# Safety

<br />

AI **safety** includes a set of design and operational techniques to follow to
avoid and contain actions that can cause harm, intentionally or unintentionally.
For example, do AI systems behave as intended, even in the face of a security
breach or targeted attack? Is the AI system robust enough to operate safely
even when perturbed? How do you plan ahead to prevent or avoid risks? Is the AI
system reliable and stable under pressure?

One such safety technique is [adversarial testing](https://developers.google.com/machine-learning/guides/adv-testing),
or the practice of trying to "break" your own application to learn how it
behaves when provided with malicious or inadvertently harmful input. The
[Responsible Generative AI Toolkit](https://ai.google.dev/responsible/docs/evaluation)
explains more about safety evaluations, including adversarial testing. Learn
more about Google's work in this area and lessons
learned in the Keyword blog post, [Google's AI Red Team: the ethical hackers
making AI
safer](https://blog.google/technology/safety-security/googles-ai-red-team-the-ethical-hackers-making-ai-safer/)
or at [SAIF: Google's Guide to Secure AI](https://saif.google/).