Data Leakage Prevention

via “personally identifiable information (pii) leakage detection”

Real-time prompt injection and LLM threat detection API.

Unique: Operates bidirectionally on both user inputs and LLM outputs, detecting PII leakage in both directions. Uses pattern matching combined with semantic analysis to identify PII across multiple formats and languages without requiring explicit data masking rules.

vs others: More comprehensive than regex-based PII detection (which misses context-dependent cases) and faster than manual compliance audits, though less accurate than human review for ambiguous cases.

via “pii-leakage-detection-and-redaction”

Enterprise LLM evaluation for hallucination and safety.

Unique: Integrated into Patronus's unified evaluation platform, allowing PII detection to be combined with hallucination, toxicity, and brand safety checks in a single evaluation run, with results aggregated in the experiment dashboard.

vs others: Offers PII detection as part of a comprehensive LLM evaluation suite rather than as a standalone tool, reducing the need to integrate multiple point solutions and enabling cross-evaluation correlation (e.g., 'hallucinations that also leak PII').

via “data loss prevention (dlp) with tool output filtering”

** - A hosted registry and control plane to install & run secure + portable MCP Servers.

Unique: Implements DLP as a platform-level output filter applied to all tool executions, preventing sensitive data leakage at the gateway layer. Most MCP implementations lack built-in DLP; mcp.run provides centralized data protection.

vs others: Provides centralized DLP filtering compared to alternatives requiring individual tool implementation of data protection, ensuring consistent policy enforcement across all tools.

via “data exfiltration prevention”

via “data-loss-prevention-for-llms”

via “pii-detection-and-masking”

via “data exfiltration prevention and blocking”