API Privacy & Governance Copilot

A comprehensive solution for discovering, scanning, and governing APIs to prevent data leaks and ensure compliance.

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🧠 Inspiration

Modern engineering teams ship APIs fast — but rarely know which endpoints leak PII, which lack authentication, or where sensitive data ends up across logs and internal tools. Most privacy issues are discovered after something goes wrong.

We wanted to build an autonomous agent that acts like a Privacy Engineer + API Auditor + Governance Analyst, but one that works in real time and without human babysitting.

That’s how API Privacy & Governance Copilot was born.

🔍 What it does

The copilot takes a docs URL or app URL and:

  1. Discovers API endpoints automatically using headless browser automation (LightPanda).
  2. Scans each endpoint for privacy risks such as PII leaks (emails, phone numbers, IDs).
  3. Applies privacy and governance policies sourced from Sanity CMS.
  4. Tokenizes and safely stores PII using Skyflow’s Data Privacy Vault.
  5. Learns over time by storing incidents semantically in RedisVL + Agent Memory Server.
  6. Surfaces similar past incidents to help engineers understand recurring patterns.
  7. Presents everything in a clean dashboard for instant review and demoability.

It’s a plug-and-play privacy officer for your API surface.

🛠️ How we built it

Frontend: A fast, static React + TypeScript SPA that interacts with a simple backend API (mocked for demo, swappable later).

Backend (planned & scaffolded with Claude):

  • Endpoint discovery orchestrated through LightPanda.
  • API testing and structuring modeled via Postman collections.
  • PrivacyVault abstraction using Skyflow (real or mock).
  • Sanity-backed policy engine for privacy/governance rules.
  • RedisVL + Agent Memory Server for semantic memory of past incidents.
  • MCPTotal configuration for secure MCP hosting and tool orchestration.

The architecture is loosely coupled, allowing each tool to plug in cleanly with minimal boilerplate.

🧩 Challenges we ran into

  • Designing autonomous API discovery — parsing docs and UIs consistently is trickier than expected.
  • PII detection without false positives — regex-based detection required careful tuning.
  • Modeling policies cleanly — building a flexible rule format that Sanity could store and the backend could enforce.
  • Semantic memory — designing embeddings and schema that let RedisVL retrieve meaningful similar incidents.
  • Clean frontend storytelling — ensuring the UI clearly communicates what the agent is doing behind the scenes in only a few interactions.

🏆 Accomplishments that we're proud of

  • A fully functional static UI that demos the entire flow clearly for judges.
  • Seamless integration story across six sponsor tools in a meaningful and coherent way.
  • A modular code structure that allows Claude Code to generate the full backend quickly.
  • A compelling hackathon narrative: “This agent is your autonomous Privacy Officer.”
  • The ability to show discovery → scanning → policy checks → PII tokenization → memory lookup in under 3 minutes.

📚 What we learned

  • How to combine browser automation, API testing, privacy vaults, CMS-based policies, and vector memory into one unified workflow.
  • How MCP-based tools can be orchestrated securely using MCPTotal.

  • How to build AI-compatible systems where each component is:

    • Swappable
    • Observable
    • Governed
    • And safe for real-world privacy use cases.

  • How to design frontends that tell a complex story in a simple, elegant way.

🚀 What's next for API Privacy & Governance Copilot

  • Full backend implementation with Claude Code generating FastAPI/Express services.
  • Real LightPanda integration for dynamic endpoint extraction.
  • Real Skyflow vault operations with live tokenization.
  • Sanity-based policy editing UI so compliance teams can update rules visually.
  • LLM-powered explanations that turn incidents into human-friendly reports.
  • CI integrations where every pull request auto-scans new endpoints for privacy regressions.

Ultimately, the goal is to evolve this into a continuous privacy observability platform that protects teams from compliance failures before they ship.

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