Cyber Audit Authority

Auditing AI and Machine Learning Systems for Cybersecurity Risks

AI and machine learning systems introduce a distinct category of cybersecurity risk that standard audit frameworks were not designed to address. This page describes the structure, scope, and professional practice of auditing these systems — covering how AI-specific threat vectors are assessed, what regulatory and standards bodies govern this space, and where AI audits differ from conventional application or infrastructure audits. The material is relevant to auditors, compliance officers, and technology governance professionals working in organizations where AI systems touch sensitive data, automated decision-making, or critical operational functions.

Definition and scope

Auditing AI and machine learning systems for cybersecurity risks is the systematic evaluation of how AI components — models, training pipelines, inference endpoints, and data supply chains — are exposed to attack, manipulation, or unauthorized access. This scope extends beyond conventional application security audit concerns to include AI-specific attack surfaces: adversarial inputs, model inversion, data poisoning, and prompt injection.

The National Institute of Standards and Technology (NIST) formally recognizes AI-specific risk in its AI Risk Management Framework (AI RMF 1.0), published in January 2023. That framework identifies four core functions — Map, Measure, Manage, Govern — applicable to AI risk broadly, including cybersecurity dimensions. NIST Trustworthy AI categories include adversarial robustness and data integrity, both of which require audit-level evaluation in operational deployments.

The scope of an AI cybersecurity audit typically encompasses:

  1. Model integrity — verifying that trained models have not been altered after deployment
  2. Training data provenance — establishing chain of custody and integrity controls over datasets
  3. Inference security — assessing exposure of prediction APIs and model outputs to adversarial manipulation
  4. Access control over AI assets — reviewing who can query, retrain, or extract model parameters
  5. Third-party and supply chain risk — evaluating pre-trained models, libraries, and external data feeds (see supply chain cybersecurity audit)
  6. Monitoring and anomaly detection — confirming that runtime behavior is logged and baseline deviations trigger alerts

The scope boundary distinguishes AI cybersecurity audits from AI ethics or fairness reviews. An AI cybersecurity audit addresses confidentiality, integrity, and availability of AI assets — not algorithmic bias or disparate impact, which fall under separate regulatory regimes.

How it works

AI cybersecurity audits follow the same phase structure as broader cybersecurity audit process phases, but with modified evidence collection methods and specialist assessment techniques.

Phase 1 — Scoping and asset inventory. Auditors identify all AI and ML components in scope: production models, shadow models, training environments, feature stores, and serving infrastructure. The cybersecurity audit scope definition process must explicitly enumerate AI assets, as they are routinely omitted from standard IT asset registries.

Phase 2 — Threat modeling for AI-specific vectors. The MITRE ATLAS framework (MITRE Adversarial Threat Landscape for Artificial-Intelligence Systems) catalogs AI-specific adversarial tactics and techniques in a format parallel to MITRE ATT&CK. Auditors use ATLAS to structure threat scenarios against the systems under review.

Phase 3 — Technical testing. This includes:
- Adversarial robustness testing (evaluating model behavior under perturbed inputs)
- Data poisoning simulation (assessing whether training pipelines validate data integrity)
- Model extraction probing (testing whether API query patterns expose model parameters)
- Prompt injection assessment (for large language model deployments)

Phase 4 — Evidence collection and control evaluation. Auditors examine logging configurations, access control policies for model repositories, encryption of model artifacts at rest and in transit, and incident response procedures specific to AI failures. Cybersecurity audit evidence collection for AI systems often requires reviewing MLOps pipeline configurations, not just network or endpoint controls.

Phase 5 — Reporting and remediation tracking. Findings are classified by severity and mapped to control gaps. The cybersecurity audit report structure for AI audits should include a dedicated section for AI-specific risks distinct from general infrastructure findings.

Common scenarios

Financial services — model risk and adversarial fraud. Banks and payment processors deploying ML-based fraud detection face adversarial evasion attacks where fraudsters systematically craft transactions to avoid model detection. The Office of the Comptroller of the Currency (OCC) and the Federal Reserve have issued guidance on model risk management (SR 11-7 / OCC 2011-12) that auditors reference when evaluating governance of AI models in regulated financial institutions. See cybersecurity audit financial services for sector-specific framing.

Healthcare — AI-assisted diagnostics and data integrity. AI systems processing protected health information (PHI) are subject to HIPAA Security Rule requirements independent of their AI nature. Training data that includes PHI must be audited for access controls and de-identification rigor. Adversarial perturbations to medical imaging models represent a patient safety and integrity risk that hipaa cybersecurity audit frameworks are beginning to address.

Government and critical infrastructure — model supply chain attacks. Federal agencies using AI are subject to Executive Order 14110 (October 2023), which directed NIST, CISA, and other agencies to develop AI safety and security guidance. CISA's cross-sector guidance on AI cybersecurity distinguishes between attacks on AI systems and attacks using AI — both are in scope for cybersecurity audit government agencies.

Large language models (LLMs) — prompt injection and data exfiltration. LLM deployments face prompt injection as a primary attack vector, where adversarial input manipulates model output to bypass access controls or expose confidential context. OWASP's LLM Top 10 (published by the Open Web Application Security Project) provides a structured taxonomy auditors use to assess LLM-specific controls.

Decision boundaries

AI cybersecurity audits differ from related service categories along three axes:

Dimension AI Cybersecurity Audit Penetration Test AI Ethics Review
Primary focus Control assurance, model integrity, access governance Exploitation of vulnerabilities Fairness, bias, transparency
NIST reference AI RMF + SP 800-53 SP 800-115 AI RMF (Trustworthy AI)
Output Audit findings against controls Proof-of-concept exploits Risk assessments for bias/harm
Regulatory driver HIPAA, FedRAMP, EO 14110, OCC SR 11-7 Scope-dependent EU AI Act, EEOC guidance

The distinction between an AI cybersecurity audit and a conventional cybersecurity audit vs penetration testing engagement is significant: penetration testing of AI systems may be a component of a broader AI audit, but it does not constitute the full audit. Similarly, an [AI ethics review] addresses different regulatory obligations than a cybersecurity control assessment.

Auditors qualified to conduct AI cybersecurity audits typically hold credentials such as CISA (Certified Information Systems Auditor, issued by ISACA) alongside demonstrated competency in ML systems — a combination that remains uncommon. See cybersecurity auditor qualifications for the broader credentialing landscape.

Organizations in federally regulated sectors must align AI audit findings to existing compliance frameworks. FedRAMP cybersecurity audit requirements, for instance, now require authorization documentation to address AI component risk where AI is part of a cloud service offering.

References

In the network

Network