Google’s Secure AI Framework (SAIF) is Google’s flagship, end‑to‑end security framework for building, deploying, and operating AI systems securely across the entire lifecycle. It covers risks from data to infrastructure, models, applications, and even advanced AI agents. Below is a clear, structured explanation based entirely on authoritative Google sources.

What Google SAIF Is

Google’s Secure AI Framework (SAIF) is a conceptual, lifecycle‑wide security framework designed to help organisations secure AI systems—from training data to deployed applications and agentic AI. It is Google’s response to the rapidly growing risks in AI, including:

• Prompt injection

• Data poisoning

• Model theft

• Rogue agent actions

• Supply‑chain compromise

• Privacy leakage

• Misuse of generative AI

SAIF is intended to be industry‑wide, not Google‑specific, and is shared openly with the Coalition for Secure AI (CoSAI).

Core Structure of SAIF

SAIF has six core elements, each addressing a major dimension of AI security.

1. Expand strong security foundations to the AI ecosystem

• Apply secure‑by‑default infrastructure to AI systems

• Harden data pipelines, training environments, and model storage

• Adapt classic mitigations (e.g., input sanitisation) to AI threats like prompt injection

2. Extend detection & response to AI

• Monitor AI inputs/outputs for anomalies

• Integrate AI systems into SOC workflows

• Use threat intelligence to anticipate AI‑specific attacks

3. Automate defenses

• Use AI to enhance detection and response

• Automate guardrails, anomaly detection, and policy enforcement

4. Harmonize platform‑level controls

• Ensure consistent security across cloud, on‑prem, and hybrid AI platforms

• Apply unified IAM, network controls, and policy enforcement

5. Adapt controls with fast feedback loops

• Continuously test and red‑team AI systems

• Update training data and fine‑tune models based on incidents

• Reinforcement learning for improved resilience

6. Contextualize AI risks in business processes

• Align AI risks with organisational governance

• Integrate AI risk into compliance, privacy, and operational processes

The SAIF Risk Map

One of SAIF’s most important contributions is the SAIF Map, a visual guide showing:

• Where risks appear across the AI lifecycle

• Which controls apply at each stage

• How risks differ for Model Creators vs Model Consumers

The map is divided into four component areas:
Data | Infrastructure | Model | Application.
It also includes a specialised diagram for AI agents, covering risks like rogue actions and unsafe tool use.

SAIF 2.0: Secure Agents

Google’s latest update focuses on agentic AI, introducing:

• Agent‑specific threat models

• Controls for tool use, planning, and autonomous actions

• Guardrails for preventing unsafe or unintended behaviour

This is critical as AI agents become more common in enterprise workflows.

What SAIF Helps Organisations Do

SAIF enables organisations to:

• Build secure‑by‑design AI systems

• Protect training data and model integrity

• Prevent data exfiltration and model theft

• Detect misuse or anomalous AI behaviour

• Govern AI systems in line with emerging regulations

• Integrate AI into existing cybersecurity programs

Google also provides a SAIF Risk Self‑Assessment tool to help organisations identify their AI risk posture.