Overview
HoundDog.ai provides a dual-purpose platform: a Privacy Code Scanner that detects sensitive data exposure and automates GDPR data mapping, and an API Context Engine that supplies AI coding agents with real-time service dependency and API usage data. It scans source code to identify PII leaks, shadow AI integrations, and hidden cross-service data flows before they reach production.
Key Features
- Code-Level Data Flow Intelligence: Maps how sensitive data moves across functions, APIs, and third-party services directly from source code.
- Shift-Left Privacy Detection: Catch PII leaks and risky data flows during development via IDE plugins (VS Code, IntelliJ, Eclipse) and CI/CD integrations (GitHub, GitLab, Bitbucket, Jenkins, etc.).
- Automated Compliance Reporting: Generates GDPR data maps, Records of Processing Activities (RoPA), Privacy Impact Assessments (PIA), and DPIA prepopulated with detected flows.
- AI Governance: Detects AI SDKs and sensitive data flows to LLM endpoints, providing visibility into shadow AI.
- API Context for AI Coding Agents: Builds live gRPC API dependency graphs and field-level usage maps, then serves them to MCP-compatible coding agents (Cursor, Claude Code, Copilot) to reduce token waste and improve code change safety.
Use Cases
- Embedding privacy-by-design into the SDLC to prevent PII leaks.
- Transforming manual GDPR data mapping and compliance documentation into an automated, code-evidence-driven process.
- Providing AI coding assistants with accurate, up-to-date API context across large monorepos and microservices.
Who It’s For
- Development teams that need to catch privacy risks early.
- Privacy and compliance officers who require verifiable data flow documentation.
- Security teams managing sensitive data exposure risks.
- Platform engineers supporting AI coding agent workflows.
The platform runs locally or in CI environments, does not require production data access, and is SOC 2 compliant with a public Trust Center.
