Organizations that create AI products can use SAIF to develop more secure AI products and mitigate these risks during the development process. Organizations that use AI products can also use SAIF to understand - and address - the potential risks of using AI.
The SAIF Map, a new paradigm and shared vocabulary to navigate the landscape of AI risks and controls
An interactive SAIF Risk Self Assessment to understand your organization’s AI risks, regardless of organizational size, sector, or current security posture
The SAIF Secure AI Development Primer, which explains the AI development process through the lens of security risks
Resources containing In-depth technical information about securing different parts of AI systems
SAIF is intended to guide any organization involved in creating AI models, products, or features. Leadership can turn to SAIF to understand the broad landscape of risks that AI introduces to their organization and learn about the controls that can address those risks. Technical practitioners, such as security specialists or AI practitioners, can use SAIF as a technical resource for guidance on how to secure specific AI systems throughout the AI development lifecycle.
Incorporating a framework like SAIF helps an organization identify and mitigate risks specific to AI systems and potentially meet forthcoming standards, such as NIST’s AI Risk Management Framework (AI RMF) and Secure Software Development Framework (SSDF).
Introduced in 2023, SAIF is an externalization of Google’s own internal framework for securing its production and use of AI.
It leverages Google’s unique position in the field:
SAIF is a distillation of lessons learned in both areas, combined to bring comprehensive AI security knowledge to the wider industry.
The potential of AI, especially generative AI, is immense. AI is rapidly becoming integrated into aspects of our lives that involve sensitive data and impact critical decisions, from cancer diagnoses to customer service interactions. With AI innovation, though, the industry needs security standards for building and deploying AI responsibly for effective risk management.
Yet, the novel risks introduced by AI are not yet widely known by technical practitioners, and traditional software security measures may not address the new dimensions that AI introduces into technical development. The breakneck pace of AI advancements means that failing to prioritize security from the outset will result in costly and complex remediation efforts down the line. A framework is needed to safeguard the technology that supports AI advancements, so that when AI models are implemented, they’re secure-by-default.
Beyond security, building AI responsibly requires consideration of important challenges that must be addressed thoughtfully and affirmatively. SAIF is just one part of a holistic approach to responsible AI development. Google's responsible AI practices also address other aspects of building AI responsibly, such as fairness, interpretability, privacy, and safety.
