Knowledge Sprint - Securing AI

As AI adoption accelerates, so do the threats targeting those systems.

From adversarial attacks and model theft to data poisoning and API abuse, the risks are real and evolving rapidly.

In this webinar, we will explore how attackers exploit vulnerabilities in AI through adversarial examples, model extraction, and data poisoning, and more importantly, and how Crystal Eye TDIR platform can defend against them.

We will break down the technical foundations and share real-world mitigation techniques across the AI lifecycle - from training and deployment to production monitoring.

What You'll Learn:

• What makes ML models vulnerable to adversarial examples.
• Techniques like defensives distillation, input sanitisation, and data augmentation to increase model resilence.

• Securing APIs and model endpoints with authentication, authorisation, and rate limiting.
• Encrypting models and data at rest and in transit.
• Using runtime monitoring and anomaly detection to flag suspicious behaviour in real time.

• Preventing model theft with watermarking and model fingerprinting.
• Enhancing privacy with differential privacy and secure data handling.
• Adopting a layered security strategy across data, model and infrastructure components.

Register Now - Secure Your Spot!
Don't miss this opportunity to strengthen your AI systems against emerging threats.