A tailored course, built for your situation
Audit-Tested AI Vendor Risk Assessment for Public-Sector Programs
Implementation-grade risk assessment framework for AI procurement 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 11 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Lack of standardized, auditable processes for evaluating AI vendors in high-compliance environments
The situation this course is for
Teams responsible for AI procurement in public-sector programs often face misalignment between technical capabilities, regulatory requirements, and audit expectations. Without a consistent, documented methodology, risk assessments become reactive, inconsistent, and difficult to defend during review cycles.
Who this is for
Business and technology professionals involved in AI procurement, compliance, risk governance, or technology oversight within public-sector or highly regulated environments
Who this is not for
Individuals seeking introductory AI awareness content or general cybersecurity training
What you walk away with
- Apply a standardized framework to assess AI vendor risk across technical, legal, and operational domains
- Produce audit-ready documentation for AI procurement decisions
- Align cross-functional stakeholders using structured evaluation criteria
- Anticipate and address common gaps in vendor due diligence processes
- Implement repeatable workflows that scale across programs and fiscal cycles
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk in Public Programs
Introduces core concepts, regulatory context, and the role of auditability in AI procurement.
12 chapters in this module
- Defining AI vendor risk in public-sector contexts
- Key differences from traditional software procurement
- Regulatory drivers shaping current expectations
- The evolution of compliance frameworks
- Why audit-readiness matters now
- Stakeholder landscape in AI procurement
- Balancing innovation with accountability
- Common misconceptions about AI risk
- Case example: Early adoption challenges
- The shift from pilot to scale
- Defining success in risk assessment
- Course roadmap and implementation goals
Module 2. Audit Principles for AI Systems
Covers audit fundamentals tailored to AI systems and third-party vendor environments.
12 chapters in this module
- What auditors look for in AI deployments
- Traceability of decision logic
- Documentation standards for model governance
- Version control and change tracking
- Independence and objectivity requirements
- Evidence collection best practices
- Preparing for internal vs external audits
- Common audit findings in AI projects
- Mapping controls to audit criteria
- Building audit trails into procurement workflows
- Third-party access and data handling
- Reporting structures for compliance teams
Module 3. Vendor Evaluation Framework Design
Guides the creation of a structured, repeatable evaluation process for AI vendors.
12 chapters in this module
- Core components of an evaluation framework
- Weighting criteria by risk impact
- Technical due diligence checklist design
- Legal and contractual red flags
- Operational integration risks
- Scalability and support model assessment
- Defining minimum viable documentation
- Creating standardized scoring rubrics
- Incorporating ethical AI principles
- Handling proprietary black-box models
- Evaluating explainability commitments
- Establishing vendor accountability mechanisms
Module 4. Due Diligence Workflow Implementation
Details step-by-step workflows for conducting AI vendor risk assessments.
12 chapters in this module
- Initiating the due diligence process
- Requesting information from vendors
- Validating claims with evidence
- Conducting technical interviews
- Reviewing architecture diagrams
- Assessing training data provenance
- Model performance validation
- Security and access control review
- Incident response readiness
- Business continuity planning
- Reference checks and case studies
- Final risk rating assignment
Module 5. Documentation Standards for Compliance
Establishes templates and formats for producing audit-ready records.
12 chapters in this module
- Required elements of a risk assessment report
- Standardizing vendor response formats
- Creating evidence logs
- Versioning assessment documents
- Secure storage and access controls
- Redaction and confidentiality handling
- Cross-referencing with procurement records
- Integrating with existing GRC platforms
- Maintaining assessment currency
- Updating documentation for new deployments
- Preparing for audit sampling
- Document retention policies
Module 6. Cross-Functional Alignment Strategies
Enables coordination between legal, procurement, IT, and technical teams.
12 chapters in this module
- Identifying key stakeholders
- Aligning language across departments
- Facilitating joint review sessions
- Resolving conflicting priorities
- Escalation paths for high-risk findings
- Building consensus on go/no-go decisions
- Training non-technical reviewers
- Communicating risk to leadership
- Integrating with enterprise risk management
- Managing procurement timelines
- Balancing speed and rigor
- Post-decision feedback loops
Module 7. Technical Risk Assessment Methods
Provides tools to evaluate AI model behavior, data quality, and system reliability.
12 chapters in this module
- Model transparency evaluation
- Assessing bias and fairness claims
- Data lineage and provenance verification
- Testing for drift and degradation
- Evaluating explainability tools
- Reviewing model validation reports
- Understanding uncertainty quantification
- Assessing adversarial robustness
- Monitoring for unintended behavior
- Reviewing retraining processes
- Evaluating API reliability
- System integration testing
Module 8. Legal and Contractual Risk Mitigation
Covers key contractual clauses and legal safeguards for AI vendor agreements.
12 chapters in this module
- Intellectual property ownership
- Liability for incorrect outputs
- Indemnification clauses
- Warranties and service level agreements
- Data ownership and usage rights
- Right to audit provisions
- Termination and exit strategies
- Subcontractor oversight requirements
- Compliance with export controls
- Jurisdiction and dispute resolution
- Insurance requirements
- Obligations for regulatory changes
Module 9. Ethical and Societal Impact Considerations
Integrates ethical AI principles into vendor evaluation.
12 chapters in this module
- Defining ethical AI in public programs
- Assessing societal impact claims
- Evaluating fairness across demographics
- Handling sensitive use cases
- Transparency commitments
- Human oversight requirements
- Redress mechanisms
- Community engagement expectations
- Monitoring for downstream effects
- Public trust considerations
- Reputation risk assessment
- Ethics review board integration
Module 10. Scaling Assessments Across Programs
Enables consistent application of risk assessment at enterprise scale.
12 chapters in this module
- Creating centralized assessment units
- Standardizing templates across departments
- Training assessors for consistency
- Automating evidence collection
- Integrating with procurement systems
- Managing workload distribution
- Quality assurance for assessments
- Benchmarking performance over time
- Sharing lessons learned
- Updating frameworks based on feedback
- Managing vendor onboarding at scale
- Continuous improvement cycles
Module 11. Emerging Threats and Adaptation
Prepares teams for evolving AI risks and regulatory shifts.
12 chapters in this module
- Tracking regulatory developments
- Anticipating new compliance requirements
- Monitoring for emerging attack vectors
- Evaluating zero-day response capabilities
- Assessing supply chain risks
- Vendor financial stability checks
- Geopolitical risk factors
- Climate resilience of AI infrastructure
- Workforce continuity planning
- Adapting to new AI capabilities
- Reassessing legacy vendors
- Future-proofing procurement strategies
Module 12. Implementation and Continuous Improvement
Guides rollout, monitoring, and refinement of the risk assessment program.
12 chapters in this module
- Pilot program design
- Stakeholder onboarding plan
- Change management strategies
- Success metrics definition
- Feedback collection mechanisms
- Reporting to leadership
- Audit preparation checklist
- Lessons learned documentation
- Updating frameworks based on audits
- Training new team members
- Maintaining executive sponsorship
- Long-term sustainability planning
How this maps to your situation
- Initial AI procurement decision
- Mid-cycle vendor reassessment
- Post-deployment audit preparation
- Enterprise-wide scaling initiative
Before vs. after
Before
Unstructured evaluations, inconsistent documentation, and reactive responses to audit requests
After
Standardized, repeatable, and defensible AI vendor risk assessment processes aligned with public-sector requirements
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 12, 15 hours of self-paced learning, with implementation activities designed to integrate directly into current workflows.
If nothing changes
Organizations that delay implementing structured AI vendor risk assessments may face increased audit findings, procurement delays, and reputational exposure due to inconsistent evaluation practices.
How this compares to the alternatives
Unlike generic AI ethics guides or high-level compliance overviews, this course provides implementation-grade workflows, audit-tested documentation standards, and public-sector-specific evaluation criteria used in actual procurement decisions.
Frequently asked
Who is this course designed for?
Business and technology professionals involved in AI procurement, compliance, risk governance, or technology oversight within public-sector or highly regulated environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate of completion?
Yes, a digital certificate is issued upon finishing all modules and assessments.
$199 one-time. Approximately 12, 15 hours of self-paced learning, with implementation activities designed to integrate directly into current workflows..
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