Its main goal is to tackle the unique vulnerabilities that come with AI technologies such as attacks that tamper with training data, trick models through engineered prompts, or steal sensitive information. SAIF draws on Google’s own experiences developing and deploying large scale AI systems and therefore is more engineering-heavy than other frameworks. SAIF is largely focused on helping organizations make their AI systems more resistant to cyberattacks and cyber adversaries and covers areas like data handling, underlying infrastructure, the AI models themselves, user-facing applications and verification processes. It offers organizations practical guidance on implementation controls, shared responsibility, and defending against technical attacks.
Technology consultancy Thoughtworks has assessed SAIF as a framework that helps organizations systematically address “common threats such as data poisoning and prompt injection through a clear risk map, component analysis, and practical mitigation strategies.” According to the firm, SAIF’s “focus on the evolving risks of building agentic systems especially timely and valuable. SAIF offers a concise, actionable playbook that teams can use to strengthen security practices for LLM usage and AI-driven applications.”
David Brumley, chief AI and science officer at Bugcrowd, says that for organizations that want to adopt a framework, the question is not really “which AI risk framework is best?” but “which framework helps [the] organization safely build, deploy, and learn from AI in the real world?”
