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Google SAIF

ScopeSecure AI Framework
Use ForOrganizational AI security posture
Linksafety.google

Google's Secure AI Framework (SAIF) provides a comprehensive organizational framework for securing AI systems across their entire lifecycle, from development through deployment and operations. Unlike technique-focused frameworks that catalog individual attacks, SAIF addresses the governance, process, and architectural controls that organizations need to build and maintain secure AI systems at scale. For AI red teamers, SAIF is valuable as a benchmark for evaluating an organization's security maturity rather than testing individual models. It defines what "good" looks like at the organizational level: secure development practices, supply chain integrity, monitoring and incident response for AI systems, and risk management aligned with existing enterprise security programs. SAIF complements the technical frameworks (ATLAS, F.O.R.G.E., OWASP) by providing the organizational wrapper that ensures those technical controls are implemented consistently, monitored effectively, and maintained over time.

Key Components​

  • Secure development foundations define security requirements for the AI development lifecycle, including secure training data handling, model validation, and safe deployment practices that reduce risk before models reach production.
  • AI supply chain security addresses the integrity of models, datasets, frameworks, and infrastructure components, covering provenance verification, dependency scanning, and third-party risk assessment for AI-specific assets.
  • Runtime monitoring and detection establishes requirements for observability in AI systems, including anomaly detection on model inputs and outputs, drift monitoring, and incident response procedures for AI-specific attacks.
  • Risk assessment methodology provides a structured approach to evaluating AI-specific risks within existing enterprise risk management frameworks, ensuring AI threats are assessed with the same rigor as traditional cyber risks.
  • Cross-functional governance model defines roles, responsibilities, and processes for AI security across development, security, compliance, and business teams, recognizing that AI security requires collaboration beyond the traditional security team.