Sensitive data exposures to third parties are rarely intentional. They happen as codebases grow. A developer prints a full user object, a tainted variable carries PII through a chain of transformations, and by the time anyone notices, the data has already been sent to a third party. HoundDog.ai traces every flow into every SDK, API, and AI integration directly in code, so Data Processing Agreement violations are caught at scan time, before any data leaves your application.
HoundDog.ai works the way developers do: in the codebase, in the IDE, and in the pull request. It traces your applications' data flows as defined in the application code logic to track more than 100 sensitive data types (including PII, PHI, CHD and auth tokens) through intermediate transformations across files, functions, and procedures regardless of nesting depth, and flagging them when they reach a third-party sink, whether that is an analytics SDK, a CRM API, or an LLM prompt.
Uncover all third-party SDKs, APIs, and shadow integrations introduced by engineering teams, often without the knowledge or approval of privacy teams, directly in the codebase before they ship.
Automated data flow mapping shows exactly which sensitive data elements reach each data sink per repository, from logs and AI services like OpenAI to third parties like Slack, Stripe, and Twilio, with every flow rated safe or risky.
Apply precise allowlists per third-party SDK or API to enforce each Data Processing Agreement at the code level, automatically blocking unsafe changes in pull requests that would send unpermitted data elements to a processor. Default allowlists ship out of the box for common processors. For example, Stripe's defaults already include bank card details and exclude SSNs, so the baseline is in place from day one and the privacy team only customizes where the DPA diverges. For continuous visibility into every third-party data flow more broadly, see third-party data flow monitoring.
Unlike GDPR compliance software that relies on questionnaires, HoundDog.ai builds the data map from code. PII detection covers more than 100 sensitive data types spanning PII, PHI, cardholder data, and authentication tokens, plus custom patterns for proprietary fields that standard scanners miss. Processing purposes are derived from actual application behavior, third-party recipients and AI endpoints are identified from real integration points in code, and the resulting data map holds up when a supervisory authority requests your records.
Purpose built for privacy teams that need data processing agreements enforced from real data flows detected directly from source code, not surveys or assumptions.
Detect and map sensitive data flows directly from source code across APIs, services, and third party integrations without relying on surveys, spreadsheets, or privacy tools that miss hidden integrations and SDKs.
Discover AI SDKs embedded in code and detect sensitive data flows to LLM prompts and external AI APIs before your apps go live.
Catch privacy issues during development and code review, not after data has already been logged, shared, or leaked.
Automatically generate audit ready PIA and DPIA documentation, and keep your RoPA current through scanner suggested edits, all from detected code level data movement so compliance stays up to date as systems evolve.
A short walkthrough of how HoundDog.ai discovers third-party integrations, traces sensitive data flows to each one, and surfaces unsafe flows in the pull request before they ship.
Watch nowTry the free Privacy Code Scanner and see exactly which data elements reach each SDK, API, and AI integration, before your DPAs are violated.