Preventing Data Leaks in AI Systems

Your AI model is only as secure as the data it was trained on—and today’s models are leaking that data at an alarming rate.

The new attack surface nobody saw coming

Membership inference attacks can tell if a specific person’s data was in training. Model inversion reconstructs faces from face recognition embeddings. Prompt injection tricks chatbots into spitting out training snippets—including emails, code, and medical records.

Real-world examples are piling up: ChatGPT leaking proprietary code. Stable Diffusion regenerating copyrighted images. Healthcare models exposing patient diagnoses.

DataCloakAI stops leaks at the source

Instead of patching models after training (and hoping), DataCloakAI ensures sensitive patterns never enter the model in the first place:

• Synthetic data generation with provable privacy bounds
• Automated redaction of PII, trade secrets, and toxic content
• Continuous monitoring for leakage risk during training

You get better, safer models—without the constant fear of the next embarrassing (or expensive) data leak.

In the age of weaponized AI, the most valuable defense isn’t a bigger moat. It’s data you can trust completely.