Business directives and competitive pressure continue to push engineering teams to build and ship software rapidly, while modern development simultaneously demands cybersecurity at every layer to ensure that speed does not introduce risk. Increasingly, developers are using AI agents as semi-autonomous teammates that can plan, act, and iterate across workflows like coding, testing, DevOps, research, data analysis, security and internal tools. However, the proliferation of agentic AI has further exacerbated the problem of securing disparate secrets, credentials and tools. In many organizations, privileged secrets and credentials still live outside the workflows where developers actually create, test and deploy code ...

Artificial intelligence is no longer assisting software development. It is becoming its primary author. Claude Code Security's launch is not important because it replaces traditional application security, it's important because it signals something far larger. AI-generated software has fundamentally changed the economics of risk, and when risk scales at machine speed, security must evolve just as fast ...

As we celebrate 25 years since the release of the Agile Manifesto, it is fascinating to reflect on how this development methodology transformed software usability, velocity, and the ability to pivot to meet customer needs and overcome obstacles. These principles remain key in modern enterprises, and many organizations still apply Agile principles today. However, with AI-assisted coding and autonomous agents bulldozing their way into most software delivery pipelines in 2026, the shift to at least a hybrid Agile/DevSecOps strategy is an inevitability ...

AI-powered systems are no longer experimental. They sit at the center of cloud-native applications, making real-time decisions that affect customers, revenue, and risk. Yet many organizations still secure these systems the same way they did a decade ago, by focusing on infrastructure and perimeter controls while assuming the "intelligence layer" will behave as expected. That assumption is becoming increasingly dangerous ...

Here's what nobody is saying about the traditional model of centralized enterprise security: It scales terribly. As a result, security teams cannot effectively scale to manage the flood of security complexities generated by today's AI-fueled enterprises. At scale, and with increasing digital complexity, these problems will not be solved by hiring more people, buying more tools, or working longer hours. For organizations to weather this storm, they will have to make an entirely different choice: Distribute security responsibilities closer to the business owners where risks emerge ...

T-Mobile had firewalls, WAFs, and a full-stack security team. It didn't matter. In 2023, attackers exploited an exposed API to harvest data from over 37 million accounts, names, emails, and phone numbers, by automating queries against a poorly scoped endpoint. No passwords were needed, no authentication tokens; just an API left open to enumeration. This vulnerability is far from unique to T-Mobile ...

For years, software security moved to a steady rhythm of discovery and patching. If there was a security issue, it almost always lived inside a questionable library or a line of code that shouldn't have been there in the first place. Large language models (LLMs) have derailed that rhythm, mostly without fanfare (or input from security!) ...

The software development landscape has undergone a seismic shift; artificial intelligence has democratized code creation, release cycles have accelerated to breakneck speeds, and the software supply chain has become more complex than ever. Yet beneath this veneer of progress lies a troubling reality — organizations are racing ahead with innovation while their security practices struggle to keep pace ...

Organizations experienced an average of 1,968 cyber attacks per week in 2025, representing a 70% increase since 2023, as attackers increasingly leverage automation and AI to move faster, scale more easily, and operate across multiple attack surfaces simultaneously, according to Cyber Security Report 2026 from Check Point Software Technologies ...

As development teams adopt the new AI standard, it is critical to reevaluate security investments to ensure more proactive protection and maintain operational resilience. The focus must shift from general security concerns to targeted strategies that can secure code generated at machine speed ...

The uptick in adoption of AI tools within the developer community aligns with growing expectations. Developers are now expected to work with greater efficiency to meet deadlines more quickly, all while delivering high-quality code. Developers might find AI assistants to be beneficial as they are immune to human-based tendencies like fatigue and biases, which can boost efficiency. But sacrificing safety for speed is unacceptable ...

DEVOPSdigest's Prediction Series continues with 2026 DevSecOps Predictions — Industry experts offer predictions on how DevSecOps will evolve and impact the industry in 2026. Part 3 covers cybersecurity risks ...