HoundDog.ai provides a static code scanner that detects and enforces guardrails on sensitive data within LLM prompts and AI data sinks such as logs and temporary files before code is pushed to production. The scanner integrates with IDEs (VS Code, JetBrains, Eclipse) and CI pipelines to run pre-merge checks and scan repositories. The tool has scanned over 20,000 repositories and reduces reactive DLP remediation, saving engineering hours. DevSecOps teams can block unapproved data types and unsafe pull-request changes while generating audit-ready RoPA and PIA reports tailored to GDPR, HIPAA and other frameworks. The scanner also detects direct and indirect AI usage, including shadow AI.
HoundDog.ai today made generally available a namesake static code scanner that enables security and privacy teams to enforce guardrails on sensitive data embedded in large language model (LLM) prompts or exposed artificial intelligence (AI) data sinks, such as logs and temporary files, before any code is pushed to production. Company CEO Amjad Afanah said the HoundDog.ai scanner enables DevSecOps teams to embrace a privacy-by-design approach to building applications. The overall goal is to enable organizations to shift more responsibility for privacy left toward application development teams as code is being written, he added.
Since its initial availability last year, HoundDog.ai has already been used to scan more than 20,000 code repositories, from the first line of code using IDE extensions for VS Code, JetBrains and Eclipse to pre-merge checks in continuous integration (CI) pipelines. The approach has saved early adopters of HoundDog.ai thousands of engineering hours per month by eliminating reactive and time-consuming data loss prevention (DLP) remediation workflows.
Finally, HoundDog.ai will also generate audit-ready reports that map where sensitive data is collected, processed and shared, including through AI models, for Records of Processing Activities (RoPA) and Privacy Impact Assessments (PIAs) that are pre-populated with detected data flows and privacy risks that can be tailored for specific regulatory frameworks, such as the General Data Protection (GDPR) or Healthcare Information Portability and Accountability Act (HIPAA).
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