HoundDog.ai’s lightweight, modern privacy code scanner enables technology driven companies to embed privacy into the development process, catch privacy risks early, and automate GDPR data mapping and privacy reporting (RoPA, PIA, and DPIA). The scanner replaces the guesswork in data flow mapping with code level evidence, mapping sensitive data flows directly from your applications’ source code, including across logs, APIs, SDKs, and AI integrations, before anything reaches production.
Many teams use it to prevent overlogging of sensitive data such as PII, PHI, CHD, and authentication tokens (see the full list of supported data elements). It also helps uncover hidden third party and AI integrations (see the full list of supported data sinks) and detect data flow changes that can violate internal policies or data processing agreements due to silent code updates early in development. Common issues include simple oversights such as printing full user objects or passing tainted variables.
HoundDog.ai’s lightweight, high performance code scanner is designed to track sensitive data flows across complex transformations and interpolations in code. These risks are often introduced by developers or AI generated code, where tainted variables or accidental dumping of objects can occur. The scanner applies deep interprocedural and taint analysis across files and functions to accurately trace how sensitive data propagates through applications and identify potential exposures before they reach production.
HoundDog.ai is trusted by Fortune 1000 companies in the technology and healthcare sectors and has recently integrated with Replit to bring privacy code scanning to its 45M creators, offering instant visibility into sensitive data flows and privacy issues.
The platform enables organizations to:
🧭 Step 1: Discover AI and third-party integrations
- Track with high confidence the types of AI and third party integrations that developers have embedded in their code repositories as part of your AI governance and third party risk management efforts.
🔐 Step 2: Trace sensitive data flows
- Track over 100 sensitive data types, including PII, PHI, CHD and authentication tokens. Follow them through transformations such as tainted variables across file boundaries, no matter how deeply buried, down to risky sinks such as LLM prompts, logs, temporary files, local storage, and third-party SDKs.
⚖️ Step 3: Bake in your privacy standards
- Apply allowlists to define which data types are permitted in LLM prompts and other risky sinks. Automatically block unsafe pull requests to maintain compliance with Data Processing Agreements.
📑 Step 4: Build customer trust through transparent data handling
- Generate evidence-based data maps that show where sensitive data is collected, processed and shared, including through AI and third-party integrations.
- Produce audit-ready Records of Processing Activities (RoPA) and Privacy Impact Assessments (PIAs), pre-populated with detected data flows and privacy risks aligned with GDPR, CCPA, HIPAA, and other regulatory frameworks.
Embed Privacy Across All Stages of Development
HoundDog.ai's privacy code scanner can be used as a CLI that installs locally to scan cloned code repositories, or as IDE plugins that flag sensitive data leak issues as code is being written. The IDE plugins are available for VSCode, Cursor, JetBrains, and Eclipse. The HoundDog.ai Cloud Platform also provides Source Code Management Platform Integrations - connecting directly to GitHub, GitLab, and Bitbucket (both cloud and enterprise versions) to automatically scan code, block PRs, and leave actionable PR comments.
Free vs Paid Plans
For details on what is included in the free version and the paid plans, please visit our GitHub repository or pricing page for the most up to date information.