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Red teaming, capability evaluations, adversarial testing, and performance verification.
Also in Risk & Assurance
Identify security and privacy improvements through self-driven adversarial attacks on AI infrastructure and products.
- **Who can implement:** - - Model Creators, Model Consumers - **Risk mapping:** - - [All](https://saif.google/secure-ai-framework/risks)
Reasoning
Red teaming conducts adversarial testing to identify security and privacy vulnerabilities in AI systems.
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 Inventory Management
Ensure that all data, code, models, and transformation tools used in AI applications are inventoried and tracked.
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 ControlsGoogle 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
Measure
Quantifying, testing, and monitoring identified AI risks
