What is an AI Audit?
An AI audit is a structured review of AI models, data, pipelines, and infrastructure to confirm they are secure, compliant, and operating as intended. Rather than assuming AI systems are behaving correctly, audits measure them against defined technical, operational, and governance standards.
AI audits typically fall into two categories:
External audits: Third-party or regulatory assessments that provide independent validation of AI systems.
Internal audits: Ongoing reviews and monitoring conducted by internal security, compliance, or risk teams.
AI audits examine both technical controls and organizational processes. On the technical side, auditors review security configurations, access permissions, and infrastructure exposure. On the governance side, they assess oversight models, documentation practices, and accountability structures.
AI audits must also account for cloud-native deployments. Many AI workloads run on managed services and distributed platforms, requiring auditors to understand containers, serverless execution models, and multi-cloud architectures.
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What's the difference between model validation and AI security audits?
Model validation (often part of Model Risk Management) focuses on whether an AI model performs as intended from a statistical and business perspective. Validators typically assess:
Model accuracy, precision, recall, and related metrics
Bias and fairness across relevant populations
Stability under varying inputs and scenarios
Alignment with business objectives and use case requirements
Documentation of assumptions and limitations
AI security audits focus on whether AI systems are protected from threats and meet security and compliance expectations. Security auditors examine:
Exposure to adversarial attacks such as prompt injection or data poisoning
Access controls and encryption for models and datasets
Infrastructure security and network exposure
Supply chain trust for third-party models and dependencies
Incident detection and response readiness
While distinct, these efforts overlap. Mature organizations integrate model validation and security audits into a unified AI risk management program, ensuring AI systems are both effective and secure.
Why Organizations Audit AI Systems
Organizations audit AI systems because these systems increasingly influence customers, finances, and critical operations. As AI becomes embedded into core business workflows, organizations need clear evidence that their AI systems are controlled, accountable, and operating within defined risk boundaries.
Regulatory pressure is a major driver. Regulations such as the EU AI Act, GDPR, and industry-specific rules explicitly address AI behavior, training data, and automated decision-making. Regular AI audits help organizations demonstrate compliance, prepare for regulatory scrutiny, and document how AI risks are identified and managed over time.
Security risk is another key factor. While AI-specific attacks such as prompt injection or model inversion receive attention, the more fundamental issue is that AI dramatically expands the attack surface. AI systems introduce new APIs, service identities, data flows, and integrations across cloud environments – often connecting sensitive data to externally accessible services. Each new model, endpoint, or pipeline increases the number of places where misconfigurations or weak controls can lead to unauthorized access or data exposure. AI audits help organizations understand and control this expanded attack surface before incidents occur.
AI audits also support operational reliability. Models can silently degrade as data changes, pipelines can become unstable, and monitoring gaps can go unnoticed. Audits surface these issues early, helping teams maintain consistent performance and avoid unexpected failures in production systems.
Finally, AI audits protect organizational trust. Customers, partners, and regulators increasingly expect transparency around how AI systems are built, secured, and governed. Demonstrating that AI systems are regularly audited – across security, data governance, and operational controls – helps organizations build confidence in their use of AI while reducing the likelihood of costly incidents or reputational damage.
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What Do AI Audits Evaluate?
AI audits examine four main areas: data, models, infrastructure, and governance. Each area answers different questions, but they all connect to give you a complete picture of your AI risk.
Data Security and Privacy
AI systems are only as safe as the data they use. Audits spend significant time on data security because training and inference pipelines often touch your most sensitive information.
Key areas include:
Training and inference data sources: Where does your data come from, and is it stored securely?
Access controls and encryption: Who can read, write, or export datasets? Is data encrypted at rest and in transit?
Sensitive data exposure: Do training sets contain PII, health records, or payment data? Can the model leak this information in its outputs?
Data lineage and retention: Can you trace how data moved from raw sources to training sets? Are retention policies enforced?
Model Security and Integrity
This area focuses on making sure your models aren't tampered with, stolen, or silently replaced.
Audits will examine how you protect model artifacts, containers, and deployment packages. They'll review your versioning and signing practices – can you verify which model version is in production, who approved it, and that artifacts carry cryptographic signatures? They'll also check your defenses against model theft and extraction attacks, including access controls on model registries and API rate limiting.
Supply chain trust matters here too. If you use open-source models or pre-trained weights, auditors want to know how you vet those external components.
Infrastructure and Deployment Security
Most AI workloads run in cloud environments, so AI audits must evaluate your cloud security posture.
Audits look at cloud configuration of AI workloads, checking for misconfigurations like public S3 buckets or Azure Blob containers holding training data. They examine network exposure of model endpoints and APIs, verifying use of private networking (AWS PrivateLink, Azure Private Link, GCP Private Service Connect) and API gateway authentication (Amazon API Gateway, Azure API Management, Apigee).
They review identity and access management to ensure AI service accounts follow least privilege – for example, checking that SageMaker execution roles or Vertex AI service accounts can only access the specific S3 buckets or Cloud Storage buckets they need. They verify that IAM policies use conditions to restrict access by IP range, time, or MFA status where appropriate.
They also check secrets management to confirm API keys, database credentials, and model registry tokens aren't hard-coded in Jupyter notebooks or training scripts. Auditors expect to see integration with managed secrets services (AWS Secrets Manager, Azure Key Vault, GCP Secret Manager) and rotation policies for long-lived credentials.
Compliance and Governance
The governance dimension covers the process side of AI auditing – how your organization manages and oversees AI systems.
Auditors check alignment with AI-related regulations (such as GDPR Article 22 on automated decision-making, the EU AI Act, and sector-specific rules) and internal policies. They verify that governance frameworks cover model development, deployment approvals, and ongoing monitoring. They review documentation of model purpose, limitations, and risk. They examine explainability mechanisms – can you explain key decisions to users or regulators through model cards, data sheets for datasets, approval logs, and risk assessments? They also verify that audit logging (CloudTrail, Azure Monitor, Cloud Logging), real-time monitoring dashboards, and human oversight controls are in place and functioning.
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EU AI Act Audit Readiness
The EU AI Act introduces a risk-based regulatory framework for AI systems, with phased enforcement beginning in 2025–2026. For many organizations, AI audits will become a primary way to demonstrate readiness, compliance, and ongoing risk management under this regulation.
Rather than treating the EU AI Act as a one-time certification exercise, organizations should view it as a requirement for continuous auditability across AI systems.
Risk Classification
AI audits begin by determining how an AI system is classified under the EU AI Act.
Auditors assess whether an AI system falls into one of the Act’s categories – prohibited, high-risk, limited-risk, or minimal-risk – based on its intended use and potential impact. Systems classified as high-risk, such as those used in employment decisions, creditworthiness assessments, or critical infrastructure, are subject to the most stringent audit and documentation requirements.
Correct classification is critical, as it determines the scope and depth of audit evidence required.
Technical Documentation
The EU AI Act places strong emphasis on documentation that demonstrates how AI systems are designed, trained, and controlled.
Audits review whether organizations maintain documentation covering:
System design and development processes
Training data sources, preprocessing methods, and data governance controls
Model architecture, performance characteristics, and known limitations
Risk management measures and testing outcomes
Human oversight mechanisms and escalation paths
Well-structured documentation enables auditors to evaluate not just outcomes, but the controls used to manage AI risk.
Post-Market Monitoring
For high-risk AI systems, audits verify that organizations have implemented ongoing monitoring after deployment.
Auditors examine whether teams can detect:
Model drift or performance degradation
Emerging bias or unintended behavior
Security incidents or misuse affecting AI outputs
They also review how incidents are documented, investigated, and remediated. This reinforces the expectation that AI compliance is an ongoing process, not a one-time review.
Transparency and Explainability
The EU AI Act requires appropriate transparency based on system risk and use case.
Audits assess whether organizations can:
Clearly communicate the purpose and limitations of AI systems
Provide meaningful explanations of AI-assisted decisions when required
Inform users of their rights, including the ability to challenge automated decisions
The depth of explainability expected varies by context, but the ability to demonstrate transparency is a core audit requirement.
Data Governance
AI audits under the EU AI Act place particular focus on training data governance.
Auditors evaluate whether training datasets are:
Relevant and representative for the intended use case
Subject to data quality and bias controls
Managed in compliance with GDPR and applicable data protection laws
Strong data governance is essential for reducing downstream risk and supporting audit defensibility.
Roles and Responsibilities for AI Audits
AI audits are not owned by a single team. Because AI systems span data, models, infrastructure, and governance, effective audits require coordination across multiple functions. Clear responsibility boundaries help prevent gaps, delays, and duplicated effort.
Rather than assigning ownership to one group, mature organizations define how responsibilities are shared across teams throughout the audit lifecycle.
Security Teams
Security teams focus on protecting AI systems from misuse, exposure, and compromise.
They typically:
Assess security controls around AI infrastructure, identities, and endpoints
Evaluate exposure to AI-specific threats and expanded attack surface
Review logging, monitoring, and incident response readiness
Security teams are usually accountable for confirming that AI systems meet baseline security expectations before and after production deployment.
AI and Machine Learning Engineering Teams
AI and ML engineering teams provide deep visibility into how models are built and behave.
They typically:
Document model architecture, training processes, and performance metrics
Provide evidence of data lineage, versioning, and model provenance
Implement model-level controls such as validation, monitoring, and rollback
Their input is essential for understanding whether models are functioning as intended and how technical risks manifest in practice.
Platform and Cloud Engineering Teams
Platform and cloud teams manage the infrastructure that AI systems depend on.
They typically:
Configure cloud resources, networking, and access controls supporting AI workloads
Maintain encryption, secrets management, and environment isolation
Address infrastructure-level findings surfaced during audits
Because most AI systems run on shared cloud platforms, platform teams play a critical role in reducing blast radius and preventing misconfigurations.
Governance, Risk, and Compliance (GRC) Teams
GRC teams coordinate the audit process and connect technical findings to regulatory and policy requirements.
They typically:
Map AI controls to internal policies and external frameworks
Manage audit planning, evidence collection, and reporting
Interpret regulatory obligations such as the EU AI Act or sector-specific rules
GRC teams help ensure audit outcomes translate into defensible compliance posture.
Data Governance and Privacy Teams
Data governance and privacy teams focus on how data is collected, processed, and retained within AI systems.
They typically:
Classify datasets and assess sensitivity
Conduct data protection impact assessments where required
Review retention, deletion, and access controls for training and inference data
Their involvement is critical when AI systems handle personal or regulated information.
Legal Teams
Legal teams assess contractual, liability, and regulatory implications of AI use.
They typically:
Review agreements with AI vendors and third-party model providers
Advise on intellectual property, liability, and regulatory interpretation
Evaluate audit findings for potential legal exposure
Legal input helps ensure that audit results are addressed in a way that reduces long-term risk.
Common AI Auditing Frameworks
Organizations don’t need to design AI audit programs from scratch. Several established frameworks provide guidance on how to assess AI risk, security, and governance in a structured way. Most AI audit programs draw from multiple frameworks rather than relying on a single standard.
The goal is not strict compliance with every framework, but using them as reference points to define controls, evidence, and audit scope.
NIST AI Risk Management Framework (AI RMF)
The NIST AI Risk Management Framework provides a comprehensive structure for identifying, assessing, and managing AI risk across the full lifecycle.
It organizes AI risk into four core functions:
Govern – establishing policies, accountability, and oversight
Map – understanding AI use cases, contexts, and impacts
Measure – assessing risks, performance, and controls
Manage – prioritizing and mitigating identified risks
AI audits often use the NIST AI RMF to evaluate whether organizations have a repeatable process for identifying and managing AI risk over time.
ISO/IEC 42001
ISO/IEC 42001 is a management system standard for AI, similar in structure to ISO 27001 for information security.
It focuses on:
Defining AI governance structures
Establishing documented controls and responsibilities
Demonstrating continuous improvement
Audits aligned to ISO/IEC 42001 emphasize organizational processes, accountability, and evidence of ongoing AI governance rather than one-time technical checks.
EU AI Act Requirements
The EU AI Act introduces legally binding obligations for AI systems, particularly those classified as high risk.
Audit programs aligned to the EU AI Act focus on:
Risk classification and documentation
Training data governance and quality controls
Transparency, explainability, and human oversight
Post-deployment monitoring and incident management
Because enforcement is risk-based, audits help organizations determine which requirements apply and how to demonstrate compliance.
OWASP ML Security Top 10
The OWASP ML Security Top 10 highlights common security risks specific to machine learning systems, such as model theft, data poisoning, and inference abuse.
AI audits use this framework to ensure security testing covers AI-specific attack patterns that traditional application security reviews often miss.
MITRE ATLAS
MITRE ATLAS provides a knowledge base of real-world adversarial techniques targeting machine learning systems.
Rather than prescribing controls, ATLAS helps auditors understand how AI systems can be attacked in practice and where defensive controls may be weakest.
How AI Audits Fit Into Cloud-Native Security
AI systems are not standalone applications. They are built, trained, and deployed on cloud infrastructure using the same compute, networking, identity, and data services as the rest of the environment. As organizations adopt AI at scale, this rapidly expands the cloud attack surface – often faster than traditional security controls were designed to handle.
Each new model, training pipeline, inference endpoint, or API introduces additional identities, permissions, network paths, and data access. Even when models themselves are well designed, weaknesses in the surrounding cloud environment can expose AI systems to misuse, data leakage, or unauthorized access. As a result, AI risk cannot be separated from cloud risk.
AI audits extend existing cloud security practices rather than replacing them. They build on disciplines such as cloud posture management, identity and access management, data security, and vulnerability management, while expanding coverage to AI-specific resources that traditional tools often overlook. This includes model endpoints, training pipelines, AI service identities, and the data flows that connect them.
Point-in-time audits struggle in cloud-native environments where change is constant. AI systems evolve quickly – models are retrained, pipelines are updated, permissions shift, and new services are introduced. A configuration that was compliant yesterday can become risky today as the surrounding context changes. AI audits therefore depend on continuous visibility rather than periodic snapshots.
Many AI risks only become visible when signals are connected across layers. An inference endpoint may appear secure in isolation, but represent significant risk when it is internet-exposed, runs under an overprivileged identity, and can access sensitive training data. Cloud-native AI audits correlate infrastructure, identity, data, and AI context to reveal these attack paths and prioritize the risks that matter most.
As AI adoption accelerates, auditing must evolve from episodic assessments to continuous assurance. Integrating AI audit logic into cloud-native security workflows – configuration monitoring, CI/CD pipelines, and runtime detection – allows organizations to remain audit-ready as their AI environments change. This approach reduces manual effort, improves coverage, and aligns AI audits with the realities of modern cloud and AI development.
Enabling AI Audit Readiness with Wiz
AI audits span security, governance, model performance, and regulatory oversight. While no single platform can cover every dimension of a complete AI audit, Wiz plays a critical role in enabling the security and cloud infrastructure portions of AI audit programs.
Because modern AI systems are built and operated in the cloud, security auditors depend on accurate, continuous visibility into cloud infrastructure, identities, data access, and AI services. Wiz provides this foundation by giving organizations a unified view of the cloud environments where AI systems run.
Wiz’s agentless approach continuously discovers managed AI services such as Amazon Bedrock, Amazon SageMaker, and Google Vertex AI, along with the storage, identities, networks, containers, and APIs that support them. This allows security and audit teams to maintain an up-to-date inventory of AI-related cloud assets (AI-BOM) without deploying agents or impacting workloads.
At the core of Wiz is the Security Graph, which correlates AI services with cloud identities, network exposure, vulnerabilities, and sensitive data. This context is essential for AI security audits, because many audit findings only become material when multiple conditions exist together – for example, an AI endpoint that is internet-exposed and runs under an overprivileged identity and can access sensitive training data.
Wiz supports key AI audit requirements on the security side by:
Identifying misconfigurations affecting AI services and infrastructure
Mapping effective permissions for AI service identities
Discovering and classifying sensitive data used in AI pipelines
Revealing attack paths created by combinations of exposure, identity, and data access
These insights help security teams produce audit evidence related to access control, data protection, logging, and cloud configuration.
Used alongside governance processes, model validation, and regulatory oversight, Wiz helps organizations scale AI security in cloud-native environments – reducing blind spots, accelerating remediation, and supporting defensible AI audit outcomes.
Request a demo to see how Wiz supports the security and cloud infrastructure components of AI audits, helping teams maintain continuous AI security visibility across AWS, Azure, and GCP.
Accelerate AI Innovation, Securely
Learn why CISOs at the fastest growing companies choose Wiz to secure their organization's AI infrastructure.