HoundDog.ai today announced the general availability of its expanded privacy-by-design static code scanner, now purpose-built to address privacy risks in AI applications. Addressing the growing concerns around data leaks in AI workflows, the new release enables security and privacy teams to enforce guardrails on the types of sensitive data embedded in large language model (LLM) prompts or exposed in high-risk AI data sinks, such as logs and temporary files, all before any code is pushed to production and privacy violations occur. HoundDog.ai is a privacy-focused static code scanner designed to identify unintentional mistakes by developers or AI-generated code that could expose sensitive data such as personally identifiable information (PII), protected health information (PHI), cardholder data (CHD) and authentication tokens across risky mediums like logs, files, local storage and third-party integrations. Since its launch from stealth in May 2024, HoundDog.ai has been adopted by a growing number of Fortune 1000 organizations across finance, healthcare and technology. It has scanned more than 20,000 code repositories for its customers, from the first line of code using IDE extensions for VS Code, JetBrains and Eclipse to pre-merge checks in CI pipelines. The platform has prevented hundreds of critical PHI and PII leaks, saved thousands of engineering hours per month by eliminating reactive and time-consuming data loss prevention (DLP) remediation workflows, ultimately saving millions of dollars.

Full report : HoundDog.ai Launches Industry’s First Privacy-by-Design Code Scanner for AI Applications.

For more see the OODA Company Profile on HoundDog.ai.