What is AI Security Audit?
AI Security Audit is a comprehensive, structured assessment of an AI system's security posture, examining its architecture, data handling, access controls, model integrity, deployment environment, and operational processes to identify vulnerabilities and verify compliance with security standards and regulations.
What is an AI Security Audit?
An AI Security Audit is a thorough evaluation of the security of an AI system across its entire lifecycle, from data collection and model training to deployment and ongoing operation. It combines traditional cybersecurity audit practices with AI-specific assessments that address the unique risks of machine learning systems.
Think of it as a comprehensive health examination for your AI system's security. Just as a financial audit examines an organisation's books to verify accuracy and compliance, an AI security audit examines your AI systems to identify vulnerabilities, verify that security controls are working, and ensure compliance with relevant standards and regulations.
Why AI Security Audits Matter
AI systems introduce security risks that traditional IT audits are not designed to assess. These include risks related to training data integrity, model behaviour, adversarial attacks, and the complex software supply chains that AI systems depend on. Without specific AI security audits, these risks go unexamined and unmanaged.
For organisations in Southeast Asia, the regulatory case for AI security audits is strengthening. Singapore's Model AI Governance Framework recommends regular assessment of AI systems. Data protection laws across ASEAN require organisations to implement appropriate security measures for systems that process personal data. An AI security audit provides documented evidence that you are meeting these obligations.
What an AI Security Audit Covers
Data Security
The audit examines how training data and operational data are collected, stored, processed, and protected. This includes evaluating encryption at rest and in transit, access controls on data stores, data lineage and provenance documentation, compliance with data protection regulations, and procedures for handling data breaches.
Model Security
The audit assesses the security of the AI model itself. This includes evaluating model architecture for known vulnerabilities, testing resilience against adversarial inputs, assessing resistance to model extraction attempts, reviewing model versioning and integrity verification, and evaluating the security of model storage and distribution.
Infrastructure Security
AI systems run on infrastructure that must be secured. The audit examines cloud platform security configurations, network segmentation and access controls, container and orchestration security, GPU and compute resource access management, and backup and disaster recovery procedures.
Application Security
For AI systems that interact with users or other systems, the audit evaluates API security and authentication, input validation and sanitisation, output filtering and safety controls, session management and rate limiting, and integration security with upstream and downstream systems.
Operational Security
The audit reviews the processes and procedures that govern AI system operations. This includes deployment pipeline security, change management procedures, incident response readiness, monitoring and alerting capabilities, and documentation and audit trail completeness.
Supply Chain Security
The audit assesses the security of third-party components used in the AI system. This includes pre-trained models and their provenance, open-source libraries and their vulnerability status, cloud services and their security configurations, and data sources and their integrity.
Conducting an AI Security Audit
Phase 1: Scoping and Planning
Define the scope of the audit, including which AI systems will be assessed, what aspects of security will be examined, and what standards or frameworks will be used as benchmarks. Common frameworks include Singapore's AI Verify, the NIST AI Risk Management Framework, and ISO 27001 adapted for AI systems.
Phase 2: Documentation Review
Examine existing documentation including system architecture diagrams, data flow maps, security policies, access control configurations, and incident response plans. This reveals the intended security posture and identifies gaps in documentation.
Phase 3: Technical Assessment
Conduct hands-on testing of the AI system's security controls. This includes vulnerability scanning, penetration testing, adversarial testing of the AI model, access control verification, and configuration review of infrastructure components.
Phase 4: Process Review
Evaluate the organisational processes that support AI security. This includes interviewing team members, reviewing change management records, verifying incident response procedures, and assessing security awareness among AI team members.
Phase 5: Reporting and Remediation
Compile findings into a clear report that classifies vulnerabilities by severity, provides specific remediation recommendations, and establishes a timeline for addressing identified issues. Track remediation to completion and verify that fixes are effective.
Audit Frequency and Triggers
Conduct comprehensive AI security audits at least annually for production systems. Additionally, trigger audits when deploying new AI systems to production, after significant architecture or model changes, following a security incident, when regulatory requirements change, and before expanding AI systems into new markets or use cases.
Building Internal Audit Capability
While external auditors bring independence and specialised expertise, building some internal audit capability provides ongoing visibility into your AI security posture. This includes training security team members on AI-specific risks, developing internal audit checklists and procedures, implementing automated security scanning for AI systems, and establishing continuous monitoring baselines.
Regional Audit Standards
Singapore's AI Verify provides a practical testing framework that can serve as a foundation for AI security audits in the region. The framework covers key governance principles including transparency, fairness, and safety, and provides standardised testing methodologies. For organisations operating across ASEAN, using AI Verify as a baseline provides a regionally recognised benchmark for AI security assessment.
AI Security Audits provide the assurance that your AI systems are protected against the threats that can cause the most damage. Without regular audits, vulnerabilities accumulate, controls degrade, and your risk exposure grows invisibly until an incident occurs.
For business leaders in Southeast Asia, AI security audits serve multiple purposes. They protect against financial losses from security breaches. They demonstrate regulatory compliance to authorities across ASEAN. They build confidence among customers and partners that your AI systems are trustworthy. And they identify areas where security investment will have the greatest impact.
The cost of a comprehensive AI security audit is a fraction of the potential cost of a serious AI security incident. Organisations that audit regularly catch and fix vulnerabilities proactively, maintain a stronger security posture, and can demonstrate their diligence to regulators and stakeholders when asked.
- Scope your AI security audits to cover data, model, infrastructure, application, operational, and supply chain security.
- Use established frameworks like Singapore's AI Verify or the NIST AI Risk Management Framework as benchmarks for your audits.
- Conduct comprehensive audits at least annually and trigger additional audits after significant changes or security incidents.
- Combine external audit expertise for independence and specialisation with internal capability for ongoing monitoring.
- Classify findings by severity and establish clear remediation timelines with accountability for completion.
- Include adversarial testing of AI models as a standard component of security audits, not just traditional IT security testing.
- Document audit findings and remediation actions thoroughly to support regulatory compliance across your operating markets.
Frequently Asked Questions
How is an AI security audit different from a regular IT security audit?
A regular IT security audit focuses on traditional infrastructure, applications, and network security. An AI security audit covers these areas plus AI-specific risks including training data integrity, model behaviour and robustness, adversarial attack resilience, AI supply chain security, and the unique challenges of securing machine learning pipelines. AI security audits require auditors with expertise in both cybersecurity and machine learning, which is a more specialised skill set than traditional IT auditing.
Should we use internal or external auditors for AI security?
Both have roles to play. External auditors bring independence, specialised expertise, and credibility with regulators and stakeholders. Internal teams provide continuous monitoring, faster response, and deeper institutional knowledge. A strong approach is to use external auditors for comprehensive annual assessments and regulatory compliance, while building internal capability for ongoing monitoring and interim reviews.
More Questions
Singapore's AI Verify is the most directly relevant regional framework and provides practical testing methodologies. The NIST AI Risk Management Framework offers a comprehensive approach widely adopted internationally. ISO 27001, adapted for AI systems, provides a recognised standard for information security management. For organisations operating across ASEAN, using a combination of AI Verify and an international standard provides both regional relevance and global credibility.
Need help implementing AI Security Audit?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how ai security audit fits into your AI roadmap.