AI-Enabled Coordinated AssuranceTM
Covers how AI can coordinate audit, risk, compliance, and governance work across functions, aligned with ISO/IEC 42001, NIST AI RMF, EU AI Act, and IIA standards.
Coordinate audit, risk and compliance with AI
- Duration:
8 hours - Languages:
English, Spanish - Level:
Executive-level understanding of AI-enabled coordinated assurance - Format:
Case studies, simulations, and labs
Prerequisites
- No mandatory prerequisites; foundational knowledge of AI, risk management, audit or compliance, and assurance or governance frameworks is recommended
The AI-Enabled Coordinated Assurance™ self-paced course includes:
1. 8 hours of on-demand videos, an e-book, and podcasts.
2. Modular quizzes to help learners monitor their progress.
The course allows learners to study at their own pace. After completing the course, learners can take the certification exam online through a secure remote-proctored system.
Why This Certification Matters?
- Integrated assurance across functions Lets audit, risk, compliance, and governance teams work in sync with AI, reducing silos, duplicated effort, and gaps in risk coverage.
- Stronger AI-driven risk visibility Continuous monitoring, real-time dashboards, and AI insights surface overlaps, gaps, and emerging risks faster.
- Better reliance decisions AI-assisted mapping and analytics help assess testing quality, rely on other providers’ work, and optimize resources.
- Governance, compliance, and control Builds understanding of ISO/IEC 42001, NIST AI RMF, EU AI Act, and IIA standards.
- Trust through responsible AI Emphasizes transparency, human oversight, and ethical AI deployment.
What You'll Learn?
Foundations and Importance of Coordinated Assurance
Roles of Assurance Providers and Stakeholders
Standard 9.5 and Governance Expectations
AI’s Impact on Data Integration and Communication
Overview of Key AI Technologies for Assurance
Collaboration Use Cases Enabled by AI
Risk in AI Utilization
Identifying Overlaps and Gaps with AI
Introduction to Integrated Assurance Mapping
Reliance Strategies and AI
Securing AI Systems Post-Deployment
Model Integrity and Auditing
Cryptographic Integrity Protections
Guardrail Testing Patterns for Automated Prompt Sanitization
Separation of Duties & Dual Control for High-Risk AI Models
RSAIF Mapping, GRC Interpretation, and Evidence Requirements
Hands-On: Implementing RBAC for Secure AI APIs
Knowledge Check
Case Study – Implementing AI in Coordinated Assurance
Ethical Considerations in the Case Study
Overview of Case Outcomes
Introduction to AI Security Tools
Automating AI Security and Compliance
Hallucination Monitoring and Scoring Mechanisms
Architecture of Automated Compliance Pipeline
Cross-Model Validation for Multi-Model AI Systems
Tool Selection Criteria and Capability Matrix
Real Automation Workflow & Evidence Generation
Hands-On Lab
AI Governance Frameworks and Controls
Trust, Transparency, and Ethics in AI-Enabled Assurance
Measuring Assurance Effectiveness (Metrics, KRIs, KPIs)
Conclusion and Next Steps
Job Roles & Industry Outlook
Chief Audit Executives & Internal Audit Leaders
Heads of audit driving integrated assurance strategies aligned with IIA Standard 9.5.
Risk & Governance Professionals
Risk managers and GRC leaders unifying risk insights and strengthening governance with AI tools.
Compliance & Regulatory Officers
Professionals meeting ISO, NIST, and EU AI Act requirements through automated monitoring.
Assurance & Control Owners
Teams across internal audit, compliance, cybersecurity, and vendor risk mapping coverage and improving reliance decisions.
AI & Security Architects
Leaders implementing secure, trustworthy AI with model integrity and enforcement controls.
Skills You’ll Gain
Tools You’ll Master