AI-Enabled Coordinated Assurance™ Certificate Program

Course structure

Module 1: Introduction to Coordinated Assurance

• 1.1 Foundations and Importance of Coordinated Assurance
• 1.2 Roles of Assurance Providers and Stakeholders in Coordinated Assurance
• 1.3 Standard 9.5 and Governance Expectations

Module 2: The Role of AI in Enhancing Collaboration

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

Module 3: Using AI for Assurance Mapping and Reliance

• 3.1 Identifying Overlaps and Gaps with AI
• 3.2 Introduction to Integrated Assurance Mapping
• 3.3 Reliance Strategies and AI

Module 4: Enforcement & Model Integrity

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

Module 5: Case Study – Implementing AI in Coordinated Assurance

• 5.1 Case Study – Implementing AI in Coordinated Assurance
• 5.2 Introduction to Ethical Considerations in the Case Study
• 5.3 Overview of Case Outcomes

Module 6: Toolkits & Automation

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

Module 7: Conclusion – Building Trust and Governance Across Functions

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