Application Programming Interfaces (APIs) run throughout modern applications, mobile apps, cloud services and integrations that operate the digital world. Like any other indispensable piece of IT infrastructure, they've also become a major attack surface. APIs are not suddenly going to stop being a part of software development, but the risks they present need to be dealt with ...

AI adoption in the enterprise has officially moved past the experimentation phase. Companies aren't just piloting AI anymore, they're embedding it into products, workflows, support operations and development pipelines at a pace most organizations couldn't have imagined two years ago. But while AI is scaling fast, confidence in the quality of those systems isn't keeping up ... Applause's 2026 State of Digital Quality in Testing AI report ... found that 55% of organizations have already released AI-powered applications or features into production. At the same time, more than half of AI initiatives still fail to reach full production due to integration complexity, quality concerns and cost pressures ...

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. ... And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Cloud-native delivery can move fast, but speed alone does not reduce operational risk. In many production environments, incidents are triggered by change. It can be a rollout that behaves differently under real traffic, a configuration shift that amplifies latency, or a recovery process that takes too long when the system is already degrading. What turns these events into business impact is rarely "lack of effort." It's uncertainty and delay. Teams can't quickly prove what is running, can't validate behavior early, and can't recover deterministically. Resilient delivery depends on shortening the feedback loop between deployment and verification so teams can detect problems before they affect a large portion of traffic. A practical way to do that is to build a Release Safety Loop into everyday delivery ...

Enterprise IT leaders have gravitated towards replacing existing toolchains with all-in-one DevOps platforms via large-scale, yearlong replatforming initiatives. However, new survey data around these migrations reveals that these initiatives are causing unintended consequences, and in some cases even backfiring completely ...

A DevOps team hits a decision point. They escalate. Leadership reviews, debates, requests more data. Another meeting is scheduled. The developers wait. Maybe that worked when product cycles lasted 18 months. Today, competitors deploy daily. Customer expectations reset weekly. Waiting kills momentum. Agility matters ...

Industry experts offer thoughtful, insightful, and often controversial predictions on how DevOps, development and AI-powered dev tools will evolve and impact the industry in 2026. Part 6 covers the AI-powered SDLC ...

Dan Twing and Tom O'Rourke are joined by Pete Goldin of DEVOPSdigest on the Enterprise Automation Excellence Podcast to discuss EMA's recent survey of AI-powered development and DevOps tools. The research shows high adoption rates of AI-native development tools with broad use of AI integration in core processes. This early success has created cautious optimism, however, significant governance gaps exist in many organizations ...

DevOps teams have always carried more than their job titles suggest. They've owned uptime, the speed of releases, performance and increasingly, accountability for what happens when something breaks in production. Over the past few years, a quieter shift has occurred, with security responsibilities increasingly landing on DevOps teams — security alerts, CVE response, access reviews, anomalies buried in logs. These are becoming routine parts of operational work, especially in organizations without a fully staffed SOC or formalized incident response process. Mainly because DevOps are the closest to production and someone needs to respond when gaps occur ...

The promises AI is making for DevOps (faster coding, faster debugging, faster reviews) is appealing. But in practice, that speed does not automatically translate into faster delivery or impact. AI often generates more tasks than it resolves: refactors, bug reports, and code suggestions can appear asynchronously from multiple tools, creating floods of new work ...

AI is no longer an optional add-on in app development ...According to App Builder's 2025 App Development Trends Report, 87% of tech leaders say their teams are already using AI in app development. And, as the technology becomes more deeply integrated into development workflows, companies are shifting their hiring priorities to match. Nearly three-quarters (71%) of tech leaders say AI and machine learning skills are non-negotiable when hiring developers ...

Global economic disruptions aren't restricted to supply chains and manufacturing; their impact also quietly infiltrates the software industry. Software teams quickly feel the pinch when the global economy experiences some turbulence ... Although software companies are not directly affected by tariffs on physical goods, the indirect consequences are becoming harder to ignore. The software industry depends heavily on hardware infrastructure ...