• Chief Audit Executives & Internal Audit Leaders: For CAEs and audit heads driving integrated assurance strategies and aligning audit functions with AI-enabled coordination and IIA Standard 9.5.
• Risk & Governance Professionals: Ideal for risk managers and GRC leaders seeking to unify risk insights, eliminate assurance overlaps, and strengthen enterprise-wide governance using AI tools.
• Compliance & Regulatory Officers: Designed for professionals responsible for meeting ISO, NIST, EU AI Act, and other regulatory requirements through automated monitoring and coordinated reporting.
• Assurance & Control Owners: Perfect for teams across internal audit, compliance, cybersecurity, and vendor risk who collaborate to map coverage, reduce duplication, and improve reliance decisions.
• AI & Security Architects: Targeted at technology and AI security leaders implementing secure, trustworthy AI systems with strong model integrity, continuous monitoring, and enforcement controls.

No mandatory prerequisites. However, a foundational understanding of AI concepts, risk management, audit or compliance processes, and familiarity with assurance or governance frameworks is recommended.

AI-Driven Assurance Coordination

Learn how AI integrates audit, risk, and compliance functions to eliminate silos, reduce duplication, and enable seamless collaboration across assurance teams.

Intelligent Risk & Coverage Mapping

Understand how AI-powered assurance mapping detects overlaps, identifies gaps, and provides a unified view of organizational risks for stronger, data-backed decisions.

Automated Monitoring & Reporting

Discover how AI enables continuous monitoring, real-time dashboards, and automated reporting to accelerate insights, improve transparency, and enhance governance outcomes.

Secure & Compliant AI Operations

Build expertise in applying AI governance frameworks, policy enforcement, and model integrity controls to ensure systems remain secure, auditable, and regulation ready.

Responsible & Collaborative Assurance Leadership

Develop the leadership skills to drive cross-functional alignment, foster trust, and implement ethical, AI-enabled assurance practices that strengthen accountability and organizational resilience.

1. 1.1 Foundations and Importance of Coordinated Assurance
2. 1.2 Roles of Assurance Providers and Stakeholders in Coordinated Assurance
3. 1.3 Standard 9.5 and Governance Expectations

1. 2.1 AI’s Impact on Data Integration and Communication
2. 2.2 Overview of Key AI Technologies for Assurance
3. 2.3 Collaboration Use Cases Enabled by AI
4. 2.4 Risk in AI Utilization

1. 3.1 Identifying Overlaps and Gaps with AI
2. 3.2 Introduction to Integrated Assurance Mapping
3. 3.3 Reliance Strategies and AI

1. 4.1 Securing AI Systems Post-Deployment
2. 4.2 Model Integrity and Auditing
3. 4.3 Cryptographic Integrity Protections (Hash Validation & Signature Rotation)
4. 4.4 Side-Channel Attack Scenarios on Model Checkpoints, Quantized Models, and GPU Memory
5. 4.5 Guardrail Testing Patterns for Automated Prompt Sanitization
6. 4.6 Separation of Duties & Dual Control for High-Risk AI Models
7. 4.7 Evaluation Guidance for Model Behavior Consistency
8. 4.8 RSAIF Mapping, GRC Interpretation, and Evidence Requirements
9. 4.9 Introducing Dual Lab Paths and a Tools Capability Matrix
10. 4.10 Hands-On: Implementing RBAC for Secure AI APIs (Dual Lab Path)
11. 4.11 Knowledge Check

1. 5.1 Case Study – Implementing AI in Coordinated Assurance
2. 5.2 Introduction to Ethical Considerations in the Case Study
3. 5.3 Overview of Case Outcomes

1. 6.1 Introduction to AI Security Tools
2. 6.2 Automating AI Security and Compliance
3. 6.3 Hallucination Monitoring and Scoring Mechanisms
4. 6.4 Architecture of Automated Compliance Pipeline
5. 6.5 Automated Rollback Workflows, Drift Alerts, and Scheduled Red Teaming
6. 6.6 Cross-Model Validation for Multi-Model AI Systems
7. 6.7 GPU Runtime Observability and Isolation Requirements
8. 6.8 Introduction: AI Security Automation Stack
9. 6.9 Expanding AI Security Tool Categories
10. 6.10 Tool Selection Criteria and Capability Matrix
11. 6.11 Real Automation Workflow & Evidence Generation
12. 6.12 Hands-On Lab

1. 7.1 AI Governance Frameworks and Controls (in Coordinated Assurance)
2. 7.2 Trust, Transparency, and Ethics in AI-Enabled Assurance
3. 7.3 Measuring Assurance Effectiveness (Metrics, KRIs, KPIs, and Continuous Improvement)
4. 7.4 Conclusion and Next Steps