Generative AI is rewriting the rules of software, but without strong data controls and real-time application security, it can become an uncontrolled risk. Code is no longer the only attack surface—your AI models and their inputs are just as exposed. Generative AI Data Controls with RASP (Runtime Application Self-Protection) bridge that gap, securing your models as they run, not just before deployment.
Traditional firewalls and static scans cannot see inside a running generative AI process. That’s why RASP is critical. It operates inside the execution environment, observing requests, parsing data flows, and enforcing policy directly where vulnerabilities surface. With generative AI, this means intercepting prompts, context, and outputs to prevent injection attacks, sensitive data leaks, or misuse of proprietary information.
Generative AI Data Controls in a RASP framework give you visibility into every interaction with your model. They make it possible to block harmful queries, redact confidential data, and audit each transaction without slowing down response time. For production AI systems, this is not optional—it’s a baseline security requirement. It’s where performance and protection meet without trade-offs.