Here’s an in-depth exploration of GitLab’s innovative CI/CD platform, which is reshaping how we build software by seamlessly blending AI and cybersecurity capabilities. The focus is on GitLab’s AI services and their integration with cloud platforms.
GitLab is a renowned internet-based Git repository administrator that offers an all-encompassing CI/CD platform. Its diverse feature set has fundamentally altered the way in which developers collaborate on software deployments. At its core, GitLab’s CI/CD platform serves as an integrated toolchain process, connecting various tools to handle all life cycle activities of software development. This integration streamlines how software modifications are constructed, tested, and deployed. By integrating the CI/CD process into the version control system, GitLab enables teams to maintain an agile development workflow. With faster feedback cycles and consistent deployments, teams can accelerate their software delivery.
Continuous integration (CI) in GitLab
Continuous integration within GitLab involves the frequent integration of code changes into a central hub. GitLab’s CI system offers a wide range of built-in features, including:
Pipelines: Using ‘.gitlab-ci.yml’ files, developers may create tailored pipeline configurations that outline the activities required to construct, evaluate, and release applications.
Runners: These runners perform CI/CD functions and can be tailored to individual projects, enabling versatility and extensibility.
Parallel execution: CI/CD pipelines optimise build times and enhance development efficiency through parallel job execution.
Continuous deployment (CD) in GitLab
Production environments receive the automatic release of code changes following successful CI testing. GitLab’s CD capabilities include:
Environment management: GitLab streamlines the administration of diverse ecosystems, including development, testing, and deployment settings, thus enabling maintenance of standardised setups.
Auto DevOps: By employing best practices, GitLab’s Auto DevOps function sets up CI/CD pipelines swiftly and with minimal effort.
Paradigm shift towards Auto DevOps
Auto DevOps boasts an AI-assisted functionality that scrutinises the codebase and establishes automatic CI/CD pipeline configurations. By integrating past data and machine learning models, Auto DevOps streamlines pipeline phases. This encompasses choosing the right runner types and tuning parameters depending on the project’s properties. Individualised CI/CD pipeline configurations are established using this method, eliminating the need for manual tweaking.
The Auto Remediation tool is intended to automatically repair failed CI/CD projects. Using AI insights, Auto Remediation provides customised advice or job retry suggestions, saving developers time and effort. This feature reduces the manual intervention required to address common build or test failures, promoting faster delivery of code changes.
GitLab’s platform offers extensive cybersecurity options for CI/CD pipelines. It prioritises cybersecurity-enabled features by incorporating robust security measures into its CI/CD platform, allowing developers to detect and mitigate security risks during development.
SAST vs DAST testing in GitLab AI services
The purpose of a static application security testing (SAST) tool is to analyse the source code of software applications to identify potential security vulnerabilities and coding errors. SAST is a type of white-box testing — it examines the internal structure and logic of the code without executing the application. The primary goal of SAST is to proactively detect security flaws early in the software development life cycle, allowing developers to address and fix these issues before the application is deployed or released to production. GitLab’s SAST tool performs a detailed review of the source code, locating security weaknesses like code injection, incorrect authentication, and XSS threats. SAST integration within the CI/CD pipeline helps ensure a secure software release by identifying and addressing security concerns earlier in development.
Dynamic application security testing (DAST) is a type of security testing that assesses web applications and APIs while they are running. Unlike SAST, which examines the source code of an application, DAST operates from the outside-in, simulating real-world attacks by interacting with the application through its front-end, just like an external user would. The running state of web applications is checked by GitLab’s DAST security feature in their CI/CD platform. Using DAST to scan deployed apps reveals potential flaws that may escape detection through other methods. An anticipatory security tactic offers robust defence against prospective dangers. There are two forms of DAST testing in GitLab, which use AI features to automate test management.
Dependency scanning: Automated dependency scanning in GitLab identifies recognised security weaknesses among project dependencies like libraries and packages. Dependency scanning integration within the pipeline minimises the chance of threats exploitation by proactively identifying and flagging obsolete or unsafe components.
Container scanning: Containerisation being more widely adopted has led GitLab to include container scanning in its CI/CD workflow. The feature flags any security concerns found within container images, including antiquated packages or an inadequate setup. Safeguarding containers strengthens comprehensive application security during deployment.
Shifting from DevOps adoption to DevSecOps adoption
DevOps is termed as a ‘cultural movement’ that emphasises the collaboration and communication between development, operations, and security teams. The goal of DevOps is to shorten the software development life cycle and improve the quality of software by automating the delivery process.
DevSecOps is an extension of DevOps that brings security into the fold. It ensures that security is considered at every stage of the software development life cycle, from the initial design phase to the final deployment.
Here are some of the key differences between DevOps adoption and DevSecOps adoption:
- DevOps focuses on speed and efficiency, while DevSecOps focuses on security. DevOps teams are often focused on getting code out the door quickly, while DevSecOps teams are focused on making sure that code is secure before it is deployed.
- DevOps teams are often siloed, while DevSecOps teams are more collaborative. DevOps teams are often made up of developers and operations engineers, while DevSecOps teams also include security engineers. This collaboration helps to ensure that security is considered at every stage of the software development life cycle.
- DevOps teams use a variety of tools to automate the software delivery process, while DevSecOps teams use more specialised tools to scan code for security vulnerabilities and to automate security testing.
Here are some of the benefits of shifting from DevOps adoption to DevSecOps adoption.
Improved security: DevSecOps can help organisations identify and fix security vulnerabilities earlier in the software development process, which can help to reduce the risk of data breaches and other security incidents.
Improved compliance: DevSecOps can help organisations comply with industry regulations, such as PCI DSS and HIPAA.
Increased efficiency: DevSecOps can help to streamline the software delivery process, which can lead to faster time to market and reduced costs.
Improved developer productivity: It can help developers to be more productive by reducing the time they spend on security tasks.
Here are some steps you can take to shift from DevOps adoption to DevSecOps adoption.
Educate the team about DevSecOps: Make sure that everyone on the team understands the principles of DevSecOps and why it is important.
Identify security risks: Assess the current security posture and identify the areas where you are most vulnerable.
Implement security controls: Put in place security controls to mitigate the risks being identified.
Automate security testing: Use automated tools to scan the code for security vulnerabilities and to automate security testing.
Monitor your security posture: Continuously monitor the security posture to ensure that you are staying ahead of the latest threats.
Shifting from DevOps adoption to DevSecOps is a journey, not a destination. It takes time and effort to fully implement DevSecOps principles. However, the benefits of DevSecOps can be significant, including improved security, compliance, efficiency, and developer productivity.
Enabling GitLab AI services for the cloud
The following are the various GitLab AI services for a cloud platform.
- Code intelligence: GitLab AI can be used to provide code intelligence, which can help developers to write more secure and efficient code. For example, GitLab AI can help developers to identify potential security vulnerabilities, code smells, and inefficient code patterns.
- Code review: GitLab AI can be used to automate code review, which can help to improve the quality of code. For example, it can be used to identify potential security vulnerabilities and code smells.
- Security testing: GitLab AI can be used to automate security testing, which can help to identify and fix security vulnerabilities. For example, it can be used to scan code for known security vulnerabilities.
- Compliance reporting: GitLab AI can be used to generate compliance reports, which can help organisations to demonstrate compliance with industry regulations. For example, GitLab AI can be used to generate a report of all security vulnerabilities that have been identified in code.
- AI-assisted code review: This feature uses machine learning to identify potential security vulnerabilities and code smells in your code. It can also suggest improvements to your code, such as making it more efficient or easier to read.
- AI-assisted security testing: This feature uses machine learning to scan your code for known security vulnerabilities. It can also identify potential security vulnerabilities that are not yet known to the public.
- AI-assisted compliance reporting: This feature uses machine learning to generate compliance reports that demonstrate your organisation’s compliance with industry regulations. It can also identify potential compliance gaps that need to be addressed.
Follow these steps to enable Gitlab AI services in a cloud platform.
- To enable the AI/ML powered features setting, go to your GitLab instance and click on the ‘Settings’ icon. Then, click on ‘General’ and scroll down to the AI/ML powered features section. Check the box next to ‘Enable AI/ML powered features’ and click on ‘Save changes’.
- To create a Google Cloud service account, go to the Google Cloud platform console https://console.cloud.google.com/ and click on the ‘IAM & Admin’ tab. Then, click on ‘Service accounts’ and on the ‘Create service account’ buttons. In the service account name field, enter a name for your service account. In the role field, select the ‘AI Platform Admin’ role. Click on the ‘Create’ button.
- Once your service account has been created, click on the ‘Download JSON key file’ button. This will download a JSON file containing the credentials for your service account.
- To create an environment variable in your GitLab project, go to your project’s ‘Settings’ page and click on the ‘Environment variables’ tab. Click on the ‘Add variable’ button and enter these values.
- Name: GITLAB_CLOUD_SERVICE_ACCOUNT_CREDENTIALS
- Value: The contents of the JSON key file for your Google Cloud service account
5. To restart your GitLab Runner, you can use this command:
sudo gitlab-runner restart
On completion of these five steps, GitLab AI services will be enabled on the cloud platform. And GitLab AI features, such as code intelligence and code review, can be used to improve the quality and security of the code.
Benefits of GitLab AI services
GitLab AI services offer several benefits that can enhance the development and deployment of AI projects. Some of the key benefits are listed here.
- Integrated AI capabilities: GitLab AI services provide built-in AI capabilities, allowing developers to seamlessly incorporate AI functionality into their projects. This integration eliminates the need for separate AI platforms or tools, streamlining the development process.
- Version control for AI models: GitLab’s version control system enables efficient management of AI models. Developers can track changes, collaborate with team members, and easily revert to previous versions if needed. This ensures better control and reproducibility of AI models throughout their life cycle.
- Continuous integration and deployment (CI/CD): GitLab AI services support CI/CD pipelines, enabling automated testing, building, and deployment of AI models. This automation reduces manual effort, speeds up the development cycle, and ensures consistent and reliable deployment of AI applications.
- Collaboration and knowledge sharing: GitLab provides a collaborative environment for AI development teams. Developers can work together, share code, and review each other’s work, fostering collaboration and knowledge sharing. This accelerates learning, improves code quality, and promotes best practices in AI development.
- Scalability and performance: GitLab AI services are designed to handle large-scale AI projects. With features like distributed computing and parallel processing, developers can leverage the power of multiple machines to train and deploy AI models efficiently. This scalability ensures optimal performance even for resource-intensive AI workloads.
- Security and compliance: GitLab prioritises security and compliance, providing features like access controls, authentication, and encryption. This ensures that AI models and data are protected throughout the development and deployment process, meeting industry standards and regulatory requirements.
- Extensibility and customisation: GitLab AI services are highly extensible, allowing developers to integrate with other AI frameworks, libraries, or tools. This flexibility enables customisation and integration with existing AI ecosystems, empowering developers to leverage their preferred tools
and workflows.
Overall, GitLab AI services offer a comprehensive platform for AI development, combining version control, collaboration, automation, and scalability. These benefits contribute to faster development cycles, improved code quality, and efficient deployment of AI applications.
GitLab’s innovative platform has disrupted software creation by seamlessly blending AI and cybersecurity capabilities. AI-based functionalities maximise workflow effectiveness, improve output, and provide specialised CI/CD pipelines for every project. These include Auto DevOps and Auto Remediation. GitLab’s security features, including SAST, DAST, dependency scanning, and container scanning, strengthen the development process. These features ensure that software projects are reliable and secure from inception to deployment. Development teams can efficiently harness these sophisticated functions to construct premium software and then successfully deploy it into production settings.
