AI Audits: Key Considerations for Compliance and Governance

The rapid integration of AI agents into organizational operations is raising significant questions about compliance and governance. According to the 2025 Vanta State of Trust report, 72% of organizations are using or planning to implement agentic AI, while 65% admit that their pace of AI adoption exceeds their understanding of it. This disconnect has sparked growing concern among auditors reflecting these dynamics in their assessments.

In 2025, 72% of S&P 500 companies reported at least one substantial AI-related risk, a sharp rise from 12% in 2023. Yet, only 26% have established governance frameworks for their AI systems. This oversight gap is prompting a shift toward formalized standards. The introduction of ISO 42001 in 2023 provides a structured AI Management System that auditors can certify against, aligning with the EU AI Act, which will become enforceable in August 2026.

As organizations navigate this environment, auditors are not waiting for AI-specific frameworks to develop; they are using existing compliance frameworks like SOC 2 and the NIST AI RMF to evaluate AI systems. With increasing scrutiny on AI governance, companies must grasp what auditors will focus on during assessments.

Key Audit Focus Areas

Auditors will likely concentrate on several critical areas when assessing AI systems. Here are nine essential factors that organizations should prepare to demonstrate:

1. Comprehensive Inventory of AI Agents
   Auditors will require a detailed inventory of all AI agents in use within an organization. This inventory should include information on the deployment of agents across various departments and the specific actions they perform. Without complete mapping, organizations risk entering the realm of shadow AI, where unmonitored systems can introduce unforeseen risks.

2. Defined Ownership of AI Systems
   Each AI system must have a designated owner responsible for its use, performance, and risk management. This clearly defined ownership helps mitigate the risks associated with shadow AI, ensuring accountability across departments.

3. Clear Boundaries of Agent Capabilities
   Establishing well-defined permissions for each AI agent is critical. Auditors will look for evidence that access and actions are controlled and that agents operate within set parameters. For instance, a support agent may have the ability to issue small refunds but require human oversight for larger transactions.

4. Evidence of Human Oversight
   Auditors expect clear mechanisms for human intervention in AI operations. This includes well-documented approval processes for sensitive actions and established escalation paths to manage unusual behavior.

   

5. Logging and Traceability
   Transparency in AI decision-making is essential. Organizations should maintain detailed logs that document agent actions, including what decisions were made, the inputs used, and the rationale behind those decisions.

6. Data Handling Controls
   The effectiveness of AI systems is directly linked to data management practices. Auditors will seek to understand how data is accessed and used, ensuring that sensitive information is adequately protected. Anonymization and minimization of data access are key factors in this evaluation.

7. AI-Specific Risk Assessments
   Given the unique risks associated with AI, auditors will expect formalized risk assessments that address potential misuse, model failures, and downstream impacts. ISO 42001 requires organizations to conduct AI impact assessments to evaluate these risks thoroughly.

8. Continuous Monitoring Practices
   AI systems are dynamic and require ongoing monitoring rather than periodic reviews. Auditors will look for systems in place to track agent behavior continuously and alert for any anomalies, ensuring proactive risk management.

9. Demonstrable Evidence of Compliance
   Finally, auditors prioritize tangible proof that controls are effective. Organizations should focus on collecting verifiable evidence that aligns with their governance policies, transforming compliance from a burdensome task into an automated process.

Preparing for Upcoming Audits

The path to effective AI governance does not require an overnight overhaul of existing systems. Organizations should start by creating a centralized inventory of AI agents, assigning clear ownership, and implementing access controls. Continuous activity monitoring and automated evidence collection will also be crucial as they prepare for future audits.

As AI continues to evolve, companies that proactively address these audit requirements will not only manage risk more effectively but also position themselves favorably within the regulatory landscape. With the anticipated full implementation of the EU AI Act on the horizon, the time to act is now.