<ul><li>AI Security series is focusing on safe data practices for AI training and inference, emphasizing the importance of data security in building trustworthy AI systems.</li><li>Data plays a crucial role in AI, where threats like training data leaks, data poisoning, inference-time attacks, and logging leaks can compromise the system's integrity.</li><li>Best practices include anonymizing user data, validating and sanitizing inputs, limiting memorization, versioning and auditing datasets for training, and implementing input filtering, token limits, response monitoring, and data logging precautions for inference.</li><li>Tools like Presidio, Cleanlab, TextAttack, Datasette, along with suggestions for fine-tuning with user support tickets and custom data guards for pipelines, can enhance data security practices in AI applications.</li></ul>

Safe Data Practices for AI Training & Inference | AI Security series

Discover more