HoundDog.ai helps developers prevent personal information from leaking

HoundDog.ai, a startup that helps developers ensure their code doesn't leak personally identifiable information (PII), came out of stealth on Wednesday and announced a $3.1 million seed round led by E14, Mozilla Ventures and ex/ante, in addition to a number of angel investors. Unlike other scanning tools, HoundDog actually looks at the code a developer writes, using both traditional pattern matching and large language models (LLMs) to find potential problems.

HoundDog was founded by Amjad Afana, who previously co-founded DCHQ, which was later acquired in 2016 by Gridstore (which, to complicate matters further, subsequently changed its name to HyperGrid). Afanah also co-founded apisec.ai, which is still active, and worked at self-driving startup Cruise. The inspiration for HoundDog came during his time at data security startup Cyral, and when he spoke to privacy teams there, he told me.

Image credits: HoundDog.ai

“When I worked at Cyral, we had a lot of data,” he said. “What Cyral does – like many others in the data security space – is they focus on production systems. They help you discover, classify your structured data and your databases, and then help you apply access controls. But the overwhelming feedback I kept getting from both security and privacy teams was, “You know, it's a little too reactive and it's not keeping pace with changes in the code base.”

HoundDog therefore shifts this process even further to the left. While it is still part of the continuous integration flow and not yet in the development environment (although that may happen in the future), the idea here is to find potential data leaks before the code is merged. And most importantly, HoundDog does this by looking at the actual code, not the data stream it produces. “Our source of truth is the code base,” Afanah said.

HoundDog.ai Product Screenshot
Image credits: HoundDog.ai

Because of this, if a development team starts collecting Social Security numbers, for example, HoundDog would raise a flag and warn the team about it before the code is ever merged; it would also alert the security team. After all, that could become a major – and expensive – problem.

The service currently supports code written in Java, C#, JavaScript, and TypeScript, as well as SQL, GraphQL, and OpenAPI/Swagger queries. Support for Python is imminent, the company says.

Afanah noted that a tool like this becomes especially important in this age of AI-generated code, something Replit CEO (and HoundDog angel investor) Amjad Masad also reiterated.

“As more companies turn to AI-generated code to accelerate development, embedding security best practices and ensuring the safety of the generated code becomes essential,” Masad said. “HoundDog.ai is leading the way in securing PII data early in the development cycle, making it an indispensable part of any AI code generation workflow. This is why I chose to invest in this company.”

However, HoundDog itself also uses AI. Currently it relies on OpenAI's models for this, but it is important to emphasize that this is optional. Users concerned about their code leaving their private repositories can also choose to rely solely on the company's more traditional code scanner.

A key part of HoundDog's value proposition is that it can reduce compliance costs for startups thanks to its automated reporting capabilities. The service can automatically generate a record of processing activities (RoPA). To do this, HoundDog uses generative AI to generate these reports and sends that data to OpenAI. The team does emphasize that only the tokens discovered by the service through the regular scanner are shared with OpenAI and that the actual source code is not shared.

The company offers a limited free subscriptionwith paid plans starting at $200/month for scanning up to two repos.

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