A tailored course, built for your situation
Practical AI Audit Readiness for Public-Sector Programs
Master compliance, governance, and implementation for AI in public-sector technology programs
$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.
Unclear audit paths and shifting expectations slow AI adoption in public-sector programs.
The situation this course is for
Teams face pressure to deploy AI responsibly, but without clear frameworks for audit readiness, initiatives stall or face rework. Documentation is inconsistent, controls are retrofitted, and stakeholders lack confidence in compliance posture.
Who this is for
Business and technology professionals in public-sector programs responsible for AI governance, compliance, risk, or delivery who need to implement with precision and accountability.
Who this is not for
This is not for vendors selling AI tools, academic researchers, or individuals seeking certification prep without implementation goals.
What you walk away with
- Map AI systems to current public-sector audit and compliance requirements
- Build repeatable documentation and control frameworks for AI audits
- Align technical delivery with governance and oversight expectations
- Simulate audit scenarios to proactively address gaps
- Lead confident, compliant AI program rollouts in regulated environments
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Audit in the Public Sector
Establish core principles of AI accountability, transparency, and regulatory alignment specific to public programs.
12 chapters in this module
- Defining AI audit readiness
- Public-sector vs private-sector expectations
- Regulatory landscape overview
- Key principles of algorithmic accountability
- Transparency requirements
- Documentation standards
- Stakeholder mapping
- Risk classification frameworks
- Ethical design alignment
- Compliance maturity models
- Audit lifecycle phases
- Governance body coordination
Module 2. Regulatory Mapping and Compliance Alignment
Translate evolving regulations into actionable compliance requirements for AI systems.
12 chapters in this module
- Global AI policy trends
- EU AI Act implications
- US federal AI guidance
- National data protection laws
- Sector-specific mandates
- Compliance gap analysis
- Mapping controls to requirements
- Jurisdictional overlaps
- Explainability mandates
- Data provenance rules
- Third-party audit expectations
- Compliance tracking systems
Module 3. AI Risk Classification and Tiering
Classify AI applications by risk level to determine audit intensity and documentation depth.
12 chapters in this module
- Risk-based audit frameworks
- High-risk AI definitions
- Medium and low-risk categories
- Use case classification
- Impact assessment design
- Bias and fairness thresholds
- Safety-critical systems
- Public safety implications
- Automated decision-making rules
- Human-in-the-loop requirements
- Scoring risk tiers
- Documentation scaling by tier
Module 4. Documentation Frameworks for Audit Readiness
Build standardized, reusable documentation for AI system audits.
12 chapters in this module
- AI system register design
- Model card creation
- Data card specifications
- Technical documentation standards
- Version control for AI systems
- Change logging
- Stakeholder communication logs
- Training data provenance
- Model performance tracking
- Bias detection reports
- Incident response documentation
- Audit trail integration
Module 5. Internal Control Design for AI Systems
Design and implement internal controls that support audit compliance.
12 chapters in this module
- Control objectives for AI
- Preventive vs detective controls
- Automated control logic
- Access governance
- Model validation controls
- Monitoring thresholds
- Alerting mechanisms
- Control testing procedures
- Segregation of duties
- Change approval workflows
- Model retraining controls
- Decommissioning controls
Module 6. Stakeholder Alignment and Governance
Align technical teams, legal, compliance, and oversight bodies around audit goals.
12 chapters in this module
- Cross-functional team roles
- Governance committee structure
- Audit readiness reporting
- Legal and compliance coordination
- Oversight body engagement
- Public communication strategy
- Ethics review integration
- Whistleblower policy alignment
- Vendor management
- Third-party audit coordination
- Public consultation processes
- Stakeholder feedback loops
Module 7. Model Lifecycle Audit Tracing
Trace AI models from design to deployment and decommissioning for audit transparency.
12 chapters in this module
- Lifecycle phase definitions
- Design documentation standards
- Development audit trails
- Testing protocols
- Validation evidence
- Deployment checklists
- Monitoring integration
- Performance drift detection
- Retraining workflows
- Version rollback procedures
- Decommissioning documentation
- Archival requirements
Module 8. Bias Detection and Fairness Audits
Implement methods to detect, document, and mitigate bias in AI systems.
12 chapters in this module
- Bias definition and types
- Statistical fairness metrics
- Disparate impact analysis
- Bias testing frameworks
- Data sampling audits
- Model performance by subgroup
- Human review protocols
- Bias mitigation strategies
- Fairness reporting
- Third-party fairness review
- Remediation workflows
- Public accountability
Module 9. Data Governance for Audit Compliance
Ensure data practices meet audit expectations for provenance, quality, and protection.
12 chapters in this module
- Data lineage tracking
- Data quality standards
- Consent management
- Data retention policies
- Anonymization techniques
- Data sharing agreements
- Data subject rights
- Data ownership models
- Data inventory creation
- Data access logging
- Data breach response
- Vendor data handling
Module 10. Incident Response and Audit Triggers
Prepare for audit triggers and system incidents with structured response protocols.
12 chapters in this module
- Incident classification
- Audit trigger identification
- Response team activation
- Root cause analysis
- Regulatory reporting
- Public disclosure protocols
- System rollback procedures
- Corrective action plans
- Lessons learned documentation
- Audit follow-up requirements
- Legal hold procedures
- Reputation management
Module 11. Third-Party and Vendor Audits
Manage compliance when using external AI systems or vendors.
12 chapters in this module
- Vendor due diligence
- Contractual compliance clauses
- Third-party audit rights
- Subprocessor oversight
- API security audits
- Model transparency requirements
- Performance SLAs
- Data handling audits
- Exit strategy planning
- Vendor lock-in risks
- Audit report validation
- Ongoing monitoring
Module 12. Audit Simulation and Readiness Testing
Run internal simulations to test audit readiness and improve compliance posture.
12 chapters in this module
- Simulation planning
- Scenario design
- Mock audit execution
- Documentation review
- Stakeholder interviews
- Control testing
- Gap identification
- Remediation tracking
- Readiness scoring
- Executive briefing
- Continuous improvement
- Audit readiness certification
How this maps to your situation
- Public-sector AI program launch
- Mid-cycle audit preparation
- Post-incident compliance review
- Vendor integration with AI components
Before vs. after
Before
Uncertain about audit requirements, inconsistent documentation, reactive compliance, stakeholder misalignment
After
Confident audit readiness, standardized documentation, proactive compliance, aligned stakeholders, successful audit outcomes
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 flexible, self-paced learning across 12 weeks or faster.
If nothing changes
Without structured audit readiness, AI programs risk delays, rework, non-compliance findings, and reputational exposure, especially as oversight increases.
How this compares to the alternatives
Unlike generic AI ethics courses or certification prep, this program delivers implementation-grade frameworks specifically for public-sector audit success, with templates, playbooks, and real-world scenarios.
Frequently asked
Who is this course designed for?
Business and technology professionals in public-sector programs who need to implement AI with compliance, governance, and audit readiness.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical or policy-focused?
It balances both, designed for practitioners who need to implement systems that meet policy and audit requirements.
$199 one-time. Approximately 4, 6 hours per module, designed for flexible, self-paced learning across 12 weeks or faster..
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