Comply · Global standards & industry frameworks
Google Secure AI Framework
Google's Secure AI Framework.
Google's Secure AI Framework (SAIF) is a conceptual framework for securing AI systems, built on six core elements: expand strong security foundations to the AI ecosystem, extend detection and response to AI, automate defenses, harmonize platform controls, adapt controls with faster feedback loops, and contextualize AI-system risks.
Who it applies to
Organizations building or operating AI systems that want a security-first operating model.
Key obligations
- Extend security foundations to AI systems
- Detect and respond to AI-specific threats
- Automate and harmonize AI security controls
- Continuously adapt controls and contextualize risk
How Maetra maps agents to Google SAIF
Maetra's Secure and Audit modules extend detection, response, and control evidence to AI agents in line with SAIF's security-first elements.
In practice, Maetra:
- Scans and fingerprints each agent. Discover reads the agent’s code — its tools, data access and sensitivity, actions, model, and environment — into an evidence-backed profile tied to the exact file and commit.
- Decides what applies. That profile determines whether Google SAIF is in scope for the agent and which of its requirements apply.
- Auto-detects controls and surfaces gaps. Controls your code already satisfies are detected automatically from the scan; the rest become a clear list of gaps, each tied to the requirement and the evidence it still needs.
- Proves it and keeps it current. Close gaps with linked evidence or generated documents — reused across every framework the same control supports — and Maetra re-checks on each rescan and flags evidence that has gone stale.
Related frameworks
Prove Google SAIF compliance with Maetra
Classify your AI agents once and Maetra maps them to Google SAIF and every other framework it supports — generating the evidence and documentation, tracking gaps and deadlines, and sealing every decision in an immutable audit trail.