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Previously, we implemented a basic testing framework to start handling data errors, corrupt data, and edge cases. In Part 4, we're going to kick it up a notch. It's time to implement robust error handling for each discrete task!

We left off having implemented a basic testing framework to drive code implementation for edge cases and errors. Now we're going to build up that framework by developing tests specifically for each discrete function and attempting to ensure we have solid error handling for anything that comes our way. We're also going to implement log rotation while we're at it, to ensure that there's a reasonable amount of logs captured without taking up too much drive space. Let's get going!

Previous entries in this series covered the initial tool setup and testing of the environment, establishing the structure of the project, initial tests, and connecting to APIs. In this third entry, we're going to enhance our testing approach through a Test-Driven Development (TDD) methodology to build a more robust and reliable system.

Initially, our tests covered basic scenarios but didn’t fully account for real-world challenges like invalid configurations, API rate limits, and malformed data. We will expand our test cases significantly to address this, focusing on edge cases and error handling.

Part 1 of this series covered the initial tool setup, and testing of the environment. In Part 2, we're going to dive into structure, tests, and API connections.

In this post, I’m excited to dive into the development of IntelliSIEM’s data collection module. This step is crucial as it lays the foundation for how we will gather, process, and analyze threat data from various sources.

Welcome to the first post in my series chronicling the development of IntelliSIEM, a robust threat intelligence aggregator and reporter tool. In this post, I’ll guide you through setting up the development environment using Python 3.12 and PyCharm Professional.