Purpose
HoundDog.ai is a static code scanner for privacy compliance and sensitive data protection, and can also serve as a data flow context engine for AI coding agents. Its core goal is to discover sensitive data leakage risks during the code development stage and automatically generate traceable compliance evidence, rather than responding passively after deployment.
Main Capabilities
- Detects more than 100 types of sensitive data in code (PII, PHI, CHD, authentication tokens), tracing their flow across nested code paths and transformations into data sinks such as logs, storage, APIs, third parties, and AI integrations.
- Covers more than 1,000 third-party and AI integrations, including identification of Shadow AI, regardless of whether data flows through SDKs or APIs.
- Supports GDPR data mapping, RoPA records, and privacy assessments (PIA, DPIA) based on code evidence.
- Optional AI analysis layered on top of static findings to automatically close false positives, adjust severity, and add context. Scanning runs locally on low-cost CPU, and AI only interprets traces the scanner has already detected.
- Provides AI coding agents with a cross-repository service catalog covering every API, every field, and every downstream consumer, so agents understand the full dependencies when modifying a service or field, increasing the speed and cost of suggesting service or API updates by 5x.
How to Use
- Local operation: scan locally checked-out code, providing a local MCP server, CLI, and Skills, compatible with any AI agent that supports MCP, running deterministically entirely on the user's hardware.
- Continuous organization-level context: cloud-hosted or deployed on the organization's premises, integrating directly with GitHub, Bitbucket, and GitLab, automatically scanning selected repositories without requiring developers to check out code; running scans in CI on every pull request to keep organization-level data fresh.
- Privacy team-controlled process: the engine makes suggestions, and the DPO approves.
Applicable Scenarios
- When engineering teams and AI coding agents need cross-repository API dependency context.
- When privacy and compliance teams want to maintain GDPR data mapping, RoPA, and privacy review evidence at development speed.
- In scenarios where sensitive data such as PII, PHI, and authentication tokens must be prevented from being exposed before being written to logs or entering LLM prompts.
- When combined with platforms such as Replit, so that AI-generated applications have GDPR and CCPA best practices built in from day one.
Limitations
The materials do not provide details such as specific pricing, the list of supported programming languages, deployment resource requirements, or scanning performance limits; these are not yet available.the list of supported programming languages, deployment resource requirements, or scanning performance limits; these are not yet available.
