SAIF

AI security depends on conventional foundations such as access control and software integrity, but also on risks introduced by learned behavior and model interaction. SAIF describes a security perspective across the AI development and deployment process, connecting risks such as poisoning, prompt injection and model exfiltration to relevant components and mitigations. Its maps help a practitioner reason about where a control belongs and which roles can implement it. The framework is guidance rather than a product that secures a system automatically, and it must be interpreted alongside the organization's architecture, responsibility boundaries and existing security practices.

The practitioner inventories AI assets and maps their flow from development inputs to deployed applications. They use SAIF's risk and control descriptions to identify missing protections and assign implementation responsibility. A useful artifact links each selected control to an asset, threat and verification method, including model or data integrity checks and restricted production access where relevant. The review should involve teams operating infrastructure as well as those building model features. Controls are reassessed when the organization adds external models, changes data sources or grants agents new capabilities.

A framework mapping is only as accurate as the asset inventory and architecture behind it. Broad statements such as secure AI can conceal untested assumptions about vendors or operational access. SAIF does not certify a model's behavior or settle legal compliance. The practitioner should distinguish recommended controls from implemented ones and verify effectiveness against concrete threats, while retaining evidence of gaps the organization has chosen to accept.