HoundDog.ai flips the model on sensitive data protection. It analyzes code early to catch the developer and AI-generated mistakes that overlog and overshare PII, PHI, cardholder data, and auth tokens, before any of it reaches production.
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 sink, whether it is a controlled sink like a database or a high-risk one like an LLM prompt or application logs.
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.
Track 100+ sensitive data types like PII, PHI, CHD, and auth tokens across function calls and transformations to detect exposure in third-party SDKs, APIs, and other risky mediums, stopping accidental leaks before code reaches production.
Apply precise allowlists for third-party SDKs and other risky sinks to enforce Data Processing Agreements, automatically blocking unsafe changes in pull requests that could result in privacy violations.
DLP reacts once sensitive data is already written, and scrubbing it back out is reactive and disruptive every time. HoundDog.ai traces the data into the log statement at scan time, before it ever executes.
String msg = String.format( "%s charged %s %s to the %s %s held by %s", merchant.getName(), amount, currency, card.getType(), card.getLast4(), cardholder.getName()); log.warn(msg); // cardholder + card data traced before it runs
WARN Uber Eats charged 148.27 USD to the CREDIT VISA-4242 held by Sarah Johnson ([email protected])
log.debug("retrieveToken failed {} {} {}", provider, grantType, refreshToken, ex) // refresh token traced before it runs
DEBUG retrieveToken failed for provider salesforce. Grant type refresh_token. Refresh Token eyJhbGciOiJIUzI1NiIsInR5cCI6...
Posture and enforcement tools only act once sensitive data is already there. Preventing the leak at the source is the missing layer, and a prerequisite for the rest of your stack.
A real PHI leak into an LLM: Medical History flows from the source, into a prompt template, and out to OpenAI through llm.invoke, traced from the line it is detected to the sink before it ever runs in production.Catch privacy risks early with IDE plugins and block risky pull requests in CI, all with no manual tracking or stale documentation.
Highlight PII leaks as code is written, catching privacy risks before they ever reach a pull request.
Select repos, push a CI config, and a pre-merge gate goes active on the next pull request to block risky changes before they merge.
The CI/CD checks above, automated for you.
Not after it reaches production. Prevention is now a requirement, not a nice-to-have.
Sensitive data can be exposed to AI tools within minutes of a code change, far faster than reactive tooling can respond.
Fixing leaks after release does not prevent the real damage. The data has already been written, ingested, and shared.
Modern privacy programs must prevent risks, not just report them after exposure has already occurred.
Catch leaks at the code level, enforce data minimization, and stop risky changes before they reach production. Start free, or book a live demo to see it on your own codebase.