Data Leaks Through SaaS AI Integrations

SaaS copilots and agents send customer PII, account records, and support history to third-party LLMs through prompts and MCP tool calls. A single retrieval or prompt-construction error can expose one tenant's data to another, and traditional DLP does not inspect these flows. PointGuard AI applies AI-native data protection inside the product so tenant data stays where it belongs.

Tenant data moves through AI pipelines that existing controls cannot see.

• Prompts carry customer PII to third-party model providers.
• Retrieval errors can surface one tenant's data to another.
• Agent tool calls can pull more account data than needed.
• Customers demand proof of data isolation for AI features.

AI Data Protection, Runtime Guardrails, and MCP Security Gateway enforce data policies inside SaaS AI features.

1. Define protected data. Use built-in PII detectors and add custom types for account IDs, tenant identifiers, and customer-specific data.

2. Inspect every interaction. Scan prompts, responses, and governed MCP tool calls at sub-100 ms latency, without adding noticeable delay for users.

3. Enforce tenant boundaries. Block responses that contain data from another tenant and redact sensitive fields before they reach external models.

4. Prove isolation. Log detections and enforcement events per tenant to support customer reviews, SOC 2 audits, and incident response.

• OWASP Top 10 for LLMs: LLM02 Sensitive Information Disclosure and LLM08 Vector and Embedding Weaknesses, covering customer data disclosure and cross-tenant leakage through shared retrieval stores.
• OWASP Top 10 for Agentic Applications: ASI02 Tool Misuse and Exploitation and ASI03 Identity and Privilege Abuse, addressing agents that misuse tools or access to move tenant data.
• NIST AI RMF: MAP identifies tenant data flows; MEASURE evaluates exposure; MANAGE enforces isolation; GOVERN defines data policies.