Guests: Amjad Afanah | Asif Bhattik | Dawei Feng
Companies: HoundDog.ai | Replit 
Show: An Eye On AI
Topic: Application Security

AI-generated software is no longer a futuristic idea — it’s a daily reality. Anyone can now build full-stack applications in minutes using natural language instead of code. But as millions of these apps begin touching healthcare data, financial records, and sensitive customer information, one problem has become impossible to ignore: privacy leaks are being shipped into production at unprecedented speed. In this conversation, HoundDog.ai Founder & CEO Amjad Afanah joins Replit’s Asif Bhattik and Dawei Feng to explain how their new integration brings privacy-by-design into the foundation of AI-driven development.

AI-native development platforms like Replit have changed everything about how software gets created. With more than 45 million users building apps, agents, and games using plain English prompts, the barrier to software creation has collapsed. But as Asif Bhattik from Replit points out, the users building these apps are no longer traditional developers. They’re physicians prototyping medical tools, lawyers creating workflow assistants, small business owners automating operations, and creators exploring ideas. Most have no formal background in cybersecurity or data protection.

That’s the challenge HoundDog.ai was built to solve. As Amjad Afanah explains, modern privacy teams are overwhelmed not by compliance paperwork but by visibility. Organizations must document how sensitive data is processed, stored, and shared. But with today’s pace of development and explosion of SDKs, plugins, and AI integrations embedded inside code, most privacy tooling is reactive. Platforms typically scan data after it has already been collected in production. By then, the risk already exists. “These tools often miss the SDKs and abstractions that developers embed in code where data may be flowing to AI integrations or third-party services,” Afanah says.

HoundDog.ai flips that model. It uses static code scanning to analyze sensitive data flows as the code is being written. Instead of discovering an issue after deployment, developers — and importantly, privacy teams — see risks the moment an app is generated. This becomes especially powerful inside Replit’s AI app generation workflow. A user can describe an idea like “Build a medical application that collects symptoms,” generate a full-stack app, and immediately run a security scan. Now, privacy is scanned at the same time. The tool checks logs, files, local storage, API calls, AI integrations, and third-party SDKs for potential exposure.

This isn’t hypothetical. As Replit engineer Dawei Feng notes, doctors using Replit’s AI agents to build medical apps may unknowingly send patient data to destinations like OpenAI or Datadog. “Nobody wants their patients’ medical conditions logged forever or sent somewhere unintended,” he says. By catching data flows from the earliest moment, HoundDog.ai prevents these risks before they appear in production systems.

The value becomes even clearer with regulated industries. Healthcare and finance aren’t simply concerned with best practices — they’re bound by GDPR, HIPAA, and strict regional privacy laws. A misplaced log or an overly broad API request isn’t just a privacy concern but a legal and compliance issue. As Bhattik notes, domain experts such as doctors are highly skilled in their fields but not privacy specialists. They need built-in guardrails that warn them when something crosses lines they may not even be aware of.

Replit has long invested in security features like its secrets management tooling, which keeps API keys out of user repositories and chat windows. The new privacy-by-design integration extends this philosophy. If an AI-generated app embeds an API key in code, the system flags it and helps automatically move it into a secure secrets panel. If sensitive data is being sent to an AI model, creators receive a warning that reminds them to ensure they have the legal basis for such processing and that it aligns with their privacy notice.

A concrete example makes this even clearer. Imagine a physician building a prototype diagnostic helper. The code collects a patient’s symptoms and sends them to an LLM for suggestions. While the creator believes they are only sending a condition name, the generated code may also include name fields or identifiers. Without static scanning, that data could leak. With HoundDog.ai, it’s flagged before deployment and can be removed or anonymized. This prevents unintended compliance violations and protects patient trust.

Afanah also emphasizes that this integration is only Phase 1. Today, it highlights privacy leaks in logs, files, and outbound requests. The next phase aims to provide full sensitive data maps across all AI-generated apps inside an organization. For enterprises with many developers building many apps, keeping track of data flows becomes increasingly complex. A future where Replit deploys an app only after passing a privacy gate — similar to CI/CD security checks — is already in motion. As Feng confirms, privacy scanning will soon be part of the deployment workflow as well, ensuring no app with active leaks reaches production.

This matters because AI app generation is accelerating faster than most governance frameworks can keep up. Developers are shipping at a velocity enterprises could not have imagined five years ago. Without automated guardrails, privacy risks will scale just as fast. “AI coding platforms are evolving at insane speed,” Afanah says. “But trust depends on platforms giving organizations confidence that apps are secure, compliant, and documented.”

The cultural shift is already underway. Security-by-design became standard only after years of breaches and industry pressure. Privacy-by-design is now following the same trajectory — except this time, the change is happening during the birth of a new development model, not after it. Tools like HoundDog.ai are emerging not to make privacy optional, but to make it automatic.

For enterprises adopting AI-generated software, Afanah’s advice is clear: use tools that reveal your data flows early, document them continuously, and empower developers — even nontraditional ones — with guardrails that keep them compliant. Whether apps are built by software engineers or business users, privacy must move to the front of the pipeline.

In this conversation, the message is unmistakable: as AI democratizes development, privacy is no longer something teams can bolt on later. It must be embedded into the workflow itself, and partnerships like HoundDog.ai and Replit show how the industry is beginning to meet that responsibility.