HoundDog.ai static code scanner not only flags PII leaks in plaintext within logs, files, cookies, and tokens but also tracks data flows to third-party integrations, highlighting data processing agreement violations before they become production issues.

Great tool to catch potential leaks at coding level and CI

Before my co-founders and I started HoundDog.ai, I served as the VP of Product at a data security company, specializing in discovering, classifying, and applying access controls to sensitive data in production. During this time, I encountered numerous concerns from security and privacy teams, who were frustrated with the reactive data security and privacy measures that struggled to keep pace with rapid changes in their applications’ codebases. This frustration sparked the idea for HoundDog.ai. Common questions from these teams included: ❓ “How can I prevent PII data from leaking in the first place, rather than catching it once it’s already in production logs, files, or third-party systems?” ❓ “How can we establish a reliable method for documenting processing activities that keeps up with changes in our codebase without relying on inconsistent tribal knowledge?” ❓ “How can we proactively track data flows to third-party integrations and ensure continuous adherence to established data processing agreements?” Our founding team had several well-informed ideas about how to address these issues, and that led to the creation of HoundDog.ai. Proactive vs. Reactive Data Security and Privacy For too long, organizations have taken a reactive approach to detecting and remediating PII leaks. In 2023, 92% of compromised data involved PII. Addressing PII in logs is complex, requiring code updates, reviewing logs for sensitive data, and assessing the risks of exposed information under various compliance frameworks. PII in logs can also spread to other systems like monitoring tools, SIEMs, and backups, increasing the challenge for security teams. Integrating security measures into the development process from the start is crucial. Tools like HoundDog.ai’s static code scanner catch PII leaks early, helping developers fix issues and saving security teams from chasing leaks across multiple systems. HoundDog.ai for Developers, Security Engineers, & Privacy Teams HoundDog.ai’s static code scanner has something to offer for everyone involved in code development, application security, and data privacy. ➡️ Developers - Eliminate the guesswork on what PII your code repositories process. - Automate responses to privacy questionnaires. - Identify PII exposed in logs, files, and third-party integrations, and fix these issues before pushing to production. ➡️ Application Security Engineers - Augment your code scanning with PII leak findings that have never been covered at the code level before. - Enhance your prioritization strategy with insights into PII data handling per code repository. ➡️ Privacy Engineers - Automate documentation of processing activities at the speed of development. - Avoid being blindsided by changes in PII. - Detect third-party data processing agreement violations before they escalate into production issues Try Our Free Scanner Today HoundDog.ai offers a free static code scanner that provides a comprehensive PII inventory of your code repositories, including the sensitivity levels of the identified PII, and automates privacy questionnaire responses with up-to-date data. Our Rust-based scanner is only a few megabytes in size and is incredibly fast, scanning 5 million lines of code in under a minute. The findings are available directly in your terminal or as neatly formatted markdown or JSON files. The scanner supports all popular programming languages, including Java, C#, Python, JavaScript, TypeScript, Ruby, and Kotlin. We welcome any and all feedback! Thanks for checking out our product!

@amjad_afanah1 Huge congrats on your launch! Wishing you a journeyfilled with success and growth. What are your next seps post-launch?

@amjad_afanah1 HoundDog.ai looks like an incredible solution for addressing the long-standing challenges of PII data security and privacy in a more proactive way. The ability to integrate data security measures into the development process and prevent PII leaks before they become production issues is a game-changer for developers, security engineers, and privacy teams alike. It’s fantastic that HoundDog.ai offers a free, fast, and lightweight static code scanner that supports multiple programming languages. The scanner’s ability to provide comprehensive PII inventory and automate privacy documentation will save countless hours and prevent potential data exposure risks. This tool is bound to make a significant impact on how organizations handle PII and stay compliant. Congrats on the launch! I’m sure it will resonate well with teams seeking a smarter approach to data security and privacy. 🚀

Hey Amjad, How does HoundDog handle false positives, especially in complex codebases? Do you have any plans to integrate with popular CI/CD pipelines for automated scanning? Congrats on the launch!

Thank you and great questions! At HoundDog.ai, we use both traditional code analysis methods and LLMs. I'd like to briefly explain the drawbacks of each approach that result in false positives, and how we've addressed them. We parse abstract syntax trees and match code segments against known regex patterns - a traditional approach still effective in many static code scanners today. Crafting these patterns can be challenging: overly restrictive patterns miss real issues, while broad ones generate false positives. Fortunately, after extensive testing we were able to come up with clever tricks and schemas that allow us to write our patterns systematically while drastically minimizing false positives. With near 100% unit test coverage, our iteration process at this point is quite robust and reliable. We're also leveraging LLMs in moderation to continuously refine our patterns, ensuring they evolve without regressions. LLMs are getting incredibly good at extracting meaning from code, and we believe they are the future of code analysis. But as of today, due to context length limits and frequent inaccuracies, you cannot throw millions of lines of code at AI and expect results that are consistent, fast, and correct all at the same time. While the inference cost is getting cheaper, expenses can add up quickly if you have to pay for an API or GPU cluster to process repositories with millions of lines of code. Some of the larger businesses we've talked to are not comfortable with giving AI their code or are not ready due to lack of proper legal and engineering infrastructure (although this will surely change in the future). This is why we are currently limiting the usage of LLMs to very specific use cases. One such use case is enhancing our regex patterns as mentioned above. And we extract only a small subset of tokens that we consider critical before sending it over to AI for verification. This is one of the reasons why our scanner can be blazingly fast. In fact, we can confidently claim that it is one of the fastest in the market. So please feel to give it a try! And yes, we support all popular CI/CD pipelines such as GitHub Actions, GitLab CICD, Jenkins etc.

Congrats ro the launch! 🚀

HoundDog.ai Static Code Scanner

Catch PII Leaks in Code & Keep Your PII Inventory Current

Catch PII Leaks in Code & Keep Your PII Inventory Current

HoundDog.ai Static Code Scanner

Catch PII Leaks in Code & Keep Your PII Inventory Current

Catch PII Leaks in Code & Keep Your PII Inventory Current

Do you use HoundDog.ai Static Code Scanner?

Do you use HoundDog.ai Static Code Scanner?