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Pragmatic AI Vendor Risk Assessment for Regulated Industries

$199.00
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A tailored course, built for your situation

Pragmatic AI Vendor Risk Assessment for Regulated Industries

A structured, implementation-grade framework for managing AI vendor risk 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.
AI adoption in regulated industries is accelerating, but vendor risk practices haven't kept pace.

The situation this course is for

Teams are pressured to move fast on AI initiatives, yet lack practical frameworks to evaluate third-party risk with precision. Generic assessments miss regulatory nuances, technical blind spots, and operational dependencies, leading to rework, compliance delays, or unplanned exposure.

Who this is for

Business and technology professionals in regulated industries (finance, healthcare, insurance, energy, government) responsible for AI procurement, risk governance, compliance, or technology oversight.

Who this is not for

This course is not for executives seeking high-level AI strategy overviews, vendors marketing AI tools, or teams operating in unregulated consumer tech spaces without compliance mandates.

What you walk away with

  • Apply a repeatable 12-step methodology to assess AI vendors against regulatory and operational standards
  • Map AI vendor capabilities to core compliance frameworks (e.g., GDPR, HIPAA, SOC 2, NIST AI RMF)
  • Structure technical due diligence that uncovers model provenance, data handling, and monitoring gaps
  • Negotiate contracts with precise language for audit rights, incident response, and model updates
  • Deploy an ongoing vendor monitoring program with clear escalation triggers and review cycles

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk in Regulated Contexts
Establish the core principles of AI risk, regulatory expectations, and stakeholder alignment.
12 chapters in this module
  1. Defining AI vendor risk in regulated environments
  2. Key regulatory drivers shaping vendor oversight
  3. Roles and responsibilities across legal, compliance, and tech teams
  4. Aligning risk tolerance with business objectives
  5. Stakeholder communication frameworks
  6. Common misconceptions about AI due diligence
  7. The evolution of third-party risk to include AI-specific concerns
  8. Benchmarking current practices against emerging standards
  9. Establishing governance boundaries for AI procurement
  10. Documenting risk appetite for AI use cases
  11. Integrating AI risk into enterprise risk management
  12. Setting success metrics for vendor risk programs
Module 2. Regulatory Mapping and Compliance Alignment
Translate regulations into actionable assessment criteria for AI vendors.
12 chapters in this module
  1. Overview of GDPR, HIPAA, and sector-specific rules
  2. Mapping AI capabilities to data protection obligations
  3. Understanding NIST AI RMF and ISO/IEC standards
  4. SOC 2 and attestation requirements for AI vendors
  5. Jurisdictional considerations for global deployments
  6. Handling cross-border data flows in AI systems
  7. Audit readiness and documentation expectations
  8. Aligning model behavior with fairness and non-discrimination rules
  9. Transparency obligations in automated decision-making
  10. Incident reporting timelines and vendor responsibilities
  11. Regulatory sandboxes and pre-approval pathways
  12. Future-proofing against upcoming AI legislation
Module 3. Vendor Pre-Screening and Scope Definition
Define assessment scope and filter vendors efficiently before deep due diligence.
12 chapters in this module
  1. Classifying AI vendors by risk tier and use case
  2. Building a pre-screening questionnaire
  3. Identifying red flags in vendor marketing claims
  4. Assessing vendor maturity through public signals
  5. Evaluating company stability and funding health
  6. Reviewing public incident history and disclosures
  7. Determining in-scope systems and integrations
  8. Defining boundaries between vendor and client responsibilities
  9. Scoping data access, storage, and processing limits
  10. Establishing integration and interoperability requirements
  11. Setting performance and uptime expectations
  12. Documenting fallback and exit strategies
Module 4. Technical Due Diligence Framework
Conduct in-depth technical assessments of AI models, data practices, and infrastructure.
12 chapters in this module
  1. Requesting and interpreting model cards and datasheets
  2. Assessing training data provenance and bias mitigation
  3. Evaluating model explainability and interpretability features
  4. Reviewing validation and testing methodologies
  5. Assessing adversarial robustness and model security
  6. Understanding inference latency and scaling behavior
  7. Auditing API security and authentication protocols
  8. Verifying encryption standards in transit and at rest
  9. Reviewing infrastructure resilience and disaster recovery
  10. Assessing monitoring and logging capabilities
  11. Validating model drift detection and retraining processes
  12. Confirming access controls and role-based permissions
Module 5. Data Governance and Privacy Safeguards
Ensure vendor data handling aligns with organizational privacy and governance policies.
12 chapters in this module
  1. Mapping data flows in AI-powered systems
  2. Assessing data minimization and retention practices
  3. Validating anonymization and pseudonymization techniques
  4. Reviewing consent management and lawful basis alignment
  5. Evaluating data subject rights fulfillment mechanisms
  6. Auditing data access logs and monitoring
  7. Assessing subprocessor transparency and control
  8. Confirming data portability and deletion capabilities
  9. Handling sensitive attributes in training data
  10. Ensuring data lineage and audit trail completeness
  11. Reviewing data ownership clauses in contracts
  12. Establishing breach notification protocols
Module 6. Contractual Risk Mitigation Strategies
Draft and negotiate contracts that enforce accountability and operational clarity.
12 chapters in this module
  1. Key clauses for AI-specific vendor agreements
  2. Defining model performance benchmarks and SLAs
  3. Incorporating audit rights and access to logs
  4. Setting incident response and breach notification terms
  5. Addressing model updates, versioning, and change control
  6. Ensuring continuity of service and disaster recovery
  7. Defining intellectual property ownership
  8. Limiting liability for automated decision outcomes
  9. Establishing termination and exit rights
  10. Requiring transparency in third-party dependencies
  11. Including right-to-explain provisions
  12. Negotiating indemnification for regulatory penalties
Module 7. Model Performance and Operational Monitoring
Implement ongoing monitoring to detect degradation, drift, and operational risks.
12 chapters in this module
  1. Setting up baseline performance metrics
  2. Monitoring for statistical drift and concept shift
  3. Tracking model accuracy and fairness over time
  4. Logging inputs, outputs, and decision rationales
  5. Implementing real-time anomaly detection
  6. Creating dashboards for risk and performance visibility
  7. Scheduling regular model validation cycles
  8. Integrating with existing observability tools
  9. Establishing thresholds for human review
  10. Documenting model behavior changes
  11. Managing version rollouts and rollback plans
  12. Reporting model performance to compliance teams
Module 8. Incident Response and Escalation Protocols
Prepare for and respond to AI-related incidents with defined procedures.
12 chapters in this module
  1. Defining what constitutes an AI incident
  2. Establishing incident classification levels
  3. Creating vendor communication playbooks
  4. Setting internal escalation paths
  5. Documenting regulatory reporting obligations
  6. Conducting root cause analysis for AI failures
  7. Managing reputational risk from AI errors
  8. Implementing temporary mitigation measures
  9. Coordinating with legal and PR teams
  10. Reviewing vendor post-incident reports
  11. Updating controls to prevent recurrence
  12. Reporting outcomes to governance committees
Module 9. Stakeholder Alignment and Cross-Functional Coordination
Align legal, compliance, IT, and business teams around a unified risk approach.
12 chapters in this module
  1. Identifying key stakeholders in AI vendor risk
  2. Creating cross-functional review workflows
  3. Facilitating risk assessment meetings
  4. Documenting decisions and rationale
  5. Managing conflicting priorities across departments
  6. Building shared risk lexicons and definitions
  7. Integrating vendor risk into procurement workflows
  8. Training teams on AI-specific risk factors
  9. Establishing escalation paths for unresolved issues
  10. Reporting risk posture to executive leadership
  11. Aligning with board-level risk expectations
  12. Fostering a culture of responsible innovation
Module 10. Third-Party Audit and Attestation Review
Evaluate vendor-provided audits, certifications, and attestations critically.
12 chapters in this module
  1. Understanding SOC 1, SOC 2, and ISO 27001 reports
  2. Assessing the scope and limitations of third-party audits
  3. Reviewing penetration test results and remediation plans
  4. Validating AI-specific audit claims
  5. Requesting additional evidence beyond standard reports
  6. Engaging independent assessors when needed
  7. Benchmarking vendor maturity against industry peers
  8. Assessing transparency in audit findings
  9. Evaluating frequency and timeliness of assessments
  10. Understanding gaps in attestation coverage
  11. Using audit results in vendor scoring models
  12. Incorporating audit findings into contract renewals
Module 11. Continuous Improvement and Program Scaling
Evolve the vendor risk program to handle increasing AI adoption.
12 chapters in this module
  1. Tracking key risk indicators over time
  2. Benchmarking performance across vendors
  3. Updating assessment templates based on lessons learned
  4. Automating data collection and scoring
  5. Integrating with GRC platforms
  6. Scaling review processes for high-volume procurement
  7. Developing vendor risk training programs
  8. Creating playbooks for new AI use cases
  9. Establishing feedback loops with vendors
  10. Measuring program efficiency and effectiveness
  11. Aligning with enterprise AI governance frameworks
  12. Planning for future regulatory changes
Module 12. Implementation Playbook and Real-World Application
Apply the full framework to real-world scenarios with guided templates.
12 chapters in this module
  1. Using the implementation playbook: overview
  2. Customizing templates for your organization
  3. Running a pilot assessment with a real vendor
  4. Documenting findings and recommendations
  5. Presenting results to stakeholders
  6. Negotiating improvements with the vendor
  7. Finalizing contracts with risk-based terms
  8. Onboarding the vendor with monitoring enabled
  9. Conducting the first operational review
  10. Iterating on the assessment process
  11. Scaling to multiple vendors and use cases
  12. Maintaining and updating your risk program

How this maps to your situation

  • You're evaluating your first AI vendor and need a structured assessment method
  • You're scaling AI adoption and need consistent vendor evaluation across teams
  • You're responding to internal audit or regulatory feedback on AI risk practices
  • You're building an AI governance program from the ground up

Before vs. after

Before
Assessments are inconsistent, reactive, and lack alignment across teams, leading to delays, oversights, and compliance uncertainty.
After
You lead confident, structured evaluations with clear documentation, stakeholder alignment, and operational safeguards, accelerating trusted AI adoption.

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 20, 25 hours to complete all modules, with flexible pacing and self-directed study.

If nothing changes
Without a structured approach, organizations risk adopting AI systems with hidden compliance gaps, technical weaknesses, or operational fragility, potentially resulting in regulatory scrutiny, reputational harm, or costly remediation later.

How this compares to the alternatives

Unlike generic cybersecurity or third-party risk courses, this program is specifically tailored to AI vendors in regulated environments, covering technical model validation, regulatory mapping, and operational monitoring in a single, integrated framework.

Frequently asked

Who is this course designed for?
Business and technology professionals in regulated industries who are responsible for AI procurement, risk governance, compliance, or technology oversight.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate of completion?
Yes, a certificate is issued upon completing all modules and passing the final assessment.
$199 one-time. Approximately 20, 25 hours to complete all modules, with flexible pacing and self-directed study..

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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