A number of years ago, we transitioned internally to GitHub and have since seen significant benefits. In this post, we want to share how team leads, and development managers can leverage GitHub’s AI-driven features to enhance development practices, improve security, and streamline workflows.
We’ve found that these stories resonate with customers in the operate phase of their custom application lifecycle, where post-project teams often struggle to manage changes. Whilst they are making code changes that resolve bugs, or even add minor improvements, they lack the resourcing to generating comprehensive release notes for change control workflows and maintaining visibility into application performance and security impacts are common challenges.
Enterprise Managed User (EMU) Instance in GitHub
For organisations migrating from Azure DevOps, a tightly integrated user authentication system backed by Entra ID is often expected. In such cases, implementing an Enterprise Managed User (EMU) instance of GitHub, with SSO and SCIM integration to Entra ID, is the best approach. The standard Enterprise GitHub offering (non-EMU) has its own IAM system. While it supports SSO with Entra ID, user provisioning and deprovisioning workflows remain separate from existing enterprise identity systems.
By utilising an Enterprise Managed User (EMU) instance, we ensure:
- Exclusive access control – Only users provisioned through Azure AD can access repositories.
- Centralised security management – Access permissions are governed by enterprise security policies.
- Restricted external access – Public GitHub accounts cannot participate, enhancing security and compliance.
Secure and Efficient Development Using Codespaces
GitHub Codespaces is a cloud-based development environment that enables teams to work securely and efficiently without the overhead of maintaining local development setups. It provides a tightly managed infrastructure, defined as code, ensuring consistency and security across development environments.
For teams supporting multiple applications, each application can have its own isolated Codespace. This allows developers to work with different framework versions and platform stacks without conflicts. Teams no longer need to manage complex developer PC build instructions or maintain dedicated VMs for each application.
Developers now leverage GitHub Codespaces to write and test code directly in a cloud-based environment, offering:
- Standardised development setups, eliminating local dependency issues.
- Enhanced security, as code never resides on personal devices.
- Improved collaboration, allowing quick environment sharing.
AI-Driven Governance for Code Review and Security in Small Teams
GitHub’s AI-driven features are well documented, with numerous examples of Copilot generating complex classes and methods. However, for small development teams, the real value lies in AI’s ability to provide peer code reviews and generate summaries for development managers to better understand the scope and impact of changes.
Security, code quality, and compliance are critical in our development lifecycle. To ensure best practices, we leverage GitHub’s AI-powered governance tools, which provide automation and efficiency in small team environments:
- GitHub Copilot assists in writing secure and optimised code by identifying vulnerabilities and suggesting improvements.
- Code QL scans for security flaws, such as SQL injection risks and hardcoded credentials.
- AI-powered PR reviews reduce manual overhead by catching errors early and ensuring code consistency.
- Dependabot automatically scans and updates dependencies when vulnerabilities are detected, mitigating security risks.
- Secret Scanning identifies hardcoded sensitive information in repositories and provides remediation steps.
By integrating these AI-driven features, small teams can enhance code quality, streamline review processes, and maintain high security standards without additional resource overhead.
Next Steps and Considerations
Curious about how we implement GitHub in our own development workflows? We’d love to share insights on our software delivery process and address any questions you have about AI data privacy in GitHub. Reach out to start the conversation!