An AI audit systematically evaluates an organization’s AI usage and helps to proactively mitigate excessive risks from negative outcomes like hallucinations and bias. It can also build trust and relieve mounting stakeholder pressure to prove that AI systems are compliant, transparent, ethical/fair, secure, accurate, and adequately governed.

If your organization is developing or using AI, you need to audit all your AI systems as a critical step toward responsible governance and to protect your customers, operations, brand reputation, and finances.

This article explains how AI audits work, their business benefits, and how to get started auditing your own AI before unseen risk catches up with you. 

Key takeaways

• If your business is using AI, you need an AI audit program to reduce unacceptable AI-specific risks and help defend an expanded cyberattack surface.
• The purpose of an AI audit is to evaluate and document that an AI system operates in a controlled, accountable manner within clearly defined risk boundaries. 
• Even though the US government advocates deregulating AI, overall compliance and market pressure demands strong governance of AI behavior, decision-making, and training data.
• AI audits offer numerous advantages, including improved risk management, compliance support, stronger cybersecurity, and increased stakeholder trust.

What is an AI audit and why is it so important?

An AI audit comprehensively evaluates an AI system’s data, algorithms, and output to ensure it is operating accurately, fairly, transparently, securely, and within compliance, policy, ethical, and legal parameters. 

As businesses embed AI into core workflows, it becomes imperative to validate and document that AI systems operate in a controlled, accountable manner within clear risk parameters. 

Failure to audit AI systems can expose a business to complex, unique, and highly damaging AI risks. For example:

• Using AI radically expands a firm’s attack surface by introducing new APIs, complex new identities, massive data flows, and cloud-based integrations—all while making sensitive data externally accessible. Misconfigurations or inadequate guardrails can open the door to data breaches or unapproved actions.
• AI-specific cyberattacks like prompt injection or model inversion can put AI systems under a hacker’s control.
• Over time AI model performance can degrade and destabilize in subtle ways, leading to unforeseen failures and unpredictable results.
• Despite US government efforts to deregulate AI, overall compliance and market pressure to control, document, and govern AI behavior, automated decision-making, and training data continues to increase. 
• Perhaps most importantly, failure to audit AI undermines brand reputation, confidence, and trust. Customers, partners, investors, and other stakeholders all demand transparency around AI system development and operation to address the increased risk of damaging incidents.

What do AI audits look for? To get a wider view of AI risk, audits look at several key areas:

• Training and inference data sources, including where they come from, how they are stored, and whether they contain or could leak sensitive data.
• AI-specific threat modeling to identify unique AI cybersecurity vulnerabilities.
• Model security and integrity, including controls to protect containers, deployment packages, artifacts, etc. 
• Supply chain risks, such as using open-source components.
• Access controls and encryption, including who/what can read, write, or export data and whether least privilege principles are enforced.
• AI system infrastructure, including cloud configurations, network security, API authentication, and management of API keys and other credentials.
• Cloud-native AI deployments on managed services and multi-cloud platforms that include containers, serverless architectures, etc.
• Governance and compliance processes, such as operation of “human in the loop” mechanisms and alignment with applicable laws (e.g., the EU AI Act) and risk management frameworks.
• The AI system’s risk classification under the EU AI Act. Identifying a model’s risk level determines the level of audit evidence needed to evaluate the AI.

How does an AI audit classify model risk?

Part of any AI audit is to classify the AI system according to its risk category under the EU AI Act. While this four-tier system is unique to the Act, knowing an AI’s risk classification is fundamental for risk-based governance and to inform investors, regulators, users, and other stakeholders even if a business is not currently covered by the Act.

The four EU AI Act risk classifications are:

• Prohibited (Unacceptable) Risk: AI systems whose use directly threatens public safety, privacy rights, peoples’ livelihoods, etc. Examples include social scoring tools, systems that conduct untargeted facial image scraping, or AI that manipulates human behavior.
• High Risk: AI systems used in law enforcement, critical infrastructure, education, credit assessment, or employment/HR that must strictly comply with applicable guidelines to avoid causing harm.
• Limited Risk: AI systems that poses minor risks for manipulating or deceiving users, such that transparency is needed so users know they are interacting with AI. Chatbots and virtual assistants are in this category, along with AI that generates deepfake content.
• Minimal Risk: AI systems that offer little or no threat to safety or rights, such as spam filters, recommendation engines, AI-enabled video games, or other AI tools that do not process sensitive data and are not intended to replace human judgement.

Why does my organization need an AI audit?

AI audits offer a range of advantages, especially:

• Risk management—AI audits are a critical part of identifying and addressing new and complex AI risks around algorithmic bias, model drift, data misuse, and legal/compliance violations.
• Compliance support—AI audits help demonstrate that your AI system(s) meet regulatory and/or agreed standards.
• Enhancing cybersecurity—AI audits include a security component that identifies code vulnerabilities and misconfigurations and helps improve controls and guardrails.
• Ensuring fairness—AI audits look for AI data and model biases that could skew decisions or lead to discrimination, potentially resulting in legal actions and reputational damage.
• Spotting hallucinations—AI audits help detect possible hallucinations (results the AI invents) and other errors not caused by hacking.
• Increasing stakeholder trust—An independent AI audit demonstrates a commitment to AI governance and builds confidence among clients, investors, employees, and senior leaders. 
• Supporting strong AI governance—As AI autonomy grows and its use cases proliferate, AI governance needs to move beyond documentation and policy to encompass ongoing evaluation and oversight for every AI system a business is using. 

Companies should audit AI systems before launching a new system and on an ongoing basis thereafter. High-risk systems should be audited more frequently.

Why do we need AI audits when the US government wants to deregulate AI?

Recent US government efforts to minimize AI regulation and governance do not eliminate the major financial, operational, strategic, security, legal, and reputational risks that AI poses to business, society, and even national security. AI governance including AI audits are not only an obligation to stakeholders but also a business continuity and incident management necessity for every organization that uses AI. 

Where government does not support AI governance, the private sector must develop and maintain acceptable AI standards to enable effective risk management and avoid unacceptable negative or catastrophic outcomes.

What are important AI audit compliance considerations?

On March 20, 2026, the Trump administration proposed its National AI Legislative Framework, which aims to create a unified AI compliance framework while nullifying more comprehensive US state laws already in place (e.g., California SB 942 and Colorado SB 24-205). This adds more confusion to an already fragmented and rapidly evolving compliance landscape, especially in the near term. 

In addition to US state and federal AI laws proposed and in effect, AI developers and users may be covered by other compliance requirements:

• The US Federal Trade Commission (FTC), Equal Employment Opportunity Commission (EEOC), and other US agencies actively apply existing laws to regulate AI.  
• The Financial Industry Regulatory Authority (FINRA) just released its 2026 oversight report for US investment firms, which includes a major new section on compliance responsibilities for AI governance.
• Regulated businesses need to ensure their AI usage complies with critical laws like the Health Insurance Portability and Accountability Act (HIPAA) and the US Federal Reserve’s Guidance on Model Risk Management.
• Firms that do business outside the US may need to comply with local laws, particularly the groundbreaking EU AI Act, which has been in force since 2024. The EU’s General Data Protection Regulation (GDPR) also impacts AI usage where it intersects privacy. Canada also has a range of provincial and industry-specific regulations governing AI but currently has no national AI law.

Other global laws and guidelines that may impact your company’s AI audit practices include:

• The voluntary NIST AI Risk Management Framework (AI RMF). Developed in 2023, it provides best-practice guidance on identifying, assessing, and managing AI lifecycle risks for both public- and private-sector entities.
• Singapore’s Model AI Governance Framework. Updated in 2024 with detailed guidance, it is a popular model for private-sector organizations worldwide.
• Japan’s AI Governance Guidelines. Updated in 2023, this standard emphasizes risk assessment and independent third-party audits to ensure accountability for AI.
• The Organization for Economic Cooperation and Development (OECD) AI Principles, a voluntary standard that has thus far been adopted by all 38 member countries plus 9 others, including Brazil, Argentina, Ukraine, Romania, and Egypt. 

Who performs AI audits?

There are two types of AI audits:

• Internal audits, reviews, and ongoing monitoring conducted by internal IT, cybersecurity, and/or compliance teams  
• External audits that independently assess AI system operation and risk, such as regulatory or third-party audits 

AI audits differ significantly from typical IT audits because of the unique nature of AI systems. Unlike the mainly static and deterministic nature of conventional software, AI exhibits probabilistic, generative, and adaptive behaviors while processing training data that is also constantly evolving. AI audits encompass the full AI lifecycle from conception to development to training to deployment to operation. As such, they benefit from special skills and experience with AI models, pipelines, data, etc.

While regulated enterprises and critical infrastructure businesses often have internal compliance and audit teams but may still use third-party experts to support AI legal and compliance programs. For SMBs that lack internal audit resources, outsourcing to a trusted specialist partner can help educate in-house staff while standing up AI audit, governance, and compliance processes and policies.

The more a business has invested in its AI strategy, the more relevant it may be to leverage third-party AI audit resources in the service of building stakeholder trust.

How can my business get started with auditing AI?

Auditing AI can start with asking fundamental questions around how your organization is using AI and what AI risks you are managing today. As you check in with IT, data science, and C-suite teams to build an AI audit strategy, you are also building the collaborative connections you will need for an AI audit program to succeed. 

Another essential step towards an AI audit program is to create an inventory of every AI system your organization is using, including AI tools embedded in SaaS applications. This list will almost certainly include many “shadow AI” offerings. 

Other analysis that can help build an AI audit process aligned with business strategy include:

• How are employees using AI in their everyday workflows and activities?
• What large language AI models (LLMs) are employees using?
• What data does AI have access to for processing and training?
• How does the company’s use of AI tools increase the cyber attack surface? 
• How does the company’s use of AI increase the risk of brand reputational harm?
• Are AI costs consistently offset by cost savings from automation?
• How are AI users held accountable to the company’s AI policy around ethics, the environment, and other social considerations?
• How do senior leaders gain visibility into how the business is using AI?

What’s next?

For more guidance on this topic, listen to Episode 157 of The Virtual CISO Podcast with guest Marco Figueroa, GenAI Bug Bounty Programs Manager at Mozilla.