HoundDog.ai is a privacy code scanner built for privacy and engineering teams at companies developing custom applications and software. It helps technology-driven organizations embed privacy and AI governance directly into the development process, catching data exposure risks early and automating GDPR data mapping and privacy reporting, including Records of Processing Activities (RoPA), Privacy Impact Assessments (PIA), and Data Protection Impact Assessments (DPIA). Instead of relying on surveys, interviews, or manual data flow mapping, HoundDog.ai traces sensitive data flows directly from your application's source code across APIs, SDKs, and AI integrations before anything reaches production. Privacy teams get accurate, audit-ready documentation generated continuously from the code itself. Traditional privacy tools require access to production data and remain blind to integrations embedded in code. HoundDog.ai takes a less intrusive, more precise approach. It plugs into your development workflow and continuously scans source code, flagging risky data flows, log leaks, and newly introduced third-party and AI subprocessors that privacy assessments often miss. The scanner covers a comprehensive and continuously expanding set of sensitive data elements and data sinks. Full lists are available at github.com/hounddogai/hounddog/blob/main/data-elements.md and github.com/hounddogai/hounddog/blob/main/data-sinks.md. Under the hood, the scanning engine is built in Rust, fully rule-based, and deterministic. The rule specification is expressive enough to model real-world code at compiler-level accuracy, while AI is used selectively to scale coverage across thousands of code patterns. This gives you the depth of LLM-based analysis without the cost, latency, or unpredictability. Code never leaves your environment, scans complete in seconds even across codebases with millions of lines, and the lightweight footprint means privacy scanning fits into CI pipelines without slowing anyone down. Teams use HoundDog.ai to prevent overlogging of sensitive data, uncover hidden third-party integrations, enforce proactive AI governance, and catch subtle data flow changes that can violate internal policies or data processing agreements after a routine code update. This includes new AI or third-party subprocessors where shared data might not align with existing DPAs, or where a DPA may not even be in place, as is often the case with AI orchestration frameworks like LangChain. These exposures 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 logged or sent to a third party. The scanner supports every stage of development, from IDE extensions for VS Code, IntelliJ, and Cursor to direct source code integrations with GitHub, Bitbucket, and GitLab. CI configuration that typically takes weeks can be rolled out in minutes, applied in bulk across selected repositories with customizable scan frequency, pull request comments, and support for self-hosted runners. Developers stay in flow with suggested fixes surfaced directly in pull request comments or within their IDE, so remediation is fast and low-friction. HoundDog.ai is trusted by Fortune 1000 companies in technology, financial services, and healthcare, and is integrated with Replit to bring privacy code scanning to over 45 million developers worldwide.