Cloud-native teams have long embraced chaos engineering, game days, and incident response to build resilient, scalable systems. We prepare for failure. We plan for it. We test it. But when it comes to cloud cost overruns? We often react —after the damage is done. It’s time to treat cost anomalies like operational incidents , because that’s exactly what they are: unplanned events that threaten system health—just in a different column of your dashboard. The Myth of Infinite Cloud = The Risk of Infinite Cost The promise of the cloud is elasticity. But elasticity without control is a budgetary time bomb. We wouldn’t let developers deploy to production without testing. So why are teams still allowed to: Launch GPU instances without a use case? Leave unused dev environments running for weeks? Exceed monthly budget targets without warning? It’s not about blame. It’s about systems thinking . Just like latency, throughput, and availability, cost is an operational signal...
As software delivery accelerates and attack surfaces grow, traditional DevSecOps practices are being pushed to their limits. The integration of artificial intelligence (AI) into DevSecOps workflows is not just a trend—it’s a strategic imperative. AI is driving a seismic shift in how we manage code quality, automate security, respond to threats, and enable secure innovation at scale. In this post, we’ll explore the key ways AI is improving DevSecOps and why forward-thinking organizations are embedding it deeply into their pipelines. 1. Proactive Threat Detection and Response In modern CI/CD pipelines, code moves fast—sometimes too fast for human eyes to catch every vulnerability or misconfiguration. AI helps shift security left and right by: Analyzing code and dependencies with natural language processing and ML to detect hidden vulnerabilities, insecure APIs, or anomalous changes during commits. Real-time anomaly detection in production environments using AI-powered o...