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Free Template · AI Security

AI Security AuditChecklist

A structured audit of your AI security posture across 7 control domains, infrastructure, data, model, access, logging, supply chain, and governance.

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0 domains
full security coverage
0+ controls
auditable checkpoints
0 frameworks
NIST, ISO 42001, EU AI Act
Free
to use and customise

Why an AI Security Audit is Different

Traditional application security audits miss the attack surfaces AI systems introduce: prompt injection, training data poisoning, model extraction, and data-in-prompt leakage. A standard SOC 2 report tells you almost nothing about whether an LLM endpoint will leak your customer data when prompted adversarially. An AI-specific audit closes that gap.

85+
controls across 7 AI-specific domains
Covers infrastructure, data, model, access, logging, supply chain, and governance, with adversarial resilience checks traditional audits lack.
€35M
maximum EU AI Act fine
High-risk AI systems failing Article 15 cybersecurity requirements face material penalties; the audit produces evidence the regulator expects.
Aug 2026
EU AI Act high-risk deadline
High-risk system obligations become enforceable in August 2026. Organisations need documented security audits in place before that date.
Continuous
monitoring supplements the audit
A point-in-time audit is a baseline; high-risk systems require continuous post-market monitoring per EU AI Act Article 61.

The Audit Checklist

Work through each domain systematically. Mark controls Pass / Fail / N/A with supporting evidence. Record risk level for each failed control.

Assess the network, compute, and cloud layer hosting AI workloads. Infrastructure gaps are the most frequent finding in enterprise AI audits.

Network segmentation in place
AI systems are isolated from general corporate networks; training and inference traffic traverses dedicated segments with explicit firewall rules.
API gateway and WAF deployed
All AI inference endpoints sit behind an authenticated API gateway with WAF protection, rate limiting, and DDoS mitigation.
Compute environment hardened
GPU/TPU instances follow a hardened baseline; container images are scanned on build; Kubernetes RBAC is enforced; no hardcoded credentials.
Secrets management enforced
Model weights, API keys, and training credentials are stored in a KMS or secret vault, never in code, container layers, or environment files checked into source control.
Cloud IAM follows least privilege
Cloud roles assigned to AI workloads are scoped to the minimum permissions required; training data buckets are private; CSPM monitoring is active.
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How to Run the Audit

Five steps to produce an audit that holds up under regulator and internal-audit scrutiny, not a checklist artefact filed in a drawer.

1
Define the audit scope and trigger
Identify which AI systems are in scope (production models, vendor AI services, training pipelines) and record the audit trigger, initial, annual, pre-deployment, incident-driven, or regulator-driven.
2
Collect evidence before scoring
Gather configuration exports, access reviews, pen-test reports, incident logs, model cards, and DPIAs. Scoring from memory produces false-positives that erode the audit's credibility with stakeholders.
3
Work through the 85+ controls
Mark each control Pass / Fail / N/A with supporting evidence. Where a control fails, record the risk level (Critical / High / Medium / Low) so findings can be prioritised downstream.
4
Roll up scores and apply the risk rating
Calculate section scores and an overall percentage. Map to the risk-rating band: 90–100% Low, 75–89% Moderate, 60–74% High, below 60% Critical. Critical findings may justify pausing the AI system until remediation lands.
5
Assign remediation and re-audit on closure
Every Fail gets an owner, severity, due date, and status. Re-audit each failed control once remediation lands, do not wait for the next annual audit to confirm closure.
FAQ

Frequently Asked Questions

An AI security audit evaluates the security posture of AI systems across infrastructure (network, compute, cloud), data (at rest, in transit, lifecycle), model integrity and adversarial resilience, access control, logging and monitoring, supply chain (third-party models, datasets, libraries), and compliance/governance. Unlike traditional application security audits, AI-specific audits must cover prompt injection resistance, training data integrity, model extraction defences, and data-in-prompt leakage.

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Free .docx checklist with 85+ controls across 7 domains. Customise to your org and start auditing.

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From Audit Snapshot to Continuous AI Security

A one-off audit catches the gaps you know about today. Aona continuously discovers shadow AI, detects sensitive data flowing to AI tools, and keeps your audit evidence current, so the next audit becomes a review, not an archaeology expedition.

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Aona AI PlatformAI Governance FrameworkAI Risk Assessment ChecklistAI Vendor Security QuestionnaireThird-Party AI Risk AssessmentAll Templates