With growth in artificial intelligence, securing and protecting data has never been more critical. This presentation discusses the importance of data privacy, compliance with regulations, and the growing need for enhanced data security measures.

We will explore security concerns associated with generative AI, such as data bias, social engineering, and model manipulation. This session introduces concepts like adversarial machine learning, data poisoning, and prompt injection, highlighting the potential risks and vulnerabilities in AI systems. Ethical considerations and responsible use of data in AI practices are emphasized, ensuring that organizations can navigate the complexities of AI with integrity.

Practical strategies and techniques for enhancing data security are discussed, including adversarial training, differential privacy, and continuous monitoring of training data. These recommendations provide data professionals with the tools they need to protect their data assets and stay ahead of emerging threats in the AI era.

Is your AI data protected? Is it ready for AI? Are you sure?

While other parts of TEC have a registration fee, this new track is totally free with registration.