Week 6: AI & LLM Integration for Smarter Threat Detection 🔐

We are excited to share our latest progress on MIRA, our AI-assisted cybersecurity assessment tool. Our focus has been on enhancing MIRA with a Large Language Model (LLM) integration. Here’s a detailed update on our recent work.

Backend Setup ⚙️

To set up our backend API, we utilized Hono, a lightweight and edge-ready web framework. Known for its efficiency and simplicity, Hono is the perfect fit for our needs. We paired it with Bun, a high-performance JavaScript runtime, known for its unparalleled speed. This combination has provided us with a responsive and high-performance environment for our application.

Key Benefits:

• Hono: Offers a straightforward and efficient way to create web applications and APIs.
• Bun: Ensures faster execution times and improved performance for JavaScript applications.

Connecting to OpenAI 🌐

The core of our integration involved setting up the OpenAI service. We used TypeScript to define interfaces for interacting with the ChatCompletion endpoint. These interfaces allowed us to structure requests and responses with precise type definitions, ensuring compatibility with the OpenAI API.

Request Structure:

• ChatCompletionRequest Interface: Defined the interface to include parameters such as model, messages, and max_tokens.
  • Ensures requests are correctly formatted.
  • Contains all the necessary information for the OpenAI API.

Response Handling:

• ChatCompletionResponse Interface: Created the interface to handle the responses from OpenAI.
  • Defines expected data, such as choices and message content.
  • Makes it easy to parse and utilize the responses.

End-to-End Flow 🔄

To provide a seamless user experience, we designed an end-to-end flow for handling user input and generating AI-assisted responses.

Steps:

• API Endpoint:
  • Created an endpoint in our Hono app to accept user input and forward it to OpenAI’s ChatCompletion endpoint for processing.
• Processing User Input:
  • Structured the user input into a messages array, maintaining conversation context.
• OpenAI API Call:
  • Used Bun’s native fetch to make the API call, passing the structured request to OpenAI.
• Response Formatting:
  • Parsed and formatted the response to ensure easy consumption before sending it back to the client.

TypeScript for Safety and Clarity 🛡️

Using TypeScript has provided us with strong safety guarantees and improved clarity in our development process. By defining custom types for both requests and responses, we minimized runtime errors and enhanced maintainability.

Advantages:

• Type Definitions: Ensures our code is type-safe and less prone to errors.
• Improved Maintainability: Clearly defined types make our codebase easier to understand and maintain.
• Reduced Runtime Errors: TypeScript’s static type checking helps catch errors during development, leading to more reliable code.

✨ What’s Next?

In the next blog, we’ll dive into how exactly the scanning process works and the tools we are using for that. Stay tuned for more updates on how we’re bringing our vision to life!

⚙️ Together, let’s innovate, impact, and inspire.