HoundDog.ai announced on August 21, 2025, the general availability of its expanded privacy-by-design static code scanner, tailored for AI applications. This innovative tool helps enterprises proactively address privacy risks in AI workflows, preventing sensitive data leaks before code reaches production.
Launched August 21, 2025; targets AI privacy risks in development.
Scans for PII, PHI, CHD, and authentication tokens in code.
Detects shadow AI and tracks 150+ sensitive data types.
Integrates with IDEs (VS Code, JetBrains, Eclipse) and CI pipelines.
Saves thousands of engineering hours, prevents costly remediation.
Trusted by Fortune 1000 firms in finance, healthcare, and tech.
HoundDog.ai’s static code scanner identifies unintentional data exposures in AI prompts, logs, files, and third-party integrations, addressing risks from developer or AI-generated code. Since its stealth launch in May 2024, it has scanned over 20,000 repositories, preventing hundreds of critical PII and PHI leaks for Fortune 1000 clients across finance, healthcare, and technology, saving millions in remediation costs.
The updated platform, purpose-built for AI applications, tackles the growing issue of data leaks in LLM prompts and high-risk data sinks. “With the explosion of AI integrations, we’re seeing sensitive data passed through prompts and SDKs without visibility,” said Amjad Afanah, CEO of HoundDog.ai. Key features include:
AI Integration Discovery: Detects direct (e.g., OpenAI, Anthropic) and indirect (e.g., LangChain) AI usage, including shadow AI.
Sensitive Data Tracing: Tracks over 150 data types (PII, PHI, CHD) across code paths to risky sinks like prompt logs.
Policy Enforcement: Applies allowlists to block unapproved data in pull requests, ensuring compliance with GDPR, CCPA, and HIPAA.
Audit-Ready Reporting: Generates evidence-based data maps and Privacy Impact Assessments (PIAs) for regulatory compliance.
PioneerDev.ai, an AI and SaaS development firm, used HoundDog.ai to secure a healthcare enrollment platform, automatically detecting privacy violations in LLM prompts and logs. By enforcing allowlists, they prevented unsafe data sharing pre-deployment. “HoundDog.ai gave us the visibility and control we needed to proactively prevent risks,” said Stephen Cefali, CEO of PioneerDev.ai.
“Protecting sensitive data in AI systems is the top security concern,” noted Katie Norton, IDC Research Manager. HoundDog.ai’s shift-left approach addresses shadow AI and data leaks early, complementing runtime tools. Its integration with IDEs and CI/CD pipelines ensures seamless adoption, with a free CLI scanner and Cursor extension for Python and JavaScript/TypeScript projects.
HoundDog.ai’s privacy-by-design scanner redefines AI governance, enabling enterprises to innovate securely while meeting stringent compliance demands.
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.
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