Should You Trust LLMs with Sensitive Data? Exploring the security risks of GenAI

How LLMs interact with data: the privacy perspective

LLMs depend on datasets like emails, codes, product documents, and website content to continuously learn and improve. Data fed into these systems can be broadly classified into two types: sensitive and non sensitive. When users interact with AI, in most cases, they don’t distinguish between sensitive and non sensitive data.

Why should you be concerned? Common AI privacy problems
1. Data usage without permission
2. Data exfiltation
3. Data surveillance bias.

Real case studies of AI privacy violations that made headlines
Case study 1: Facebook and Cambridge Analytica
Case study 2: Clearview AI Facial Recognition
Case study 3: Google DeepMind & NHS.

Finding the right balance between privacy and productivity

• Intelligent Data Masking: Protecto identifies and masks sensitive information, such as PII PHI, within AI prompts and responses, allowing AI models to operate on data without exposing sensitive details.
• Context-Aware Detection: Unlike traditional tools that rely on keyword matching, Protecto employs AI-aware masking techniques that understand the context of data.
• Enterprise-Scale Support: Designed for scalability, Protecto offers dynamic auto-scaling and multi-tenant support, handling millions of data records daily with minimal latency.
Want to know how we helped businesses like yours protect their sensitive data? Schedule a demo to see how Protecto’s data protection platform can help.