Watch HoundDog.ai discover third-party SDKs in source code, map sensitive data flows for GDPR, and auto-generate Privacy Impact Assessments so privacy teams validate reviews with code-level evidence.
All third-party and AI integrations detected directly in source code, including Shadow AI, whether the data flows through an SDK or API, with 1,000+ integrations covered out of the box.
Trace 100+ sensitive data types (PII, PHI, CHD, auth tokens) across code paths and into every data sink, including logs, storage, APIs, third-party, and AI integrations.
Keep your RoPA updated as new categories of personal data and subprocessors are introduced, detected directly from source code.
Validate design-phase privacy reviews with code-based evidence before code is pushed to production.
HoundDog.ai discovers third-party SDKs, APIs, and open-source integrations directly in your source code, before they are deployed or granted access to data. Because detection happens in code rather than from network traffic or identity providers, it surfaces sanctioned and unsanctioned integrations alike, including shadow SDKs that other tools miss.
The scanner traces how sensitive data flows across functions, services, APIs, third-party SDKs, and AI integrations, then builds a code-level data map showing where personal data is collected, processed, and shared. The map is generated from analysis of your actual codebase, so it stays current as code changes, without surveys or manual discovery.
Yes. For each application it scans, HoundDog.ai auto-generates audit-ready Privacy Impact Assessments (PIA) and Data Protection Impact Assessments (DPIA), pre-populated with the data flows and privacy risks detected in that application's code and aligned with GDPR, CCPA, HIPAA, and other frameworks. Because these reports are scoped to the scanned application, privacy teams start each review from real, code-level evidence rather than a blank template.
The Org RoPA covers all of an organization's processing activities, not only the codebases HoundDog.ai scans, including functions like Support, Sales, Marketing, and Data Analytics. For the activities tied to scanned code, the scanner surfaces each new data flow or subprocessor as a suggested edit to the RoPA. For activities outside the codebase, the relevant stakeholders can be invited to propose suggested updates in the same way. Privacy teams review and approve every suggested change, with full history tracking, so they maintain full control over the entire record.
HoundDog.ai produces taint traces that show exactly how sensitive data reaches each destination in code. Privacy teams use these traces to confirm that what was approved at the design stage matches what was actually implemented, validating privacy reviews with verifiable evidence before code ships.