Artificial intelligence is transforming how organisations operate, make decisions and deliver services. However, AI systems introduce unique security and compliance challenges that traditional cyber security frameworks do not fully address. From adversarial attacks on machine learning models to privacy concerns in AI training data, organisations must develop new capabilities to manage AI-related risks effectively.
The AI Risk Landscape
AI systems face a distinct set of threats that differ from traditional software. These include adversarial inputs designed to cause misclassification, training data poisoning that corrupts model behaviour, model extraction attacks that steal proprietary algorithms, privacy attacks that extract sensitive training data, supply chain risks in ML pipelines and frameworks, bias and fairness issues that lead to discriminatory outcomes and hallucination risks where AI generates plausible but false information.
Security Challenges in AI Systems
Unlike traditional software where behaviour is deterministic and testable, AI systems learn from data and can behave unpredictably. This creates challenges for security testing, validation and monitoring. Models can be manipulated through carefully crafted inputs, their decision-making processes are often opaque and they can degrade in performance over time as the data they encounter diverges from training data.
Governance and Oversight
Effective AI governance requires clear accountability structures including an AI ethics board or governance committee, defined roles for AI risk management, model documentation and inventory, impact assessment processes for new AI deployments and monitoring and audit mechanisms for deployed models.
Our consultancy service can help you establish an AI governance framework tailored to your organisation's needs and regulatory requirements.
Regulatory Landscape
AI regulation is evolving rapidly. The EU AI Act establishes a comprehensive regulatory framework with risk-based classifications. Singapore's Model AI Governance Framework provides practical guidance for responsible AI deployment. The US has issued executive orders and sector-specific guidance. Organisations must track and adapt to these evolving requirements to maintain compliance.
Privacy and Data Protection
AI systems often process large volumes of data, including personal data. This creates obligations under GDPR, PDPA and other privacy laws. Key considerations include establishing a lawful basis for AI training data processing, conducting Data Protection Impact Assessments for AI systems, implementing data minimisation and purpose limitation, addressing the right to explanation for automated decisions and managing data subject rights in the context of trained models.
Our Data Protection Manager helps organisations manage the intersection of AI and privacy compliance effectively.
Technical Security Measures
Protecting AI systems requires both traditional security measures and AI-specific controls including secure ML development pipelines, input validation and adversarial robustness testing, model access controls and API security, output monitoring and anomaly detection, training data integrity verification, model versioning and rollback capabilities and privacy-preserving techniques (federated learning, differential privacy).
Testing and Validation
AI systems require specialised testing approaches including adversarial testing (red teaming AI systems), bias and fairness testing, performance validation on diverse datasets, security testing of AI APIs and interfaces, robustness testing under edge cases and stress testing for reliability and availability. Regular penetration testing should include AI systems and their APIs in scope.
Monitoring Deployed Models
Once deployed, AI models must be continuously monitored for performance degradation (model drift), unexpected or biased outputs, security anomalies indicating attacks, data quality issues in input streams and compliance with governance policies. Implement automated alerting and human review processes to catch issues before they cause harm.
Building an AI Security Programme
- Inventory all AI systems including third-party AI services
- Classify AI systems by risk level (following EU AI Act or similar framework)
- Conduct risk assessments for each AI system
- Implement appropriate technical and organisational controls
- Establish monitoring and incident response for AI systems
- Train personnel on AI security risks and responsibilities
- Regularly review and update your AI security programme
Conclusion
AI security and compliance is a rapidly evolving field that requires proactive attention from organisations deploying or developing AI systems. By establishing robust governance, implementing appropriate technical controls and staying abreast of regulatory developments, organisations can harness the benefits of AI while managing its unique risks. Integrate AI security into your broader information security management system to ensure comprehensive coverage.