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Copilot, not autopilot: Building a defensible trust chain for AI testing across GRC and audit
May 13 @ 2:00 pm – 3:00 pm EDT
Description:
AI is no longer a future consideration for assurance functions. It is reshaping how monitoring, testing, and risk decisions are made today. As organizations rapidly deploy copilots, agents, and AI-enabled controls, the central question has shifted from whether to adopt AI to how to govern it, scale it responsibly, and demonstrate measurable value without eroding accountability.
This session introduces a practical AI trust chain model aligned to the three lines model, designed to address the growing tension between accelerating AI capabilities and the slower pace of enterprise assurance readiness. Attendees will explore how GRC can establish guardrails for AI usage, validation, and change management, while internal audit applies risk-based validation, selective reperformance, and documentation standards that allow AI-assisted testing to withstand regulatory and stakeholder scrutiny.
Rather than treating AI as a black box or bolting it onto legacy workflows, this webinar focuses on human-in-the-loop assurance, clear ownership, and evidence-based reliance. Participants will leave with a concrete framework for distinguishing AI-native assurance practices from superficial automation, enabling teams to move beyond pilots and into scalable, defensible use of AI across GRC and audit.
Learning objectives:
- Differentiate AI-native assurance from AI-overlay tooling – Identify the practical and governance differences between tools that merely automate existing audit workflows and those designed with AI-first assurance principles, including implications for control reliability, explainability, and regulator confidence.
- Design a defensible AI trust chain across the Three Lines – Apply a clear model for how management, GRC, and internal audit each contribute to validating AI usage, covering ownership, validation boundaries, documentation expectations, and escalation points.
- Establish evidence standards for AI-assisted testing and monitoring – Define what “audit-reliance ready” looks like for AI outputs, including selective reperformance, validation thresholds, human review checkpoints, and traceability aligned to risk.
- Prove ROI without sacrificing accountability or assurance quality – Connect AI efficiency gains (coverage, cycle time reduction, continuous monitoring) to measurable outcomes while maintaining human judgment, governance oversight, and regulatory defensibility.
CPE credit(s): 1 CPE upon live viewing and participation. CPEs not offered on-demand.
Field of study: Auditing
Instructional delivery method: Group Internet Based
Level: Basic
Prerequisite: None
Advanced preparation: None
*Click here for more CPE information.
