Google — SAIF (Secure AI Framework): a practitioner’s map
• Category: Security
- SAIF is positioned as a practitioner’s guide for building AI securely and responsibly, grounded in Google’s defensive experience.
- Its key value: it treats AI security as “real controls for real systems,” not a separate discipline with brand new fundamentals.
- It encourages approaching AI development through a security lens and enumerating risks + the controls that address them.
- For teams shipping LLM apps/agents, SAIF is a good forcing function: define boundaries, identity, data access, monitoring, and response.
- Use it as a checklist for architecture reviews: what are we defending, against whom, and what telemetry proves it works?
- Don’t overfit to “model threats” — SAIF naturally extends to connectors, RAG pipelines, and tool execution (where most incidents happen).
Why it matters
AI systems collapse multiple trust boundaries into one workflow: user input → retrieval → model output → action. Frameworks like SAIF help teams apply disciplined security thinking before they grant broad permissions to “helpful automation.”
What to do
- Run a SAIF-style review per AI feature: assets, threats, controls, and evidence (logs/evals) — write it down.
- Keep tool access least-privilege: scope retrieval, require approvals for state-changing actions, and sandbox execution.
- Instrument everything: prompt/tool calls, retrieval sources, and user identity — so you can investigate abuse.
- Adopt release gates: security evals (prompt injection, leakage, tool misuse) and rollback plans.