Privileged Client Data Leaking Through AI

Drafting copilots and research assistants now handle privileged filings, deal documents, and client financials. File-based DLP cannot inspect prompts or tool calls, so confidential client data can reach unapproved models or the wrong matter team without detection. PointGuard AI applies AI-native data protection at every AI interaction, keeping privileged work product inside governed boundaries.

Client confidentiality depends on controls that do not inspect AI traffic.

• Lawyers and consultants paste client documents into AI tools.
• Research assistants retrieve data across matters.
• Third-party AI vendors may retain client inputs.
• Engagement terms restrict how client data may be used.

AI Data Protection and Runtime Guardrails enforce confidentiality policies across firm AI tools.

1. Define protected data. Use built-in detectors for PII and financial data, and add custom types for client names, matter numbers, and deal code names.

2. Inspect every interaction. Scan prompts, responses, and tool calls at sub-100 ms latency.

3. Enforce matter boundaries. Block client data headed to unapproved tools or to users outside the matter team.

4. Record evidence. Log detections for risk, ethics, and information governance teams.

• OWASP Top 10 for LLMs: LLM02 Sensitive Information Disclosure and LLM07 System Prompt Leakage, covering disclosure of client work product and exposure of sensitive prompt context.
• OWASP Top 10 for Agentic Applications: ASI02 Tool Misuse and Exploitation and ASI03 Identity and Privilege Abuse, addressing research agents that misuse tools or access across matters.
• NIST AI RMF: MAP identifies client data flows; MEASURE evaluates exposure; MANAGE enforces policy; GOVERN assigns confidentiality ownership.