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Strategic AI Vendor Risk Assessment for Compliance Officers

$199.00
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A tailored course, built for your situation

Strategic AI Vendor Risk Assessment for Compliance Officers

Master the frameworks, controls, and governance practices to confidently onboard and monitor AI vendors in regulated 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.
AI vendor assessments are often inconsistent, reactive, or lack alignment with compliance mandates, leading to delays, rework, and regulatory exposure.

The situation this course is for

Compliance teams face increasing pressure to evaluate AI vendors quickly and thoroughly, but without standardized methods, assessments vary in quality and depth. This creates bottlenecks, inconsistent risk ratings, and gaps in oversight, especially when dealing with fast-moving innovation in AI services.

Who this is for

Compliance officers, risk leads, and governance professionals in technology-driven organizations who are responsible for third-party risk and AI governance.

Who this is not for

This course is not for engineers building AI models in-house or vendors marketing AI tools. It is designed specifically for compliance and risk professionals assessing external AI providers.

What you walk away with

  • Apply a repeatable risk classification framework for AI vendors
  • Align vendor assessments with ISO, NIST, and GDPR-aligned controls
  • Negotiate AI contracts with stronger data protection and audit rights
  • Design ongoing monitoring programs for AI service performance and compliance
  • Lead cross-functional AI vendor reviews with confidence and clarity

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk
Establish core definitions, risk categories, and the evolving regulatory landscape shaping AI vendor oversight.
12 chapters in this module
  1. Defining AI in the context of third-party services
  2. Common use cases and associated risk profiles
  3. Regulatory trends influencing vendor assessment
  4. The role of compliance in AI governance
  5. Risk vs innovation: balancing priorities
  6. Key stakeholders in the vendor review process
  7. Overview of global AI governance frameworks
  8. Mapping AI risk to existing compliance programs
  9. Case study: AI chatbot in customer service
  10. Case study: AI-driven underwriting in finance
  11. Common pitfalls in early-stage assessments
  12. Building a business case for structured AI risk review
Module 2. AI Risk Classification Models
Learn to categorize AI vendors by risk level using data sensitivity, autonomy, and impact criteria.
12 chapters in this module
  1. Principles of risk tiering for AI systems
  2. Data classification and its impact on vendor risk
  3. Autonomy levels and decision-making authority
  4. Assessing impact on individuals and operations
  5. Scoring models for risk prioritization
  6. Low-risk vs high-risk AI use cases
  7. Dynamic reclassification over time
  8. Aligning risk tiers with due diligence depth
  9. Worked example: Document processing AI
  10. Worked example: Predictive analytics platform
  11. Integrating classification into intake workflows
  12. Validating risk scores with cross-functional teams
Module 3. Control Framework Alignment
Map AI vendor risks to established controls from NIST, ISO, and sector-specific standards.
12 chapters in this module
  1. Overview of NIST AI Risk Management Framework
  2. Mapping AI risks to NIST SP 800-53 controls
  3. Applying ISO/IEC 42001 for AI management systems
  4. GDPR and AI: data protection by design
  5. Sector-specific requirements: finance, health, public sector
  6. Control gaps in vendor documentation
  7. Using control matrices for consistent assessment
  8. Third-party attestations: SOC 2, ISO 27001, and beyond
  9. Worked example: AI payroll system
  10. Worked example: AI-powered recruitment tool
  11. Handling incomplete or redacted vendor evidence
  12. Building internal control libraries for AI
Module 4. Due Diligence Process Design
Structure a scalable, repeatable due diligence workflow for AI vendor assessments.
12 chapters in this module
  1. Designing intake forms for AI vendors
  2. Automating preliminary risk screening
  3. Questionnaire design: clarity, scope, and depth
  4. Follow-up protocols for incomplete responses
  5. Engaging legal, security, and product teams
  6. Timeboxing assessment phases
  7. Documentation standards for audit readiness
  8. Version control for assessment artifacts
  9. Worked example: AI legal contract review tool
  10. Worked example: AI customer segmentation engine
  11. Managing vendor resistance to scrutiny
  12. Scaling due diligence across multiple business units
Module 5. Contractual Risk Mitigation
Identify and negotiate high-impact clauses in AI vendor contracts.
12 chapters in this module
  1. Key contractual risks in AI service agreements
  2. Data ownership and usage rights
  3. Model transparency and explainability commitments
  4. Audit rights and access to training data
  5. Incident response and breach notification
  6. Liability for algorithmic harm or bias
  7. Right to exit and data portability
  8. Subprocessor management and chain of custody
  9. Worked example: Cloud-based AI inference API
  10. Worked example: On-premise AI inference server
  11. Negotiation tactics for compliance teams
  12. Maintaining contract consistency across vendors
Module 6. Model Transparency and Explainability
Evaluate vendor claims about model behavior, bias testing, and decision explainability.
12 chapters in this module
  1. Understanding model transparency reports
  2. Types of explainability: local, global, feature importance
  3. Bias detection and mitigation strategies
  4. Vendor documentation: what to request and verify
  5. Testing model outputs for fairness
  6. Handling black-box models with limited disclosure
  7. Third-party model audits: feasibility and scope
  8. Setting internal thresholds for acceptable opacity
  9. Worked example: Credit scoring AI
  10. Worked example: AI-powered diagnostic assistant
  11. Communicating model limitations to stakeholders
  12. Building internal review checklists for transparency
Module 7. Data Governance and Privacy
Assess how AI vendors collect, process, store, and protect data across the lifecycle.
12 chapters in this module
  1. Data provenance and lineage tracking
  2. Training data composition and sourcing
  3. Synthetic data: benefits and validation needs
  4. Data retention and deletion policies
  5. Cross-border data transfer mechanisms
  6. Encryption standards for data at rest and in transit
  7. Access controls and role-based permissions
  8. Data minimization in AI workflows
  9. Worked example: AI voice assistant with voiceprint storage
  10. Worked example: AI-driven customer support transcript analysis
  11. Handling PII and special category data
  12. Validating vendor data practices through evidence
Module 8. Security and Infrastructure Review
Evaluate the technical environment where AI models are hosted, trained, and served.
12 chapters in this module
  1. Cloud vs on-premise vs hybrid deployment risks
  2. Model serving infrastructure security
  3. API security and rate limiting
  4. Model poisoning and adversarial attack risks
  5. Secure model update and versioning
  6. Infrastructure as code and configuration management
  7. Penetration testing and vulnerability disclosure
  8. Incident response planning for AI systems
  9. Worked example: AI fraud detection in payments
  10. Worked example: AI content moderation system
  11. Assessing vendor SOC 2 and penetration test reports
  12. Red teaming AI systems: scope and limitations
Module 9. Performance Monitoring and KPIs
Define and track key performance indicators for AI vendor systems post-onboarding.
12 chapters in this module
  1. Defining success metrics for AI services
  2. Accuracy, precision, recall, and F1 score
  3. Latency, uptime, and service level agreements
  4. Drift detection: data, concept, and model decay
  5. Monitoring for degraded performance
  6. Feedback loops and user-reported issues
  7. Automated alerting and escalation paths
  8. Quarterly business reviews with vendors
  9. Worked example: AI document classification system
  10. Worked example: AI-powered forecasting tool
  11. Benchmarking against internal baselines
  12. Reporting performance to leadership and auditors
Module 10. Ongoing Compliance Oversight
Design continuous monitoring programs to maintain compliance throughout the vendor lifecycle.
12 chapters in this module
  1. Annual review cycles and trigger-based reassessments
  2. Updating risk classifications as systems evolve
  3. Reassessing controls after incidents or changes
  4. Vendor change management processes
  5. Monitoring regulatory updates affecting AI
  6. Internal audit coordination
  7. Preparing for external audits and exams
  8. Maintaining assessment records and evidence
  9. Worked example: AI-powered claims processing
  10. Worked example: AI-driven marketing personalization
  11. Handling vendor mergers or ownership changes
  12. Decommissioning AI services securely
Module 11. Cross-Functional Collaboration
Lead effective coordination between compliance, legal, security, and business units.
12 chapters in this module
  1. Building a center of excellence for AI governance
  2. RACI models for AI vendor reviews
  3. Facilitating alignment across departments
  4. Communicating risk to non-technical leaders
  5. Training business teams on AI risk basics
  6. Escalation paths for high-risk findings
  7. Documenting decisions and rationale
  8. Managing conflicting priorities
  9. Worked example: AI-powered HR screening
  10. Worked example: AI inventory forecasting
  11. Creating standardized playbooks for collaboration
  12. Measuring team effectiveness in vendor reviews
Module 12. Future-Proofing AI Governance
Anticipate emerging risks and adapt frameworks for next-generation AI capabilities.
12 chapters in this module
  1. Generative AI: new risk dimensions
  2. Multimodal models and expanded data types
  3. Autonomous agents and decision delegation
  4. Regulatory horizon scanning
  5. Preparing for AI liability laws
  6. Ethical AI principles in vendor selection
  7. Sustainability and carbon footprint of AI models
  8. Open source vs proprietary model risks
  9. Worked example: AI code generation assistant
  10. Worked example: AI customer service avatar
  11. Building adaptive governance frameworks
  12. Positioning compliance as an innovation enabler

How this maps to your situation

  • Onboarding a new AI vendor with minimal documentation
  • Responding to an internal audit finding on AI risk coverage
  • Designing a company-wide AI vendor review process
  • Negotiating a high-stakes AI contract with aggressive timelines

Before vs. after

Before
AI vendor assessments are inconsistent, reactive, and lack alignment with compliance mandates, leading to delays, rework, and regulatory exposure.
After
You lead structured, repeatable AI vendor reviews that align with global standards, reduce risk, and accelerate time-to-value, positioning compliance as a strategic enabler.

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 of self-paced learning, designed to be completed over 6, 8 weeks with weekly module focus.

If nothing changes
Without a structured approach, organizations face inconsistent risk coverage, regulatory scrutiny, and potential harm from undetected model bias, data misuse, or security gaps in AI systems.

How this compares to the alternatives

Unlike generic third-party risk courses, this program focuses exclusively on AI-specific risks, controls, and implementation tools. It goes beyond theory to provide actionable templates, scoring models, and negotiation guidance tailored to compliance officers.

Frequently asked

Who is this course designed for?
Compliance officers, risk leads, and governance professionals responsible for assessing third-party AI vendors in regulated environments.
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 after finishing all modules and passing the final assessment.
$199 one-time. Approximately 45, 60 hours of self-paced learning, designed to be completed over 6, 8 weeks with weekly module focus..

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
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