GUEST ESSAY: An assessment of how ‘Gen-AI’ has begun to transform DevSecOps

By Priyank Kapadia

Combining DevSecOps with Generative Artificial Intelligence (Gen-AI) holds the potential to transform both software development and cybersecurity protocols.

Related: The primacy of DevSecOps

Through harnessing the power of Generative AI, enterprises can usher in a new era of DevSecOps, elevating development velocity, security, and robustness to unprecedented levels.

DevSecOps teams can test and debug code 70 percent faster with generative AI, which in turn saves businesses money and employee hours.

Generative AI can also help DevSecOps professionals to identify areas that are ripe for automation, enhance real-time monitoring and analytics, and even predict and address security problems before they happen.

Accelerating automation

DevSecOps and cybersecurity teams often encounter repetitive, time-consuming tasks that can lead to inefficiencies and errors when they handle these tasks manually. AI can play a pivotal role in automating these processes.

Tasks like code review, test case generation, systematically generating, storing, and managing configuration files, and infrastructure provisioning are prime candidates for automation. Leveraging generative AI in these areas can significantly speed up the delivery process and reduce human errors that could become cybersecurity threats.

AI engineers can train the AI model on a dataset of historical code changes. The model will learn to identify potential problems in code, such as security vulnerabilities, performance issues, and compliance violations. The AI model can then review new code changes automatically. This frees up the DevSecOps teams to focus on higher-order tasks, such as testing and developing new authentication features.


Generative AI can also be used to suggest and/or generate test cases. This baseline test coverage helps immensely in DevSecOps processes and automation and delivers immense value at negligible costs. And in terms of generating configurations, generative AI software allows teams to completely automate the configuration process.

Responding in real-time

But AI-powered issue-spotting goes beyond simple code review. Generative AI algorithms can also continuously analyze vast amounts of data generated during the software development and deployment process. They can monitor key performance metrics, server health, response times, and application stability in real time.

By detecting deviations from normal patterns, such as sudden spikes in server load or unexpected errors in the code, the AI system can promptly alert the appropriate teams to any potential security issues, enabling them to respond swiftly and minimize fallout.

AI can also be programmed to initiate automated remediation actions, also called “self healing,” when issues arise. For instance, if AI identifies a particular type of error that could cause a vulnerability, it can then trigger an automated rollback to a stable version of the software, or it can suggest changes to the code and once accepted, the software is upgraded to perform satisfactorily. This reduces the need for manual intervention and accelerates the incident response process, maintaining a smooth software delivery experience.

Predictive maintenance  

Generative AI algorithms can continuously analyze data from various sources, such as server logs, application performance metrics, and user interactions. By learning normal patterns and behaviors, the AI system can flag deviations that indicate potential issues or security threats.

Anomalies might include unusual traffic patterns: a sudden influx of traffic to a particular website, for example, can herald a Denial of Service attack.

By leveraging historical data and past incident patterns around anomalies, generative AI can build predictive models to anticipate potential failures or breaches. If the AI identifies specific warning signs that have previously led to system crashes or service disruptions, it can warn the tech teams about the likelihood of similar failures occurring in the future.

Armed with this foresight, teams can take preventive measures, implement necessary fixes, and ensure uninterrupted software delivery.

DevSecOps applications

Generative AI is revolutionizing the realm of DevSecOps, providing enhanced security measures throughout the software development lifecycle. Products like Google Cloud’s Security Command Center and DeepCode detect vulnerabilities in cloud environments and code respectively.

Other tools such as Checkmarx’s Codebashing deliver interactive security training, while Palo Alto Networks’ Cortex XSOAR and Red Canary automate vital security tasks. Snyk offers protection against security flaws, and DeepArmor combats malware threats. The real-time threat response is made possible by a software called Insights. Collectively, these advancements underscore the importance and capabilities of generative AI in fortifying software security.

As the cybersecurity industry continues to evolve, demands will only increase and become more complex. Expectations for efficiency will rise right along with them.

In addition, the need for generative AI in DevOps and IT is only going to grow. So embracing AI is not just a luxury for teams grappling internal demands on their time. It’s a necessity to stay ahead of the competition and keep tech companies moving forward.

About the essayist: Priyank Kapadia is a seasoned technology leader at Accolite, delivering solutions through design-led product engineering and advising clients to adopt Generative AI responsibly. 

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