{"id":501,"date":"2026-06-20T00:00:00","date_gmt":"2026-06-19T23:00:00","guid":{"rendered":"https:\/\/kosokoking.com\/?p=501"},"modified":"2026-06-10T20:16:37","modified_gmt":"2026-06-10T19:16:37","slug":"googles-secure-ai-framework-saif","status":"publish","type":"post","link":"https:\/\/kosokoking.com\/index.php\/technology\/googles-secure-ai-framework-saif\/","title":{"rendered":"Google&#8217;s Secure AI Framework (SAIF)"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">The <a href=\"https:\/\/owasp.org\/www-project-machine-learning-security-top-10\/\" title=\"\">OWASP ML Top 10<\/a> and <a href=\"https:\/\/owasp.org\/www-project-top-10-for-large-language-model-applications\/\" title=\"\">LLM Top 10<\/a> are vulnerability checklists. They name specific risks and rank them. <a href=\"https:\/\/saif.google\" title=\"\">Google&#8217;s Secure AI Framework (SAIF)<\/a> does something different. It maps the entire AI application lifecycle, from data ingestion through model deployment to agent orchestration, and then overlays risks, responsible parties, and controls onto that map. Where OWASP gives you a ranked list of what can go wrong, SAIF gives you a structural model of where things go wrong, who is responsible for fixing them, and what controls exist at each point.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">SAIF was first published in June 2023. The framework was substantially expanded to version 2.0 in early 2026 to cover agentic AI systems. In September 2025, Google donated the SAIF data to the <a href=\"https:\/\/www.coalitionforsecureai.org\" title=\"\">Coalition for Secure AI (CoSAI)<\/a>, an OASIS Open project with 35 industry partners, making the Risk Map available as an open industry resource.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">SAIF vs OWASP<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The two frameworks serve different purposes and work best together.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th><\/th><th>OWASP ML\/LLM Top 10<\/th><th>Google SAIF<\/th><\/tr><\/thead><tbody><tr><td>Format<\/td><td>Ranked vulnerability list<\/td><td>Lifecycle risk map with components, risks, and controls<\/td><\/tr><tr><td>Scope<\/td><td>Specific vulnerability classes<\/td><td>Entire AI development pipeline<\/td><\/tr><tr><td>Responsibility model<\/td><td>No explicit ownership assignment<\/td><td>Maps each control to model creator, model consumer, or both<\/td><\/tr><tr><td>Risk coverage<\/td><td>10 risks per list (ML and LLM separately)<\/td><td>15 risks covering both ML and LLM in a single framework<\/td><\/tr><tr><td>Control mapping<\/td><td>General mitigations described per risk<\/td><td>Named controls mapped to specific risks with explicit ownership<\/td><\/tr><tr><td>Governance<\/td><td>Not covered<\/td><td>Assurance and governance controls apply to all risks<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">OWASP is a technical checklist for identifying what can be attacked. SAIF is an organisational framework for building secure AI applications across the full pipeline. Many of SAIF&#8217;s 15 risks map directly to OWASP entries, but SAIF adds risks that OWASP does not cover, including Unauthorised Training Data, Excessive Data Handling, Model Source Tampering, Model Deployment Tampering, Model Exfiltration, and Model Reverse Engineering.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The four areas<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">SAIF divides an AI application into four areas, each containing multiple components. Every risk in the framework maps to one or more of these components, showing where the risk is introduced, where it is exposed, and where it can be mitigated.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Data<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Covers everything related to the data the model learns from.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Data Sources<\/strong>\u00a0are the original repositories from which data is gathered (databases, APIs, web scrapes, sensor feeds)<\/li>\n\n\n\n<li><strong>Data Filtering and Processing<\/strong>\u00a0covers cleaning, transforming, labelling, deduplication, and synthetic data generation<\/li>\n\n\n\n<li><strong>Training Data<\/strong>\u00a0is the final curated dataset fed into the model during training<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">In traditional software, code defines behaviour. In AI, data defines behaviour. Compromising training data is the AI equivalent of modifying application source code.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Infrastructure<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Covers the hardware, storage, frameworks, and deployment systems underpinning the AI pipeline.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Model Frameworks and Code<\/strong>\u00a0is the code and libraries required to train and run the model (PyTorch, TensorFlow, JAX, etc.)<\/li>\n\n\n\n<li><strong>Training, Tuning, and Evaluation<\/strong>\u00a0covers the process of teaching, adjusting, and testing the model<\/li>\n\n\n\n<li><strong>Data and Model Storage<\/strong>\u00a0includes both training data storage and model storage (local checkpoints, published model hubs)<\/li>\n\n\n\n<li><strong>Model Serving<\/strong>\u00a0is the systems and processes that deploy a model in production<\/li>\n<\/ul>\n\n\n\n<pre class=\"wp-block-code\"><code># Where infrastructure risks sit in a typical pipeline\n\nData Sources --&gt; Data Filtering --&gt; Training Data\n                                         |\n                                         v\nModel Frameworks\/Code --&gt; Training, Tuning, Evaluation --&gt; Model Storage\n                                                              |\n                                                              v\n                                                        Model Serving --&gt; Application\n<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\">Model<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The central area. Covers the model itself and how inputs and outputs are handled.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>The Model<\/strong>\u00a0is the pairing of code and weights produced by training<\/li>\n\n\n\n<li><strong>Input Handling<\/strong>\u00a0covers filtering, sanitising, and protecting against malicious inputs<\/li>\n\n\n\n<li><strong>Output Handling<\/strong>\u00a0covers filtering, sanitising, and protecting against unwanted or dangerous outputs<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Application<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Covers how users and external systems interact with the model.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Application<\/strong>\u00a0is the product or feature that uses the model (a chatbot, a code assistant, an internal tool)<\/li>\n\n\n\n<li><strong>Agents<\/strong>\u00a0are services or additional models called by the AI application to complete specific tasks (tool use, plugin calls, external API interactions)<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Each agent or plugin connection opens a transitive set of risks, meaning the risks multiply with each external integration.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The 15 risks<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">SAIF defines 15 risks across the four areas. Each risk is mapped to who can mitigate it (model creator, model consumer, or both) and which controls address it.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Risk<\/th><th>OWASP equivalent<\/th><th>Who mitigates<\/th><\/tr><\/thead><tbody><tr><td>Data Poisoning<\/td><td>ML02, LLM04<\/td><td>Model creators<\/td><\/tr><tr><td>Unauthorised Training Data<\/td><td>No direct OWASP equivalent<\/td><td>Model creators<\/td><\/tr><tr><td>Model Source Tampering<\/td><td>ML03 (partially)<\/td><td>Model creators<\/td><\/tr><tr><td>Excessive Data Handling<\/td><td>No direct OWASP equivalent<\/td><td>Model creators, consumers<\/td><\/tr><tr><td>Model Exfiltration<\/td><td>ML06<\/td><td>Model creators, consumers<\/td><\/tr><tr><td>Model Deployment Tampering<\/td><td>No direct OWASP equivalent<\/td><td>Model creators<\/td><\/tr><tr><td>Denial of ML Service<\/td><td>ML05, LLM10<\/td><td>Model consumers<\/td><\/tr><tr><td>Model Reverse Engineering<\/td><td>ML07<\/td><td>Model consumers<\/td><\/tr><tr><td>Insecure Integrated Component<\/td><td>LLM03 (supply chain)<\/td><td>Model consumers<\/td><\/tr><tr><td>Prompt Injection<\/td><td>LLM01<\/td><td>Model creators, consumers<\/td><\/tr><tr><td>Model Evasion<\/td><td>ML01<\/td><td>Model creators, consumers<\/td><\/tr><tr><td>Sensitive Data Disclosure<\/td><td>LLM02<\/td><td>Model creators, consumers<\/td><\/tr><tr><td>Inferred Sensitive Data<\/td><td>No direct OWASP equivalent<\/td><td>Model creators, consumers<\/td><\/tr><tr><td>Insecure Model Output<\/td><td>LLM05<\/td><td>Model creators, consumers<\/td><\/tr><tr><td>Rogue Actions<\/td><td>LLM06 (excessive agency)<\/td><td>Model consumers<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Several risks have no direct OWASP equivalent. These are worth understanding because they cover gaps that OWASP&#8217;s lists do not address.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Unauthorised Training Data<\/strong>\u00a0is when a model is trained on data it is not authorised to use. This is a legal and ethical risk rather than a technical attack. <a href=\"https:\/\/en.wikipedia.org\/wiki\/Heart_on_My_Sleeve_(Ghostwriter977_song)\" title=\"\">In 2023, Streaming Platforms removed &#8216;Heart on My Sleeve,&#8217;<\/a> an AI-generated track that cloned the voices of Drake and The Weeknd without authorisation, following a copyright complaint from Universal Music Group.  The risk is about compliance with privacy policies, licensing agreements, and data protection regulations.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Excessive Data Handling<\/strong>&nbsp;occurs when data collection or retention exceeds what is permitted by privacy policies. This is distinct from data poisoning or disclosure because the data itself may be legitimate, but the way it is collected, stored, or retained violates policy or regulation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Model Source Tampering<\/strong>&nbsp;targets the model&#8217;s code, dependencies, or weights directly, either through supply chain attacks or insider access. This includes model architecture backdoors, which are backdoors embedded in the neural network architecture definition and can survive full retraining.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Inferred Sensitive Data<\/strong>&nbsp;is distinct from Sensitive Data Disclosure. In disclosure, the model reveals data it was trained on. In inference, the model provides sensitive information it never had direct access to by reasoning from patterns in training data or prompts. The model works something out that it was never explicitly told.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Model Deployment Tampering<\/strong>&nbsp;targets the serving infrastructure rather than the model itself, compromising components used to deploy models in production.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Controls<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">SAIF organises controls into six categories, mapped to the four areas plus two cross-cutting categories.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Category<\/th><th>Scope<\/th><th>Example controls<\/th><\/tr><\/thead><tbody><tr><td>Data<\/td><td>Training pipeline<\/td><td>Training Data Sanitisation, Training Data Management, Privacy-Preserving Technologies (PETs), User Data Management<\/td><\/tr><tr><td>Infrastructure<\/td><td>Storage, serving, tooling<\/td><td>Model and Data Access Controls, Integrity Management, Inventory Management, Secure-by-Default ML Tooling<\/td><\/tr><tr><td>Model<\/td><td>Input\/output handling<\/td><td>Input Validation and Sanitisation, Output Validation and Sanitisation, Adversarial Training and Testing<\/td><\/tr><tr><td>Application<\/td><td>User and agent interaction<\/td><td>Application Access Management, User Transparency and Controls, Agent User Control, Agent Permissions<\/td><\/tr><tr><td>Assurance<\/td><td>Cross-cutting<\/td><td>Applied to all risks, all stages of the lifecycle<\/td><\/tr><tr><td>Governance<\/td><td>Cross-cutting<\/td><td>Applied to all risks, all stages of the lifecycle<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Each control specifies who is responsible for implementation.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># Example: Output Validation and Sanitisation\n\nControl:       Output Validation and Sanitisation\nAction:        Block, nullify, or sanitise insecure output before passing to applications or users\nImplemented by: Model Creators, Model Consumers\nRisk mapping:   Prompt Injection, Rogue Actions, Sensitive Data Disclosure, Inferred Sensitive Data\n<\/code><\/pre>\n\n\n\n<pre class=\"wp-block-code\"><code># Example: Agent Permissions\n\nControl:       Agent Permissions\nAction:        Apply least-privilege as the upper bound on agent permissions,\n               minimise tools the agent can interact with and actions it can take\nImplemented by: Model Consumers\nRisk mapping:   Insecure Integrated Component, Sensitive Data Disclosure, Rogue Actions\n<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">The distinction between model creator and model consumer is practical. If HackTheBox uses Google&#8217;s Gemini for a chatbot, Google is the model creator (responsible for training data sanitisation, adversarial training) and HackTheBox is the model consumer (responsible for application access management, agent permissions, output validation in their application layer).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The Risk Map<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The SAIF Risk Map is the central reference that ties everything together. For each of the 15 risks, the map shows three things.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Risk introduction<\/strong>\u00a0is where in the pipeline the risk originates (e.g. data poisoning is introduced at data sources, data filtering, or data storage)<\/li>\n\n\n\n<li><strong>Risk exposure<\/strong>\u00a0is where the risk manifests in the running system (e.g. data poisoning is exposed during training\/evaluation or in the model&#8217;s outputs)<\/li>\n\n\n\n<li><strong>Risk mitigation<\/strong>\u00a0is where controls can be applied to address the risk (e.g. data poisoning is mitigated through data sanitisation, access controls, and integrity management early in development)<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This three-point mapping makes the Risk Map useful for threat modelling AI applications. Instead of working from a flat list of vulnerabilities, you can trace each risk through the pipeline from origin to exposure to mitigation.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">SAIF 2.0 and agent security<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/saif.google\/focus-on-agents\" target=\"_blank\" rel=\"noopener\" title=\"\">SAIF 2.0<\/a>, released in early 2026, extends the framework to cover agentic AI systems. Agents differ from standard LLM applications because they take autonomous actions, calling tools, querying APIs, modifying data, and interacting with external services on the user&#8217;s behalf.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The agent extension adds new components to the Risk Map.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Perception<\/strong>\u00a0processes user inputs and contextual data before they reach the reasoning core, and must distinguish trusted commands from untrusted environmental data<\/li>\n\n\n\n<li><strong>Reasoning Core<\/strong>\u00a0plans and iterates on multi-step actions, with the level of autonomy directly governing the severity of a security failure<\/li>\n\n\n\n<li><strong>Orchestration<\/strong>\u00a0manages agent memory, tool calls, RAG content, and auxiliary models, each of which is an attack surface<\/li>\n\n\n\n<li><strong>Response Rendering<\/strong>\u00a0formats agent output for display and is a critical security boundary for preventing injection through dynamic content<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">New controls for agents include Agent Observability (monitoring agent actions), Agent User Control (requiring user approval for state-changing actions), and Agent Permissions (least-privilege applied dynamically based on context rather than statically).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The self-assessment<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Google provides an interactive Risk Self-Assessment at <a href=\"https:\/\/saif.google\/risk-self-assessment\" target=\"_blank\" rel=\"noopener\" title=\"\">saif.google\/risk-self-assessment<\/a>. It asks questions about your AI system&#8217;s security posture covering training, tuning and evaluation, access controls, adversarial input handling, coding frameworks, and agent configurations, then generates a tailored checklist of relevant SAIF controls. The assessment runs locally (Google does not collect answers or results) and is designed for security practitioners as a starting point for conversations about AI-specific risks in their organisation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A reference guide to Google&#8217;s Secure AI Framework, covering the four areas, 15 risks, control mapping, SAIF 2.0 agent security, and how it relates to OWASP.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[640,786,630,109,635,784,662,777,708,785],"class_list":["post-501","post","type-post","status-publish","format-standard","hentry","category-technology","tag-adversarial-ai","tag-agentic-ai-security","tag-ai-red-teaming","tag-ai-security","tag-data-poisoning","tag-google-secure-ai-framework","tag-machine-learning-security","tag-owasp-top-10","tag-prompt-injection","tag-saif"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 4.9.8 - 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