Aspire’s Aju Mathew on DevOps and Generative AI

Aju Mathew, Vice President, Software Engineering at Aspire Systems discussed the role of generative AI in a wide array of software development processes, from DevOps to Infrastructure as Code to security assessments.

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Over the last decade, DevOps has offered major improvements in software creation by integrating software developers and IT operations into one concerted effort. Now generative AI is poised to push DevOps far higher by enabling an advanced toolset that promises to supercharge the development process. Specifically, DevOps systems can use generative AI to generate scripts.

“These scripts could be for ARM [processor chips] or terraform templates, to automatically provision infrastructure like networking servers, operating systems or storage in different supported programming languages, both within the cloud or on-prem,” said Aju Mathew, Vice President of Software Engineering at Aspire Systems.

This automation would help reduce the time needed to create Infrastructure as Code (IaC) templates. IaC creates a far more nimble enterprise workflow by enabling code to support computing infrastructure instead of manual settings and processes. Yet boosting DevOps and IaC are just a few of the ways that generative AI can drive software development.

Watch my extended interview with Aju Mathew to learn how AI can support Pipeline as Code processes, security assessments, and more, or read select interview highlights below.

Generative AI Drives Security Assessments and DevSecOps

Generative AI tools can speed up the vulnerability and security assessment of software in development. One scenario for this is preventive, where the “backend API or the frontend code generated by the platform follows best practices to avoid any vulnerability and security glitches,” Mathew said. This procedure, which is already in use, can be accomplished with the assistance of prompt chaining and setting the ideal parameters.

A second scenario involves vulnerability and security assessment tools, both static and dynamic, which follow a template or rules-based identification of issues that would shift to a generative AI-based detection mechanism. This system essentially automates the process of security testing—a vast improvement over manual testing.

Looking further ahead, Mathew is optimistic. “I’m anticipating concepts of DevSecOps getting implemented using generative AI platforms, with basically end-to-end security and cooperation during application design development, test build and infrastructure provisioning,” he said. “So it’s end-to-end security implementation using generative AI chips.”

Another forward-looking technique that benefits from AI is Pipeline as Code, which is the practice of defining software deployment pipelines using code instead of rigid manual processes. This enables a continuous integration of rapidly iterated code instead of separate, monolithic updates.

“From a Pipeline as Code standpoint, the future I see is that generative AI platforms use the application architecture or design document as the input,” Mathew said. This input would be efficiently sourced based on the programming language used, along with all the associated modules, libraries and code dependencies.

While this approach is advanced, Mathew said, he’s “sure it’s possible in the near term.”

Read our in-depth guide to generative AI models to learn about the inner workings of artificial intelligence and related dynamic technologies.

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