AI’s role in DevSecOps was described as a “tectonic shift” by David DeSanto, then GitLab’s chief product officer, in an ITPro interview published on 20 April 2023. He was arguing that AI could help software teams with tasks such as suggesting code and identifying possible vulnerabilities—and that its value should be considered across software delivery, not just while developers write code. That was an executive’s view of the opportunity, not evidence that AI had already improved security or productivity.
What DevSecOps means—and why it matters
DevSecOps brings security practices into software development and operations rather than leaving security as a final hand-off. The aim is to consider security throughout the work of building, testing, and delivering software. That matters because issues found late can require teams to revisit work, while earlier attention gives development and security staff more opportunities to identify and address them.
AI enters this picture as a possible assistant for parts of that work. It does not change the basic responsibility to assess code and security findings: teams still need to decide whether a suggestion is appropriate and whether a suspected vulnerability is real.
What DeSanto meant by a “tectonic shift”
“I’ve been calling it a tectonic shift in how DevSecOps is done,” DeSanto said in an interview at KubeCon 2023, reported by ITPro on 20 April 2023. He linked the phrase to companies struggling to staff the work they wanted to do and to the possibility that AI could make existing team members more effective. The phrase is his characterization of a potential change, not a measured conclusion about results.
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Where AI might assist software teams
Code suggestions
The interview identified code suggestions as one opportunity for AI assistance. Suggestions may help a developer draft or complete code, but the article did not evaluate their accuracy, quality, or effect on development time. Developers need to review suggestions in the context of the codebase and the task.
Vulnerability identification
The other named opportunity was identifying vulnerabilities. A tool’s warning is a lead for investigation, not proof that code is vulnerable; equally, the interview did not establish that AI finds vulnerabilities reliably or catches every issue. Security review remains necessary.
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Why he argued for lifecycle-wide use
DeSanto’s point was that applying AI only to the developer experience would leave other parts of software delivery untouched. A team considering AI for DevSecOps can therefore ask which delivery stages it supports beyond code authoring, how people review its suggestions and findings, and whether it helps with an actual bottleneck. This is a practical way to assess the idea, not a claim that one platform covers every team’s needs.
What the 2023 adoption figures do—and do not—show
ITPro relayed figures from GitLab’s 2023 Global DevSecOps report: 65% of developers were using AI or machine learning in testing, or expected to use it within three years. The article also reported that 62% used AI or machine learning to check code, compared with 51% the preceding year.
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These are company survey figures as reported by ITPro, not current adoption rates or independent measurements of outcomes. The ITPro article does not give the survey sample or methodology, so the numbers should not be treated as representative of all developers. They describe reported use or expectations; they do not show that AI caused better security, faster delivery, or higher-quality code.
How a team can assess an AI-assisted workflow
The interview did not compare products or test a particular platform. For a team evaluating an AI-enabled workflow, useful questions include:
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- Coverage: Which stages of delivery does it address beyond code authoring?
- Human review: Who checks generated suggestions and investigates potential vulnerability findings?
- Impact: What changes in review time, security findings, defects, or developer workload can the team measure locally?
- Fit: Does it address a specific bottleneck, and do newer and more experienced contributors need different kinds of assistance?
Those measures can help a team decide whether a tool is useful in its own workflow. The 2023 interview itself does not establish any present-day productivity, code-quality, security, or retention gains from AI.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the interview can tell readers today
The headline reflects DeSanto’s 2023 view that AI could change how DevSecOps work is done, with assistance extending beyond individual developers. The article is useful for understanding that argument and the survey numbers ITPro attributed to GitLab. It is not a current account of AI capabilities or proof that AI has delivered measurable security gains. The interview mentions GitLab’s platform and its then-new code suggestions beta, but offers no product comparison or evaluation.
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