A Proactive Approach to Preventing PII Data Leaks and Automating Privacy Compliance Workflows
About Us
HoundDog.ai helps organizations proactively detect and prevent the overexposure of sensitive data in high risk mediums that could lead to privacy violations. By embedding detection, enforcement, and audit ready reporting directly into the development process, HoundDog.ai streamlines privacy compliance from day one. Its domain specific static code scanner analyzes code from IDE to CI, identifying sensitive data handling risks before code is deployed. Designed to catch unintentional mistakes by developers or AI generated code, the scanner flags exposure of PII, PHI, CHD, and authentication tokens across often overlooked surfaces such as logs, files, local storage, third party SDKs, and AI specific mediums like LLM prompts and embedding stores, enabling true privacy by design at the code level.
Our Mission
HoundDog.ai’s mission is to operationalize Zero Trust through proactive data minimization by detecting and eliminating sensitive data risks early in development to prevent privacy violations and costly incidents. As AI and continuous delivery reshape software development, we provide a privacy focused static code scanner that embeds privacy by design into the development workflow. By tracing over 150 sensitive data types from IDE to CI/CD and enforcing allowlist based policies, HoundDog.ai helps organizations prevent exposure across overlooked surfaces such as logs, local storage, third party SDKs, LLM prompts, and embedding stores.
The Founding Team
Amjad Afanah
Amjad Afanah is a serial entrepreneur with a rich background in cybersecurity. He led his first company, DCHQ, a cloud management startup, to acquisition, and later founded APISec.ai, which developed one of the first API security scanners. Before founding HoundDog.ai, Amjad served as the VP of Product at Cyral, a data security platform that implements security controls on production data. His experience at Cyral, coupled with significant feedback from security and privacy teams frustrated by the prevalent reactive approach to data security and privacy—which often remains unaligned with evolving codebases—inspired him to start HoundDog.ai.
Joohwan Oh
Joohwan Oh is an experienced engineering leader skilled in both scaling services for millions of users and developing new software from scratch. Before joining HoundDog.ai, he was a founding engineer at Aktos, a FinTech startup focused on modernizing the accounts receivable management industry. Joohwan has also led key projects at prominent companies like Facebook, Amazon, and Instacart. Currently, he oversees the development of HoundDog.ai’s cloud platform and the essential AI workflows that significantly enhance our scanner’s accuracy and coverage.
Make Privacy-by-Design a Reality in Your SDLC
Shift Left on Privacy. Scan Code. Get Evidence-Based Data Maps. Prevent PII Leaks in Logs and Other Risky Mediums Early—Before Weeks of Remediation in Production.