targeting CTOs, DevOps leads, and security engineers in SaaS companies.
The Challenge
Software development teams using AI coding assistants (GitHub Copilot, Cursor, Claude via API) regularly expose customer data embedded in their development environment: unit tests containing real customer records, log files with production data used for debugging, database migration scripts with sample data, and configuration files referencing production credentials. When this code is shared with AI coding assistants, the AI vendor receives production customer data. GitHub's 2025 research found that 39 million secrets (API keys, credentials, PII) were leaked in public repositories in 2024, with a significant portion coming from test data and debugging artifacts.
By the Numbers
- 39 million, 2025, 2024
Real-World Scenario
A SaaS engineering team uses Cursor (AI IDE) for development. After discovering production customer email addresses in unit test fixtures, their CTO mandated PII review before all AI-assisted code review. anonym.legal's MCP Server integration in Cursor enables developers to anonymize test data in-workflow: select file, run anonymization, paste anonymized version to AI assistant for review. Zero new external tools; same anonym.legal account they use for other PII work. Production customer data removed from AI assistant context in first week.
Technical Approach
The MCP Server integration brings anonym.legal's PII detection directly into Claude Desktop and Cursor AI IDE. Developers can process code files, test data, and log excerpts through the anonymization pipeline before sharing with their AI assistant. Custom entities for internal identifiers (customer IDs, account numbers) work alongside standard PII types. The same engine available in all other contexts means consistent detection whether reviewing code in the IDE or documents in the web app.
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