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User vetting, access restrictions, encryption, and infrastructure security for deployed systems.
Also in Operations & Security
Ensure that all data, code, models, and transformation tools used in AI applications are inventoried and tracked.
- **Who can implement:** - - Model Creators, Model Consumers (if storing models) - **Risk mapping:** - - [Data Poisoning](https://saif.google/secure-ai-framework/risks#data-poisoning), [Model Source Tampering](https://saif.google/secure-ai-framework/risks#model-source-tampering), [Model Exfiltration](https://saif.google/secure-ai-framework/risks#model-exfiltration)
Reasoning
Inventory tracking provides observability and control infrastructure protecting against tampering and exfiltration.
Privacy Enhancing Technologies
Use technologies that minimize, de-identify, or restrict use of PII data in training or evaluating models.
1.1.1 Training DataTraining Data Management
Ensure that all data used to train and evaluate models is authorized for the intended purposes.
2.3.2 Access & Security ControlsTraining Data Sanitization
Detect and remove or remediate poisoned or sensitive data in training and evaluation.
1.1.1 Training DataUser Data Management
Store, process, and use all user data (e.g. prompts and logs) from AI applications in compliance with user consent.
2.3.2 Access & Security ControlsModel and Data Access Controls
Minimize internal access to models, weights, datasets, etc. in storage and in production use.
2.3.2 Access & Security ControlsModel and Data Integrity Management
Ensure that all data, models, and code used to produce AI models are verifiably integrity-protected during development and deployment.
1.2.4 Security InfrastructureGoogle Secure AI Framework
Google (2024)
SAIF is Google’s Secure AI Framework, which offers guidance for building and deploying AI responsibly. As AI technology rapidly advances and threats continually evolve, the challenge of protecting AI systems, applications, and users at scale requires that developers have a high-level understanding of AI-specific privacy and security risks in addition to established secure coding best practices. SAIF describes Google’s approach for addressing AI risks—including security of data, models, infrastructure, and applications involved in building AI—and is aligned with Google's Responsible AI practices, to keep more people safe online. SAIF is designed to help mitigate risks specific to AI systems like model exfiltration, data poisoning, injecting malicious inputs through prompt injection, and sensitive data disclosure from training data.
Operate and Monitor
Running, maintaining, and monitoring the AI system post-deployment
Developer
Entity that creates, trains, or modifies the AI system
Manage
Prioritising, responding to, and mitigating AI risks
