Framework
Google Secure AI Framework (SAIF)
A conceptual framework from Google for securing AI systems across their lifecycle, built on six core elements that extend familiar security principles into the AI domain.
Google’s Secure AI Framework (SAIF) provides a practical, principle-based approach to AI security grounded in Google’s experience deploying AI at scale. It is designed to be adopted incrementally and to complement existing security programmes.
The six elements below follow the SAIF structure. Wording and framing may be refined by Google over time; the official SAIF material remains the primary source.
Six core elements
| Element | Principle | Practical implication |
|---|---|---|
| 1. Expand strong security foundations to the AI ecosystem | Apply existing security controls to AI: access management, data protection, network security | Extend what already works rather than reinvent security |
| 2. Extend detection and response to AI | Incorporate AI-specific threats into detection engineering and incident response playbooks | Prompt injection, model exfiltration and poisoned training data need dedicated detection patterns |
| 3. Automate defences to keep pace with attacks | Use AI itself to scale threat detection, response and remediation | AI-assisted security operations for AI-powered threats |
| 4. Harmonise platform-level controls | Centralise security policy and enforcement across AI tools, frameworks and deployment environments | Consistent controls across managed platforms, custom deployments and third-party models |
| 5. Adapt controls to adjust mitigations | Continuously tune AI-specific controls based on observed threats and changing model behaviour | Feedback loops from incidents to control improvement |
| 6. Contextualise AI system risks in surrounding business processes | Evaluate AI risk within the broader enterprise risk framework, not as a standalone concern | AI security decisions informed by business impact, data classification and regulatory obligations |
ATLAS connection
SAIF’s six elements align with ATLAS controls: strong foundations map to SSO/MFA and approved tool registers; detection and response connect to logging, monitoring and the incident response pipeline; platform-level controls correspond to standardised tool access; and contextualisation reinforces the ATLAS principle that AI risk is operational risk, not a separate domain.
Official source: Google