AI+ Coordinated Assurance: Enhancing Collaboration Across Risk and Audit Functions

Who will benefit from this course?

Internal auditors seeking to strengthen relationships with other assurance providers or regulators Risk management and compliance professionals involved in enterprise assurance mapping External auditors and consultants collaborating with internal audit functions Chief Audit Executives (CAEs), Chief Risk Officers (CROs), Chief Compliance Officers (CCOs) and audit leaders (VPs, directors, etc.) responsible for optimizing coordinated assurance frameworks.

Course Objectives

By the end of this course, participants will be able to demonstrate the following competencies and outcomes:

• Coordinate Assurance Activities Using AI: Design and implement a coordinated assurance approach across multiple functions (audit, risk, compliance, etc.) leveraging AI tools to improve coverage and reduce duplication.
• Develop Integrated Assurance Maps: Use AI-assisted platforms to create assurance maps that identify overlaps, gaps, and reliance opportunities among different assurance providers, thereby optimizing risk coverage enterprise-wide.
• Apply AI Governance Frameworks: Interpret and apply key AI governance and risk management frameworks – including NIST’s AI Risk Management Framework, ISO/IEC 42001 (AI Management System Standard), and relevant regulations like the EU AI Act – to evaluate and audit AI systems for compliance, ethical considerations, and risk mitigation.
• Utilize AI for Audit Efficiency and Reporting: Employ machine learning and generative AI tools (e.g., NLP-driven analytics, large language models) to streamline audit processes – from risk assessment and data analysis to drafting audit reports and executive summaries – while maintaining accuracy and consistency in communications across functions.
• Enhance Communication and Collaboration: Use collaborative platforms and AI-driven insights to improve communication2 between internal audit, risk management, compliance, and external auditors. Participants will learn techniques (including assurance mapping and joint risk workshops) to build trust and reliance on each other’s work, aligned with IIA Standard 9.5.
• Identify and Mitigate AI-Related Ethical Risks: Recognize potential AI ethics issues such as bias in AI models or “hallucinations” (fabricated outputs) from generative AI. Develop controls and oversight practices to mitigate these risks, ensuring transparent, fair, and reliable use of AI in assurance activities.
• Demonstrate Strategic Leadership in AI-Enabled Assurance: Articulate the value and ROI of AI-enabled coordinated assurance to senior management and boards. Define metrics (KPIs) to measure the effectiveness of coordinated assurance (efficiency gains, risk coverage, etc.), and continuously improve the assurance strategy using data-driven insights.

Course Topics

Module 1: Introduction to Coordinated Assurance (Foundations and Context)
This opening module establishes the concept of coordinated assurance and its importance in the current regulatory and business environment.

Module 2: The Role of AI in Enhancing Collaboration
This module explores how artificial intelligence can act as an enabler for coordinated assurance. It provides a primer on relevant AI technologies and illustrates their collaborative potential.

Module 3: Using AI for Assurance Mapping and Reliance
In this module, participants learn how to practically build and utilize assurance maps with the help of AI, to visualize and manage coverage across the organization’s risk universe. They also examine how AI can identify where one function can rely on another’s work, reducing duplication.

Module 4: Applying AI to Streamline Communication and Reporting
This module focuses on how AI can be used to unify reporting across assurance functions and streamline the flow of information to stakeholders. It covers AI applications in report generation, policy review, and ensuring consistency in risk language.

Module 5: Case Study – Implementing AI in Coordinated Assurance
In this module, learners examine a detailed real-world case study of an organization that adopted AI to enhance coordinated assurance. The case study provides a narrative of how AI tools were implemented, the challenges faced, and the benefits realized across audit and risk functions.

Module 6: Practical Application – Coordinated Assurance Simulation
This is a hands-on workshop module where participants actively apply what they have learned in a simulated environment. The simulation is designed to mirror a real coordination scenario and involves role-playing and use of AI tools under instructor guidance.

Module 7: Conclusion – Building Trust and Governance Across Assurance Functions
The final module wraps up the program by focusing on maintaining trust in AI-enabled assurance and instituting proper governance for the ongoing use of AI in assurance activities.