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Production-Grade AI Vendor Risk Assessment for Regulated Industries

$199.00
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A tailored course, built for your situation

Production-Grade AI Vendor Risk Assessment for Regulated Industries

A 12-module implementation framework for compliance, risk, and technology leaders

$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 risk is no longer just a compliance checkbox, it’s a strategic execution challenge.

The situation this course is for

Teams in regulated industries are being asked to assess AI vendors with confidence, but lack standardized, scalable methods. Generic frameworks don’t address real-world integration risks, audit trails, or regulatory scrutiny. Without an implementation-grade approach, assessments remain reactive, inconsistent, and difficult to defend.

Who this is for

Compliance officers, risk managers, legal advisors, and technology leads in financial services, healthcare, legal, and government sectors who need to evaluate, approve, and monitor AI vendors with precision.

Who this is not for

This course is not for individuals seeking introductory AI ethics overviews or academic discussions. It is not designed for non-regulated consumer tech environments or teams not involved in vendor due diligence or governance.

What you walk away with

  • Apply a repeatable, auditable framework for AI vendor risk assessment
  • Align technical, legal, and compliance requirements across stakeholders
  • Identify hidden risks in AI vendor architectures and data practices
  • Negotiate stronger contractual and operational safeguards
  • Deploy a customized implementation playbook tailored to regulated environments

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk in Regulated Contexts
Establish core principles, regulatory touchpoints, and risk categories unique to AI vendors in high-compliance environments.
12 chapters in this module
  1. Defining AI vendor risk beyond general cybersecurity
  2. Regulatory frameworks shaping vendor oversight
  3. Key differences: AI vs traditional software vendors
  4. Risk taxonomy for AI systems in legal and financial settings
  5. The role of governance, accountability, and transparency
  6. Jurisdictional considerations in cross-border AI use
  7. Emerging expectations from auditors and regulators
  8. Mapping AI risk to existing compliance programs
  9. Stakeholder alignment: legal, risk, IT, and procurement
  10. Common misconceptions in early-stage AI vendor reviews
  11. Building a risk-aware vendor intake process
  12. Case study: AI due diligence in a global law firm
Module 2. Technical Due Diligence for AI Systems
Assess model architecture, data provenance, and system integrity with technical precision.
12 chapters in this module
  1. Understanding model inputs, outputs, and decision logic
  2. Evaluating training data quality and bias mitigation
  3. Model versioning, reproducibility, and audit trails
  4. Inference pipeline security and monitoring
  5. API security and third-party dependencies
  6. Model drift detection and retraining protocols
  7. Explainability requirements for regulated decisions
  8. Assessing model performance under stress conditions
  9. Vendor transparency on model limitations
  10. Reverse-engineering risk from documentation gaps
  11. Technical debt in vendor AI platforms
  12. Checklist: Technical red flags in vendor assessments
Module 3. Regulatory Alignment and Compliance Mapping
Map vendor practices to GDPR, HIPAA, CCPA, FINRA, and other relevant standards.
12 chapters in this module
  1. Crosswalk of AI risk to data protection regulations
  2. Mapping vendor controls to HIPAA and PHI handling
  3. AI and financial compliance: FINRA, SEC, and MiFID II
  4. Consumer rights under AI-driven decisioning
  5. Recordkeeping and audit trail requirements
  6. Jurisdiction-specific AI disclosure obligations
  7. Aligning with NIST AI Risk Management Framework
  8. Mapping to ISO/IEC standards for AI systems
  9. Compliance by design in vendor onboarding
  10. Handling regulatory inquiries about AI vendors
  11. Vendor documentation that satisfies compliance reviewers
  12. Case study: Aligning AI vendor use with legal ethics rules
Module 4. Contractual Risk Mitigation and SLAs
Draft and negotiate enforceable terms that protect organizational interests.
12 chapters in this module
  1. Key clauses for AI vendor contracts
  2. Defining performance metrics and success criteria
  3. Service level agreements for model uptime and accuracy
  4. Liability for incorrect or biased AI outputs
  5. Indemnification for regulatory penalties
  6. Data ownership and usage rights in AI systems
  7. Right to audit and inspection protocols
  8. Exit strategies and data portability
  9. Penalties for model drift or performance degradation
  10. Subcontractor and supply chain disclosures
  11. Change management and update approval processes
  12. Template: AI vendor contract addendum
Module 5. Operational Resilience and Incident Response
Ensure AI vendors can maintain service and respond to incidents effectively.
12 chapters in this module
  1. Disaster recovery and business continuity planning
  2. Incident response timelines and notification duties
  3. Failover mechanisms in AI-driven workflows
  4. Monitoring for anomalous model behavior
  5. Vendor communication protocols during outages
  6. Human-in-the-loop requirements for critical decisions
  7. Fallback processes when AI systems degrade
  8. Testing resilience through tabletop exercises
  9. Vendor transparency during incident investigations
  10. Post-incident review and improvement mandates
  11. Reporting obligations to regulators and clients
  12. Checklist: Operational red flags in vendor operations
Module 6. Bias, Fairness, and Ethical Risk Assessment
Evaluate fairness, equity, and ethical implications in AI vendor systems.
12 chapters in this module
  1. Defining fairness in regulated decision-making contexts
  2. Identifying proxy variables and unintended bias
  3. Bias testing methodologies for AI models
  4. Fair lending, employment, and access implications
  5. Vendor accountability for discriminatory outcomes
  6. Transparency in fairness mitigation efforts
  7. Stakeholder review of ethical AI practices
  8. Documenting bias assessment for audit purposes
  9. Third-party fairness certification programs
  10. Handling complaints about AI-driven decisions
  11. Ethical review board considerations
  12. Case study: Bias audit of a legal tech vendor
Module 7. Data Governance and Privacy by Design
Assess how vendors handle sensitive data across the AI lifecycle.
12 chapters in this module
  1. Data minimization in AI training and inference
  2. Anonymization and de-identification effectiveness
  3. Consent management in AI-driven processing
  4. Cross-border data transfer mechanisms
  5. Purpose limitation and secondary use risks
  6. Data retention and deletion policies
  7. Vendor access controls and insider threat prevention
  8. Logging and monitoring data access
  9. Privacy impact assessments for AI systems
  10. Vendor accountability under joint controller models
  11. Data subject rights fulfillment support
  12. Template: Data governance questionnaire
Module 8. Model Validation and Ongoing Monitoring
Implement continuous validation and performance tracking.
12 chapters in this module
  1. Pre-deployment validation protocols
  2. Ongoing performance benchmarking
  3. Model accuracy drift detection
  4. Calibration and confidence interval analysis
  5. Statistical process control for AI outputs
  6. Automated monitoring dashboards
  7. Thresholds for model retraining or replacement
  8. Independent validation requirements
  9. Third-party model auditing options
  10. Documentation for model lifecycle management
  11. Handling vendor model updates and version changes
  12. Checklist: Model monitoring maturity assessment
Module 9. Vendor Ecosystem and Supply Chain Risk
Map and assess risks across the full AI vendor stack.
12 chapters in this module
  1. Identifying sub-vendors and dependencies
  2. Open-source component risks in AI systems
  3. Software bill of materials (SBOM) requirements
  4. Security of pre-trained models and APIs
  5. Vendor financial stability and continuity risk
  6. Geopolitical risks in AI supply chains
  7. Concentration risk in dominant AI platforms
  8. Due diligence on cloud infrastructure providers
  9. Resilience of AI model hosting environments
  10. Vendor lock-in and exit barriers
  11. Transparency in supply chain disclosures
  12. Case study: Uncovering hidden risks in a legal AI stack
Module 10. Stakeholder Communication and Reporting
Develop clear, defensible reporting for boards and regulators.
12 chapters in this module
  1. Tailoring AI risk reports for executive audiences
  2. Board-level risk dashboards
  3. Regulatory filing requirements
  4. Internal audit coordination
  5. Legal disclosure obligations
  6. Client communication about AI use
  7. Managing reputational risk from AI failures
  8. Documenting decision rationale for reviewers
  9. Building trust through transparency
  10. Responding to media inquiries on AI vendors
  11. Escalation protocols for high-risk findings
  12. Template: AI vendor risk summary report
Module 11. Implementation Roadmap and Change Management
Deploy the framework across teams and systems.
12 chapters in this module
  1. Phased rollout planning
  2. Change management for risk teams
  3. Training procurement and legal partners
  4. Integrating with vendor management systems
  5. Pilot program design and evaluation
  6. Feedback loops for continuous improvement
  7. Scaling across business units
  8. Governance committee setup
  9. KPIs for program success
  10. Overcoming resistance to new processes
  11. Budgeting for ongoing AI risk oversight
  12. Case study: Implementing AI risk assessment in a law firm
Module 12. Future-Proofing and Adaptive Governance
Anticipate evolving threats and regulatory shifts.
12 chapters in this module
  1. Monitoring emerging AI regulations
  2. Adapting frameworks to new model types
  3. Generative AI and hallucination risk management
  4. Zero-day vulnerability response in AI systems
  5. Preparing for AI-specific breach scenarios
  6. Scenario planning for regulatory changes
  7. Building internal AI expertise
  8. Engaging with standards development
  9. Vendor innovation vs. risk tolerance balance
  10. Long-term AI governance strategy
  11. Succession planning for AI risk leadership
  12. Final checklist: Maturity assessment and next steps

How this maps to your situation

  • You’re leading AI vendor due diligence in a regulated environment
  • You’re advising clients or internal teams on AI risk and compliance
  • You’re building or enhancing an AI governance framework
  • You’re responding to increased board or regulatory scrutiny on third-party AI

Before vs. after

Before
Uncertain, inconsistent, or reactive AI vendor assessments that lack defensibility and scalability.
After
A structured, repeatable, and auditable process for evaluating and managing AI vendor risk in regulated environments.

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 36, 48 hours of self-paced learning, designed for busy professionals. Modules are structured to support incremental progress with immediate applicability.

If nothing changes
Organizations that delay implementing rigorous AI vendor risk practices may face increased exposure to compliance failures, regulatory penalties, reputational damage, and operational disruptions, especially as oversight intensifies and AI adoption grows.

How this compares to the alternatives

Unlike generic AI ethics courses or high-level compliance overviews, this program delivers a production-grade, implementation-focused framework tailored to the specific demands of regulated industries. It goes beyond theory to provide actionable tools, templates, and real-world application scenarios.

Frequently asked

Who is this course designed for?
Compliance officers, risk managers, legal advisors, and technology leaders in regulated industries who are responsible for evaluating or overseeing AI vendors.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a money-back guarantee?
Yes, a 30-day money-back guarantee is included.
$199 one-time. Approximately 36, 48 hours of self-paced learning, designed for busy professionals. Modules are structured to support incremental progress 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
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