Sensitive Data Leakage Through AI Tools

Engineers paste source code and logs into public AI assistants, while business teams share customer records, contracts, and roadmaps with chatbots and copilots. Traditional DLP does not inspect these interactions, so sensitive data leaves the company without a trace. PointGuard AI applies AI-native data protection across prompts, responses, and tool calls, keeping code and customer data within approved boundaries.

Sensitive technology data moves through AI tools that existing DLP cannot see.

• Source code, secrets, and logs are pasted into public assistants.
• Customer records and contracts are shared with unapproved chatbots.
• Agents can retrieve and forward data through connected tools.
• Generic detectors miss proprietary code and project names.

AI Data Protection and Shadow AI Control enforce data policies across sanctioned and unsanctioned AI tools.

1. Define protected data. Use built-in detectors for PII, credentials, and source code, and add custom types for product names, repositories, and roadmaps.

2. Inspect every interaction. Scan prompts, responses, and governed tool calls at sub-100 ms latency across browsers, IDEs, and AI applications.

3. Redact, block, or redirect. Mask sensitive data when work can continue, block it when it cannot, and steer users toward approved AI tools.

4. Report and refine. Track detections by team and tool, and feed events into security operations and policy updates.

• OWASP Top 10 for LLMs: LLM02 Sensitive Information Disclosure and LLM07 System Prompt Leakage, covering disclosure of code and customer data and exposure of sensitive context.
• OWASP Top 10 for Agentic Applications: ASI02 Tool Misuse and Exploitation, addressing agents that move sensitive data through connected tools.
• NIST AI RMF: MAP identifies sensitive data flows; MEASURE evaluates exposure; MANAGE enforces policies; GOVERN sets acceptable use.