Harden Your AI Systems: Applying Industry Standards In The Real World
Red Hat, Monday, December 15th, 2025
In the last article, we discussed how integrating AI into business-critical systems opens up enterprises to a new set of risks with AI security and AI safety. Rather than reinventing the wheel or relying on fragmented, improvised approaches, organizations should build on established standards and best practices to stay ahead of cybercriminals and other adversaries.
To manage these challenges, enterprises need to adopt a formal approach by using a set of frameworks that map AI threats, define controls, and guide responsible adoption. In this article, we'll explore the evolving AI security and safety threat landscape, drawing from leading efforts such as MITRE ATLAS, NIST, OWASP, and others.
Note: Before diving into frameworks, it's important to understand differences between AI security and AI safety. Check out our previous article, which provides key characteristics and examples of each.