HoundDog.ai Launches First Privacy-by-Design AI Code Scanner

ByRicardo

HoundDog.ai helps groups catch privateness violations in AI prompts and code earlier than they attain manufacturing, lowering compliance threat and audit ache.

HoundDog.ai right this moment introduced the final availability of its expanded privacy-by-design static code scanner, now purpose-built to deal with privateness dangers in AI purposes. Addressing the rising issues round knowledge leaks in AI workflows, the brand new launch allows safety and privateness groups to implement guardrails on the forms of delicate knowledge embedded in massive language mannequin (LLM) prompts or uncovered in high-risk AI knowledge sinks, similar to logs and non permanent recordsdata, all earlier than any code is pushed to manufacturing and privateness violations happen.

HoundDog.ai is a privacy-focused static code scanner designed to determine unintentional errors by builders or AI-generated code that would expose delicate knowledge similar to personally identifiable info (PII), protected well being info (PHI), cardholder knowledge (CHD) and authentication tokens throughout dangerous mediums like logs, recordsdata, native storage and third-party integrations.

Since its launch from stealth in Might 2024, HoundDog.ai has been adopted by a rising variety of Fortune 1000 organizations throughout finance, healthcare and expertise. It has scanned greater than 20,000 code repositories for its prospects, from the primary line of code utilizing IDE extensions for VS Code, JetBrains and Eclipse to pre-merge checks in CI pipelines. The platform has prevented a whole lot of crucial PHI and PII leaks, saved 1000’s of engineering hours monthly by eliminating reactive and time-consuming knowledge loss prevention (DLP) remediation workflows, in the end saving tens of millions of {dollars}.

What’s New: Constructed for AI Privateness

The up to date HoundDog.ai platform addresses rising issues round knowledge leaks in AI workflows, enabling engineering and privateness groups to “shift privateness left” by embedding detection, enforcement and audit-ready reporting immediately into the event course of.

“With the explosion of AI integrations in utility improvement, we’re seeing delicate knowledge handed by means of LLM prompts, SDKs and open supply frameworks with out visibility or enforcement,” mentioned Amjad Afanah, CEO and co-founder of HoundDog.ai. “Now we have expanded our platform to fulfill this new problem head-on by giving groups a solution to proactively management privateness in AI purposes with out slowing down innovation. This shift left method redefines how organizations detect and stop delicate knowledge exposures within the age of LLMs, steady deployment and rising regulatory stress.”

New Capabilities for AI Privateness Enforcement

Conventional AI safety instruments usually function at runtime, typically lacking embedded AI integrations, shadow utilization and organization-specific delicate knowledge. With out code-level visibility, understanding how that knowledge entered an AI mannequin or immediate is sort of unattainable.

The expanded HoundDog.ai privacy-focused code scanner for AI purposes addresses these limitations by:

  • Discovering AI integrations – Routinely detecting all AI utilization as a part of your AI governance efforts, together with shadow AI, throughout each direct integrations (similar to OpenAI and Anthropic) and oblique ones (together with LangChain, SDKs, and libraries).
  • Tracing delicate knowledge flows throughout layers of transformation and file boundaries – monitoring over 150 delicate knowledge sorts, together with PII, PHI, CHD, and authentication tokens, all the way down to dangerous sinks similar to LLM prompts, immediate logs and non permanent recordsdata.
  • Blocking unapproved knowledge sorts – Making use of allowlists to implement which knowledge sorts are permitted in LLM prompts and different dangerous knowledge sinks, and mechanically blocking unsafe modifications in pull requests to keep up compliance with Information Processing Agreements.
  • Producing audit-ready stories – Creating evidence-based knowledge maps that present the place delicate knowledge is collected, processed and shared, together with by means of AI fashions. Producing audit-ready Data of Processing Actions (RoPA) and Privateness Affect Assessments (PIAs), prepopulated with detected knowledge flows and privateness dangers aligned with GDPR, CCPA, HIPAA, the SCF and different regulatory frameworks.

Actual-World Affect

PioneerDev.ai, a software program improvement agency specializing in AI and SaaS internet purposes, deployed HoundDog.ai to safe an AI-powered healthcare enrollment platform. Utilizing HoundDog.ai, the PioneerDev.ai group was in a position to mechanically detect privateness violations throughout each direct and oblique AI integrations, together with LLM prompts, logs and different high-risk areas. By configuring allowlists that mirrored their privateness insurance policies, PioneerDev.ai was in a position to forestall unsafe knowledge sharing earlier than deployment. The HoundDog.ai platform additionally automated the technology of Privateness Affect Assessments, full with mapped knowledge flows and flagged dangers.

“IDC analysis finds that defending delicate knowledge processed by AI techniques is the highest safety concern when constructing AI capabilities into purposes. In lots of circumstances, these fashions are being built-in into codebases with out the data or approval of safety and privateness groups — a follow sometimes called “shadow AI.” Such undisclosed integrations can expose delicate info, together with private knowledge, to massive language fashions and different AI companies,” mentioned Katie Norton, Analysis Supervisor, DevSecOps and Software program Provide Chain Safety at IDC. “Detecting these connections and understanding the information they entry earlier than code reaches manufacturing is changing into a precedence, with proactive knowledge minimization rising as an essential complement to conventional runtime detection and response.”

“Our shoppers belief us to guard their most delicate knowledge, and with the rising use of LLM integrations within the customized purposes we develop, the danger of that knowledge being uncovered by means of prompts or logs grew to become a severe concern,” mentioned Stephen Cefali, CEO of PioneerDev.ai. “A single leak may undermine compliance, injury belief and set off pricey remediation. HoundDog.ai gave us the visibility and management we wanted to proactively forestall these dangers and uphold our privateness commitments from the beginning.”

Out there Now

HoundDog.ai additionally introduced the final availability of its Cursor extension, enabling builders to embed privacy-by-design in AI-generated apps from day one. Each the CLI scanner and Cursor extension can be found free for Python and JavaScript/TypeScript tasks.

The publish HoundDog.ai Launches First Privacy-by-Design AI Code Scanner first appeared on AI-Tech Park.

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