A tailored course, built for your situation
Pragmatic AI Vendor Risk Assessment for Regulated Industries
A structured, implementation-grade path for professionals navigating AI procurement in compliance-sensitive 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.
Navigating AI vendor evaluations without clear, actionable frameworks can slow innovation and increase compliance exposure.
The situation this course is for
Teams in regulated sectors often lack standardized methods to evaluate AI vendors, leading to inconsistent risk assessments, delayed deployments, and misalignment between legal, security, and operations.
Who this is for
Compliance officers, risk managers, technology procurement leads, and product leaders in financial services, healthcare, insurance, and government-adjacent sectors.
Who this is not for
This course is not for data scientists building AI models or developers focused on technical architecture. It’s for those assessing third-party AI solutions from a governance, risk, and operational due diligence perspective.
What you walk away with
- Apply a repeatable framework to assess AI vendor risk across technical, legal, and operational domains
- Map vendor claims to regulatory requirements in real time
- Identify red flags in AI vendor documentation, SLAs, and data practices
- Build defensible procurement packages that satisfy internal audit and oversight bodies
- Implement risk-scoring systems tailored to organizational risk appetite
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk
Introduces core concepts, regulatory touchpoints, and the evolving landscape of third-party AI risk.
12 chapters in this module
- Defining AI vendor risk in context
- Key stakeholders in the assessment process
- Regulatory drivers shaping vendor evaluation
- Common misconceptions about AI compliance
- The role of procurement in risk governance
- Vendor lifecycle overview
- Risk vs. innovation tradeoffs
- Industry-specific considerations
- Baseline assessment frameworks
- Evaluating vendor transparency
- Understanding AI use-case risk tiers
- Getting started: initial checklist
Module 2. Regulatory and Compliance Mapping
How to align AI vendor assessments with current compliance mandates and reporting standards.
12 chapters in this module
- Mapping to GDPR, HIPAA, and SOX implications
- Sector-specific regulatory bodies and expectations
- AI in financial services: key compliance touchpoints
- Healthcare AI and patient data safeguards
- Privacy by design in vendor contracts
- Audit readiness for AI systems
- Data sovereignty and jurisdictional risk
- Handling cross-border data flows
- Compliance documentation requirements
- Internal policy alignment
- Certifications to look for in vendors
- Checklist: compliance red flags
Module 3. Vendor Documentation Analysis
Techniques for extracting meaningful risk signals from AI vendor materials and marketing claims.
12 chapters in this module
- Reading between the lines of vendor whitepapers
- Interpreting model cards and data sheets
- Evaluating claims of fairness and bias mitigation
- Spotting vague or misleading terminology
- Understanding training data provenance
- Assessing model performance claims
- Red flags in vendor case studies
- Interpreting accuracy metrics responsibly
- Handling 'black box' claims
- Evaluating explainability commitments
- Third-party validation indicators
- Checklist: documentation completeness
Module 4. Data Governance and Security
Assessing how AI vendors handle data access, storage, encryption, and breach response.
12 chapters in this module
- Data handling policies in AI workflows
- Encryption standards for training and inference
- Access control models in vendor environments
- Breach notification timelines and protocols
- Data retention and deletion commitments
- Third-party data sharing disclosures
- API security and integration risks
- Penetration testing disclosures
- SOC 2 and ISO 27001 alignment
- Secure development lifecycle review
- Incident response planning
- Checklist: data risk scoring
Module 5. Model Risk Management Frameworks
Applying structured approaches to evaluate AI model behavior, performance, and monitoring.
12 chapters in this module
- Model risk in regulated contexts
- Lifecycle stages of model validation
- Performance decay and drift detection
- Bias and fairness evaluation methods
- Human-in-the-loop requirements
- Model monitoring commitments
- Revalidation frequency expectations
- Model documentation standards
- Handling edge cases and failures
- Model lineage and version tracking
- Fallback mechanisms and fail-safes
- Checklist: model risk scorecard
Module 6. Contractual and SLA Evaluation
Key clauses to scrutinize in AI vendor contracts and service level agreements.
12 chapters in this module
- Defining AI-specific SLAs
- Uptime and availability commitments
- Remediation processes for underperformance
- Liability for incorrect or harmful outputs
- Indemnification clauses
- Termination rights and data portability
- Right to audit provisions
- Subcontractor oversight
- IP ownership and usage rights
- Change management processes
- Pricing model transparency
- Checklist: contract red lines
Module 7. Third-Party Validation and Audits
How to interpret and request independent assessments of AI vendors.
12 chapters in this module
- Types of third-party audits available
- Understanding audit scope and limitations
- Penetration testing vs. compliance audits
- Ethical AI audits: what they cover
- Evaluating audit firm credibility
- Requesting audit reports from vendors
- Redacting sensitive findings appropriately
- Benchmarking against peer vendors
- Continuous monitoring options
- Audit trail access rights
- Vendor transparency score
- Checklist: audit readiness
Module 8. Stakeholder Alignment and Communication
Strategies for aligning legal, compliance, IT, and business teams on AI vendor risk.
12 chapters in this module
- Building cross-functional assessment teams
- Translating technical risk for executives
- Communicating findings to non-technical leaders
- Aligning risk appetite across departments
- Escalation pathways for high-risk vendors
- Creating shared assessment templates
- Vendor review board structures
- Documenting decision rationale
- Managing conflicting priorities
- Change management for new vendors
- Training teams on risk criteria
- Checklist: stakeholder alignment
Module 9. Operational Risk Scoring
A practical system for assigning risk scores to AI vendors based on evidence and thresholds.
12 chapters in this module
- Designing a risk scoring rubric
- Weighting regulatory, technical, and operational factors
- Setting risk thresholds for approval
- Scoring data governance practices
- Evaluating model reliability
- Assessing vendor financial stability
- Reputation and track record analysis
- Supply chain risk considerations
- Scoring third-party dependencies
- Dynamic risk reassessment cycles
- Risk score reporting formats
- Checklist: operational risk scorecard
Module 10. Implementation Playbook Integration
Applying the course framework using the included hand-built implementation playbook.
12 chapters in this module
- Using the playbook for first assessment
- Customizing templates to your organization
- Setting up a vendor intake process
- Integrating with procurement workflows
- Onboarding team members to the framework
- Running a pilot assessment
- Documenting findings efficiently
- Presenting results to leadership
- Tracking vendor performance over time
- Updating assessments with new data
- Scaling across multiple vendors
- Checklist: first 30 days
Module 11. Continuous Monitoring and Reassessment
Establishing ongoing oversight for AI vendors after initial deployment.
12 chapters in this module
- Designing reassessment schedules
- Monitoring for model drift and degradation
- Tracking regulatory changes affecting vendors
- Vendor incident reporting expectations
- Annual review processes
- Handling vendor ownership changes
- Monitoring for new vulnerabilities
- Updating risk scores dynamically
- Reassessment communication plan
- Exit strategy triggers
- Auditing vendor updates and patches
- Checklist: continuous monitoring
Module 12. Scaling Across the Organization
Expanding AI vendor risk practices enterprise-wide.
12 chapters in this module
- Building a central oversight function
- Standardizing assessment criteria
- Training regional teams
- Creating vendor risk libraries
- Integrating with GRC platforms
- Reporting to executive leadership
- Benchmarking against industry peers
- Sharing best practices
- Managing vendor risk at scale
- Future-proofing for emerging AI types
- Evolving the framework over time
- Checklist: organizational rollout
How this maps to your situation
- Assessing a new AI vendor for procurement
- Responding to internal audit findings on AI use
- Building a vendor risk framework from scratch
- Scaling existing risk practices to new AI tools
Before vs. after
Before
Uncertain about how to systematically evaluate AI vendors in a regulated environment, relying on ad-hoc reviews and fragmented input from different teams.
After
Confidently lead AI vendor assessments with a repeatable, defensible framework that satisfies compliance, security, and business stakeholders.
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 self-paced learning with immediate applicability.
If nothing changes
Without a structured approach, organizations risk inconsistent evaluations, compliance gaps, and delayed innovation, leaving them exposed to regulatory scrutiny and operational disruptions.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level compliance overviews, this program delivers implementation-grade tools specifically for assessing third-party AI vendors in regulated settings, complete with templates, scoring systems, and real-world application guidance.
Frequently asked
Who is this course for?
Compliance officers, risk managers, procurement leads, and technology leaders in regulated industries evaluating 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, upon finishing all modules, participants receive a certificate of completion from The Art of Service.
$199 one-time. Approximately 4, 6 hours per module, designed for self-paced learning with immediate applicability..
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