Google Secure AI Framework

SAIF draws inspiration from software development, such as evaluating, testing, and managing the supply chain

To safeguard AI systems, apps, and users, this involves utilizing secure-by-default infrastructure safeguards

This involves employing threat intelligence to foresee assaults and keeping an eye on the inputs and outputs of generative AI systems

The scope and velocity of security incident response activities can be enhanced by the most recent advancements in AI

To guarantee that all AI applications have access to the finest protections in a scalable and economical way can mitigate AI risk

Continuous learning and testing of implementations can guarantee that detection and prevention capabilities adapt to the ever-changing threat landscape

Last but not least, completing end-to-end risk assessments on an organization’s AI deployment can aid in decision-making