AI applications are becoming more powerful and widespread, but with that growth comes increasing security threats. From adversarial inputs and model inference attacks to output injection, API abuse, and knowledge distillation, AI systems face a range of risks that can compromise their integrity, reliability, and safety.

In this presentation, we’ll explore best practices for securing AI applications, including input validation, output moderation, access controls, monitoring, continuous updates, etc. Additionally, we’ll discuss how AI-driven mitigation models can enhance security by detecting and responding to emerging threats in real time.

Finally, we’ll dive into techniques for making AI models distillation-proof, ensuring that proprietary knowledge remains protected against extraction attempts. By the end of this session, you’ll have actionable strategies to strengthen your AI systems against evolving security challenges.