Enhancing DevSecOps Workflows with Generative AI: A Comprehensive Guide

With the advent of generative AI, a new wave of innovation has enabled organizations to accelerate DevSecOps workflows by alleviating the tedious, manual, and time-consuming aspects of software development and delivery. To realise AI’s full potential, however, developers must embed AI across the entire software development lifecycle, not just in code creation.

According to GitLab’s Global DevSecOps Report, developers spend only a quarter of their time working on actual code generation. However, as recently discussed in a webinar featuring GitLab Product Lead for AI Taylor McCaslin and Field CTOs Lee Faus, and Brian Wald, there are so many other essential parts of the software development lifecycle—such as the initial commits or final production stages—that could also benefit from the power of AI.

Integrating AI throughout the software development process can ensure faster, higher-quality, and more secure software delivery from the start. By automating test builds using AI, for example, developers can quickly and easily identify a failed build and better understand how to fix it. While embedding AI doesn’t entirely remove the tast from the workflow, it does increase developer productivity and efficiency overall.

That said, in order to implement AI responsibly and sustainably, it’s crucial to establish strong guardrails to mitigate any introduced risks. A good place to start is with a thorough assement of existing workflows.

This is a sponsored article by Gitlab. GitLab is a complete DevOps platform, delivered as a single application, fundamentally changing the way Development, Security, and Ops teams collaborate and build software. From idea to production, GitLab helps teams improve cycle time from weeks to minutes, reduce development costs and time to market while increasing developer productivity. Learn more about GitLab.

Initiating AI Integration: Workflow Assessment

Understanding and mapping out your current workflows is the first step towards proper AI integration. This involves identifying and establishing a workflow that allows for the best and most consistent approach to using AI, while setting the necessary safeguards and policies in place to prevent potential risks. For example, when code is automatically generated with AI, there’s a risk of security vulnerabilities being present. Implementing a proactive workflow designed to detect and rectify these issues early in the development process is critical to avoiding security gaps, while allowing for the innovation and velocity that AI can provide.

Key Strategies for Successful AI Deployment

Prioritize Major Development Challenges: Focus first on revamping workflows that directly address your most significant software development and delivery issues, whether its modernizing legacy systems, enhancing security protocols to account for increased vulnerabilities, or optimizing resources and operational overhead.

Establish AI Guardrails: As previously mentioned, it’s crucial to recognize the risks associated with AI, especially in terms of data protection and compliance requirements. Collaborate with your legal, compliance, and DevSecOps teams as you consider the various AI models, vector databases, and large language models (LLMs) that are being leveraged ans accessed. Resources from the GitLab AI Transparency Center, along with specific blog posts on building a transparency-first AI strategy, offer valuable guidance in this regard.

Streamline AI Tool Usage: Managing a single platform instead of multiple tools can help you simplify your AI toolset, minimize complexity, and reduce potential security risks. An overcrowded tool landscape can lead to operational inefficiencies and increased overhead costs and security vulmnerabilities. By simplyfing your toolchain, you’ll make it easier for developers to create efficient and trustworthy software.

Measuring AI’s Impact on Productivity

To understand the true impact of AI within an organization, it is crucial to holistically measure the changes in productivitiy and other key metrics. Organizations must now move beyond traditional indicators like code deployment frequency or bug remediation times in order to gain a comprehensive view of AI’s influence on productivity and development velocity.

GitLab measures the impact of AI by establishing standardized workflows within the platform’s groups and projects hierarchy, enabling teams to aggreate and analyze their metrics and outputs directly within the user interface.

This structure, combined with the power of AI, shows a clear relationship between the roles both play in accelerating the speed and efficiency of the development process—from merge request validation to vulnerability resolution.

GitLab Duo: AI-assisted Features Across a Unified DevSecOps Platform

With GitLab Duo, a toolkit of AI features that leverages powerful AI models and advanced technologies from leading hyperscalers, GitLab is paving the way for how to successfully embed AI through the entire softwre developement lifecycle. Features such as code assistant, conversational chat assistant and vulnerability explainer help increase velocity and productivity, reduce cycle times, and solve key pain points across the software development process—thereby freeing up developers to focus on creating the best software possible.

The “Omdia Market Radar: AI-Assisted Software Development, 2023–24” report recognizes GitLab Duo as a “suitable solution for enterprise-grade application development,” highlighting that the AI assistance is integrated throughout the software development lifecycle pipeline built into GitLab.

Practical Applications of GitLab Duo:

• Merge Request Descriptions: Automatically generates detailed descriptions for merge requests and their string of commits, as well as identifies missing tasks related to a particular merge request.
• Code Explanation in Natural Language: Enables QA testers to gain a deeper understanding of complex code, facilitating the creation of comprehensive test cases.
• Pipeline Error Analysis: Offers insights into potential root causes of pipeline failures, providing actionable solutions that can be copied and pasted back into a CI job for swift resolution.
• Vulnerability Resolution: Empowers engineering teams with the knowledge to identify, locate, and fix vulnerabilities efficiently, ensuring secure software development from the start.

By strategically incorporating generative AI into your DevSecOps environment, you can unlock new levels of productivity and innovation, ensuring your development processes are not only faster but also more secure and reliable.