Parth Sehgal shares how the GitHub engineering team used GitHub Copilot coding agents to automate and scale secret protection, offering practical lessons for engineering teams.

How GitHub Copilot Accelerated Secret Protection Engineering

Accidentally committing secrets to source code is a common risk for developers. GitHub Secret Protection addresses this by alerting teams about exposed credentials and offering features to block such leaks. Key functions include push protection that prevents sensitive data from entering the codebase, validity checks to help prioritize urgent exposures, provider notifications for rapid responses, Copilot-assisted secret scanning even for generic secrets, and support for custom detection patterns.

Framework-Driven Expansion with Copilot

The engineering team, led by Parth Sehgal, aimed to expand the validity check system’s coverage, originally built to validate the most commonly leaked credentials like AWS keys and Slack tokens. The challenge was extending this to less common token types efficiently.

Their workflow involved:

Researching the right API endpoints for token validation
Coding validators for each new type
Darkshipping (deploying validators in observation mode)
Fully releasing validated checkers

While research and nuanced test case work remained manual, the coding and release automation became targets for AI assistance. The team tried using Copilot as a coding agent for automating code changes, using manually crafted, information-rich issues as prompts for Copilot in GitHub workflows. While Copilot sometimes struggled with external documentation, providing more detailed prompts improved its effectiveness.

Copilot auto-generated pull requests, with human engineers reviewing and integrating the output. The team used darkshipping to safely observe new validators before deployment, enabling safe, incremental improvements. Final tweaks and releases also involved Copilot handling configuration changes.

Outcomes and Insights

Before integrating Copilot, the team supported 32 partner token types. After leveraging Copilot, and, notably, with contributions from interns, onboarding nearly 90 new token types took only a few weeks. Key lessons learned include:

Automation suits repeatable workflows: Well-defined, repetitive processes benefit most from coding agents
Human-in-the-loop is critical: Human engineers must review and verify AI-generated code
Prompt quality matters: The more context given to Copilot, the better the result
Iterate and refine: Both code and prompting strategies needed ongoing improvement
Parallelization: Copilot enabled the team to distribute work across multiple agents and developers, vastly increasing throughput

Advice for Teams Looking to Automate

Identify repeatable engineering workflows that could benefit from automation
Provide detailed, example-rich prompts for coding agents
Maintain collaborative review and QA processes for any AI-generated output

Copilot didn’t replace the team’s expertise, but multiplied their capacity, freeing engineers to focus on complex tasks. The team’s success offers a model for integrating AI coding assistants in other framework-driven engineering environments.

“Copilot delivers speed and scale, but it’s no replacement for human engineering judgment. Always review, test, and verify the code it produces.” – Parth Sehgal

This post appeared first on “The GitHub Blog”. Read the entire article here