Artificial Intelligence (AI) is evolving at an unprecedented pace, necessitating robust risk management strategies. To address this need, Google has introduced the Secure AI Framework (SAIF), a comprehensive approach to securing AI systems. This framework comprises six core elements designed to enhance security across the AI ecosystem.
1. Strengthening Security Foundations
Google leverages over two decades of expertise in secure-by-default infrastructure protections to safeguard AI systems, applications, and users. The framework advocates for the development of organisational expertise to keep pace with AI advancements and adapt infrastructure protections to evolving threat models. For instance, traditional injection techniques like SQL injection can be mitigated using input sanitisation and limiting, thereby defending against prompt injection attacks.
2. Extending Detection and Response
To effectively manage AI-related cyber incidents, organisations must extend their threat intelligence and detection capabilities. This involves monitoring inputs and outputs of generative AI systems to detect anomalies and using threat intelligence to anticipate potential attacks. Collaboration with trust and safety, threat intelligence, and counter-abuse teams is crucial to enhancing the organisation’s ability to respond to AI-specific threats.
3. Automating Defences
Given the likelihood of adversaries using AI to scale their impact, it is essential to harness AI innovations to improve the scale and speed of response efforts to security incidents. Automation in defence mechanisms ensures that organisations can stay agile and cost-effective in protecting against both existing and emerging threats.
4. Harmonising Platform-Level Controls
Consistency in security across various platforms and tools is vital. The framework recommends aligning control frameworks to support AI risk mitigation and scaling protections across different platforms. At Google, this includes extending secure-by-default protections to AI platforms like Vertex AI and Security AI Workbench. By integrating controls and protections into the software development lifecycle, the entire organisation can benefit from state-of-the-art protections.
5. Adapting Controls for Continuous ImprovementContinuous Improvement encourages small, incremental changes to the current process, avoiding the disruptions that larger changes can cause. This approach facilitates continuous improvement over time.
Continuous learning and adaptation are key to effective AI security. This involves regularly testing implementations, evolving detection and protection mechanisms, and employing techniques like reinforcement learning based on incidents and user feedback. Organisations should update training datasets, fine-tune models to respond strategically to attacks, and incorporate additional security measures. Regular Red Team exercises can also enhance safety assurance for AI-powered products and capabilities.
6. Contextualising AI System Risks
End-to-end risk assessments are essential for understanding how AI will be deployed within an organisation. This includes evaluating business risks such as data lineage, validation, and operational behaviour monitoring for specific applications. Automated checks to validate AI performance ensure that the deployment of AI is both secure and reliable.
Summary
Google’s Secure AI Framework provides a robust structure for managing the security of AI systems. By focusing on strong security foundations, extending detection and response capabilities, automating defences, harmonising platform-level controls, adapting controls for continuous improvement, and contextualising AI system risks, organisations can effectively mitigate risks associated with AI deployment. This comprehensive approach ensures that AI advancements are met with equally advanced security measures, paving the way for safer and more reliable AI integration in various industries.