paths
- Communication protocols
- Shared documentation standards
- Conflict resolution frameworks
- Change approval workflows
- Risk appetite alignment
- Budget ownership clarity
- Performance metric consensus
- Vendor performance review cycles
- Lessons learned integration
Module 10. Assessment Workflow Design
Build scalable, repeatable evaluation processes.
12 chapters in this module
- Checklist development for RFPs
- Scoring model creation
- Weighted criteria frameworks
- Automated screening tools
- Manual review thresholds
- Document collection systems
- Version control for assessments
- Reviewer assignment logic
- Time-to-completion benchmarks
- Integration with procurement systems
- Continuous monitoring setup
- Feedback loops for improvement
Module 11. Implementation Playbook
Deploy assessment frameworks in real enterprise environments.
12 chapters in this module
- Pilot program design
- Stakeholder onboarding plan
- Training materials development
- Toolchain integration
- Process documentation
- KPI definition and tracking
- Change management communication
- Vendor onboarding coordination
- Internal audit preparation
- Scaling from pilot to org-wide
- Lessons from early adopters
- Sustaining momentum post-launch
Module 12. Future-Proofing and Evolution
Adapt frameworks as AI capabilities and regulations evolve.
12 chapters in this module
- Monitoring emerging AI capabilities
- Tracking regulatory developments
- Updating risk criteria annually
- Reassessing existing vendors
- Integrating new control frameworks
- Benchmarking against peers
- Investing in team upskilling
- Scenario planning for disruption
- Building internal AI expertise
- Engaging with standards bodies
- Contributing to best practices
- Leading governance innovation
How this maps to your situation
- Evaluating a new AI vendor for enterprise deployment
- Scaling AI adoption across multiple departments
- Responding to increased board-level scrutiny of AI risk
- Preparing for upcoming regulatory audits
Before vs. after
Before
Uncertainty in vendor evaluation, inconsistent criteria, and reactive risk management.
After
Structured, repeatable, and defensible AI vendor risk assessment 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 minutes per module, designed for busy professionals to complete at their own pace over 6-8 weeks.
If nothing changes
Without a standardized approach, organizations face increased compliance exposure, integration failures, and reputational damage from poorly vetted AI vendors.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level compliance webinars, this program delivers actionable, implementation-grade frameworks specifically for enterprise AI vendor evaluation, complete with templates, scoring models, and a real-world playbook.
Frequently asked
Who is this course designed for?
Business and technology professionals in established enterprises involved in AI procurement, risk management, compliance, or technology governance.
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 awarded after finishing all modules and passing the final assessment.
$199 one-time. Approximately 45-60 minutes per module, designed for busy professionals to complete at their own pace over 6-8 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