This is a submission for the Permit.io Authorization Challenge: AI Access Control
What I Built
I built an AI Content Assistant, is an AI-powered content summarization tool enhanced with fine-grained access control using Permit.io. It allows users to submit content for summarization using AI (mocked in this version), while only authorized roles such as admins can review and publish the generated summaries. The goal is to demonstrate how externalized authorization can be used to secure AI-driven workflows in a real-world application.
Problem It Solves:
In many content-heavy environments—like media companies, knowledge bases, or educational platforms—AI can streamline the summarization process. However, there’s often a need to ensure that only approved personnel can review or publish AI-generated output. This project solves that by integrating Permit.io to manage access control declaratively and securely, ensuring users only perform actions they're authorized for. It helps prevent unauthorized publishing of unreviewed AI content while maintaining a seamless workflow between users and reviewers.
Demo
Project Repo
My Journey
Process & Challenges:
My process began by outlining the core functionality of the AI Content Assistant: allowing users to submit text content for summarization using AI, and enforcing fine-grained authorization through Permit.io. The goal was to build two distinct experiences—one for content users, and another for admins/reviewers who manage approval.
Key steps I followed:
Project Setup
I initialized a modern frontend with Vite, React, TypeScript, and
TailwindCSS, and used Node.js with Express for the backend. We chose
Render for backend deployment and Vercel for the frontend for quick and
cost-effective hosting.
Permit.io Authorization Integration
Using Permit.io’s SDK, we created roles (user, admin) and enforced permissions through declarative permit.check() calls. For example, users could only summarize content, while admins were the only ones authorized to review or publish.
AI Integration & Challenge Mitigation
Initially, I planned to use OpenAI's API for summarization, but ran into
quota limitations and couldn’t afford a paid plan. To overcome this, I
mocked the AI responses in a realistic way, allowing the core flow of
the app to remain intact and testable.
Integrating Fine-Grained Authorization with Permit.io
Used permit.check() to enforce who can:
Submit content (user)
Review and publish summaries (admin)
User & Admin Interfaces
I built:
A **User Dashboard **where content can be submitted and feedback is received on submission.
An Admin Dashboard where pending summaries are listed with options to approve or reject.
UI Feedback and UX Polish
We implemented alerts to notify users when submissions were successful or when errors occurred, improving usability.
Testing Authorization Paths
A big part of the challenge was validating that Permit.io’s rules were correctly enforced. We tested each API endpoint with both roles to confirm that unauthorized actions were blocked as expected.
🚧 Challenges Faced & How We Solved Them
API Quota Exceeded (OpenAI):
Once our OpenAI usage quota was exceeded, the backend returned 429 errors. Rather than halting progress, we mocked the summarization logic to simulate AI behavior, keeping the project on track.
Role-Based Access Control Logic:
Ensuring that authorization checks were properly scoped to each endpoint required close coordination between frontend logic and Permit.io policy definitions. Debugging was made easier using Permit.io’s activity logs and Policy Studio.
Frontend Feedback on Submission:
Initially, users had no confirmation that their content had been processed. We resolved this by adding alert modals and real-time UI updates.
Authorization for AI Applications with Permit.io
🔐 How We Built Authorization Controls Specifically for AI-Based Tools or Features
In the AI Content Assistant, I applied fine-grained access control using Permit.io to ensure that AI-powered features like content summarization and publishing could only be accessed by authorized roles. Here's how we structured and implemented the authorization:
🧩 Roles and Permissions Design
I defined two roles within the Permit.io Policy Editor:
**User:**
Has permission to summarize content using the AI feature.
Cannot view, approve, or publish summaries.
**Admin:**
Can access the /review and /review/:id endpoints.
Has permissions to review, approve, or reject AI-generated
summaries.
Can view published content.🧠 AI Feature Protection with **permit.check()**
At every backend route related to AI operations, we wrapped access
in _permit.check()_ calls. For example:```
const allowed = await permit.check(user, "summarize",
"content");
if (!allowed) return res.status(403).json({ error: "Not
authorized to summarize content" });
```This ensures that even if a user tries to access a protected route manually (e.g., via Postman or dev tools), they’ll be denied unless their role explicitly allows it.
I used this same pattern for:
_/ai/summarize _→ to protect the AI summarization endpoint.
/review and /review/:id → to ensure only admins can review and manage content.
_/published _→ to restrict access to approved summaries only to authorized roles
🔍 Why Externalized Authorization Was Critical
Instead of hardcoding access rules into the application logic, Permit.io allowed us to declaratively manage permissions. This gave us several benefits:
We could easily update or test roles without changing the backend code.
Authorization logic was consistent and transparent across the app.
It helped demonstrate real-world, secure handling of AI tools, which is especially important for tools that can influence published content or decisions.
✅ Summary
Using Permit.io for authorization allowed us to:
Secure our AI-powered routes without writing complex custom middleware.
Enforce least-privilege access.
Easily scale the permission system as we add more roles or features.
