hounddog.ai Today we announced the general availability of Static Code Scanner extensions designed with designs currently built to address the privacy risks of AI applications. With a new release addressing growing concerns about data leaks in AI workflows, security and privacy teams can implement GuardRails on types of sensitive data embedded in high-risk AI data sinks such as logs and temporary files, or exposed to high-risk AI data sinks such as logs and temporary files, before code occurs in production and privacy violations.
hounddog.ai is a privacy-centric static code scanner designed to identify unintended mistakes by developers that can publish sensitive data such as Personally Identifiable Information (PII), Protected Health Information (PHI), Card Holder Data (CHD), Risk Media, Logs, Local Storage, and Authentication Tokens such as Active Integration by Third Party.
Since its launch from Stealth in May 2024, HoundDog.AI has seen an increase in the number of Fortune 1000 organizations across finance, healthcare and technology. I used VS Code, Jet Brain and Eclipse IDE extensions to scan over 20,000 code repositories for customers from the first line of code using CI pipeline pre-registration checks. The platform saved thousands of engineering time a month and ultimately millions of dollars by preventing hundreds of critical PHI and PII leaks and eliminating reactive and time-consuming data loss prevention (DLP) remediation workflows.
New: Built for AI privacy
The updated hounddog.ai platform addresses growing concerns about data leaks in AI workflows and enables engineering and privacy teams to “shift left privacy” by embedding detection, enforcement and audit-ready reports directly into the development process.
“The explosion of AI integration in application development allows us to see sensitive data passed through LLM prompts, SDKs and open source frameworks without visibility or enforcement,” he said. Amjad AfanahCEO and co-founder of hounddog.ai. “We have expanded our platform to meet this new challenge head on by providing teams with a way to proactively control the privacy of their AI applications without delaying innovation.
New features for AI privacy enforcement
Traditional AI security tools usually work at runtime and often lack embedded AI integration, shadow use, and organization-specific sensitivity data. Without code-level visibility, it is almost impossible to understand how that data was entered into an AI model or a prompt.
An extended HoundDog.AI privacy-centric code scanner for AI applications addresses these limitations.
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Discover AI Integration – Both direct integrations (such as Openai and humanity) and indirect (such as Langchain, SDKs and Libraries) automatically detect all AI usage as part of AI governance efforts, including Shadow AI.
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Sensitive data flows across the boundaries of the transformation layer and file. Tracks over 150 sensitive data types including PII, PHI, CHD and authentication tokens, and even risky sinks such as LLM prompts, prompt logs, temporary files and more.
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Blocking Unauthorized Data Types – Apply AllowLists to enforce permitted data types in LLM prompts and other dangerous data sinks, and automatically block unsafe changes to pull requests to maintain compliance with data processing agreements.
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Generate Audit-enabled Reports – Create evidence-based data maps that show where sensitive data is collected, processed and shared, including AI models. We have created audit response records for Processing Activities (ROPA) and Privacy Impact Assessment (PIA) to adopt risks that detectable data flows and privacy risks tailored to GDPR, CCPA, HIPAA, SCF and other regulatory frameworks.
Real-world impact
PioneerDev.ai, a software development company specializing in AI and SAAS web applications, has deployed hounddog.ai to protect its AI-powered healthcare registration platform. Using hounddog.ai, the Pioneerdev.ai team was able to automatically detect privacy violations with both direct and indirect AI integrations, such as LLM prompts, logs and other high-risk areas. By configuring Allowlists that reflect our privacy policy, PioneerDev.AI was able to prevent unsecure data sharing prior to deployment. The HoundDog.AI platform also automates the generation of privacy impact assessments with mapped data flows and flagged risks.
“IDC research shows that protecting sensitive data processed by AI systems is the biggest security concern when building AI capabilities into applications. In many cases, these models are integrated into the codebase without the knowledge or approval of the security and privacy team. Such private integrations allow sensitive information, including personal data, to large language models and other AI services.” Katie NortonIDC Research Manager, Developer, Software Supply Chain Security. “Detecting these connections and understanding the data that code accesses before it reaches production is becoming a priority, and minimizing proactive data is emerging as an important complement to traditional runtime detection and response.”
“Our clients trust us to protect our most sensitive data, and with increasing use of LLM integrations in custom applications we develop, the risk that data will be exposed through prompts or logs has become a serious concern,” he said. Stephen SefariCEO of Pioneerdev.ai. “A single leak can undermine compliance, damage trust and cause costly repairs. HoundDog.ai has given us the visibility and control needed to actively prevent these risks and maintain privacy commitments from the start.”
Available now
HoundDog.ai also announced the general availability of cursor extensions, allowing developers to embed them on a privacy-by-privacy basis in AI-generated apps from day one. Both the CLI scanner and cursor extensions are available free of charge in Python and JavaScript/Typescript projects.
For more information, please visit www.hounddog.ai
About hounddog.ai
hounddog.ai helps organizations to actively detect and prevent overexposure of sensitive data in high-risk media that can lead to privacy violations. By incorporating detection, enforcement, and audit prepared reports directly into the development process, HoundDog.ai streamlines privacy compliance from day one. That domain-specific static code scanner analyzes the code from the IDE to the CI and identifies sensitive data that handles risk before the code is deployed. Designed to catch unintended mistakes from developers or AI-generated code, scanners flag exposures of PII, PHI, CHD, and authentication tokens. For more information, please visit www.hounddog.ai.
