Business of DevOps
SRE in the Age of AI: What Reliability Looks Like When Systems Learn
Muhammad Yawar Malik | | adaptive systems, AI observability, AI reliability, concept drift, data pipeline reliability, feedback loops, incident response, learning systems, ML operations, MLOps, model drift, model performance monitoring, reliability engineering, site reliability engineering in AI, SREAs AI and ML become core production components, SRE is evolving from managing deterministic systems to ensuring the reliability of dynamic, learning systems. New metrics, workflows, guardrails and cross-disciplinary practices are redefining ...
MyDecisive Open Sources Platform for Processing OpenTelemetry Data
Mike Vizard | | CNCF, DevOps observability costs, golden signals, Kubernetes telemetry, memory-based filtering, MTTR reduction, observability, open-source observability, OpenTelemetry, OTel collectors, proactive monitoring., telemetry filtering, telemetry overloadBy open-sourcing its Smart Telemetry Hub, MyDecisive pushes for an evolution of OpenTelemetry—adding local, memory-based filtering to shrink telemetry volume and help DevOps teams lower observability costs and improve MTTR ...
The Deterministic Future of AI-Generated Code
Yechezkel Rabinovich | | AI code generation, AI code review, AI development workflows, automated pull requests, behavioral verification, CI pipeline, code validation, deterministic guardrails, DevOps AI, ebpf, generated code risks, observability, smarter linters, software reliabilityAI has eliminated the bottleneck of writing code—but introduced massive uncertainty in verifying it. This piece explores why deterministic guardrails, smarter linters, and eBPF-driven observability are becoming essential to code review and ...
Observability is the Next Frontier of DevOps and Cloud Security
Joe Selvam | | adaptive baselines, API tracing, business impact monitoring, Cloud Security, cloud-native, configuration drift, DevOps visibility, devsecops, hybrid cloud resilience, mttr, observability, proactive observability, SRE, unified dashboards, unified telemetryIn today’s cloud-native, hybrid-multi-cloud world, DevOps teams face a new paradox. They can deploy code faster than ever, but their visibility often lags. Traditional monitoring tools might reveal that something broke, but ...
The MLSecOps Era: Why DevOps Teams Must Care about Prompt Security
Alex Vakulov | | AI attack surface, AI chaos testing, AI Governance, AI supply chain risk, autonomous agent security., CI/CD pipeline security, CodeShield, DevOps AI, ISO 42001, LlamaFirewall, LLM security, MLSecOps, model drift, NIST AI RMF, prompt injection, prompt validation, PromptGuard, PromptOps, RBAC for AI, red team AI, runtime monitoring, secure AI pipelinesAI-driven software delivery introduces new risks, especially prompt manipulation within CI/CD workflows. This article details the emerging fields of PromptOps and MLSecOps and offers practical strategies for securing prompts, models, and pipelines ...
Google Code Wiki Aims to Solve Documentation’s Oldest Problem
Tom Smith | | AI code analysis, AI documentation tools, automated architecture diagrams, automated code documentation, code repository documentation, codebase understanding, developer productivity tools, legacy code maintenance, onboarding developers faster, software architecture diagramsGoogle introduces Code Wiki, an AI-powered platform that automatically generates and maintains documentation for code repositories. With Gemini integration, live diagrams, and interactive code-aware chat, Code Wiki aims to reduce onboarding time, ...
Survey Sees AI Coding Creating Need for More Software Engineers
Mike Vizard | | agentic AI, AI compliance challenges, AI in DevOps, AI in software development, AI tools adoption, AI-generated code vulnerabilities, developer upskilling, DevOps productivity, DevSecOps challenges, GitLab DevSecOps survey, platform engineering, SDLC automation, software engineer demand, technical debt from AI, vibecoding risksA GitLab survey of 3,266 DevSecOps professionals shows AI is boosting code creation but increasing the need for skilled engineers, compliance challenges and human oversight ...
How Hyperconnected AI Development Creates a Multi-System Secret Sprawl
Guillaume Valadon | | AI development security, AI secret leaks, API key sprawl, Claude Code security, Cursor IDE risks, database credential leaks, GitHub secret exposure, hyperconnected AI workflows, large language model security, leaked API keys, MCP configuration files, MCP security risks, mcp.json leaks, OWASP AI testing, secret scanning toolsIf you've been building artificial intelligence (AI) tools lately, you've probably noticed something: Your development workflow has become incredibly connected. Tools such as model context protocol (MCP) sit at the center of it all, acting as the ‘brain’ that orchestrates how your large language ...
GitOps in the Wild: Scaling Continuous Delivery in Hybrid Cloud Environments
Ankur Mahida | | continuous delivery, devops automation, drift detection, GitOps architecture, GitOps at scale, GitOps best practices, GitOps case study, GitOps challenges, GitOps governance, GitOps observability, GitOps security, hybrid cloud GitOps, hybrid cloud operations, infrastructure as code, Kubernetes delivery, multi-cloud DevOps, Open Policy Agent, policy as codeA deep dive into scaling GitOps for hybrid and multi-cloud environments, exploring architecture, governance, security, observability and real-world enterprise practices ...
AI-Driven Performance Testing: A New Era for Software Quality
Akash Thakur | | AI agents, AI defect detection, AI in DevOps, AI load testing, AI observability, AI performance testing, AI testing tools, continuous assurance, intelligent performance systems, LLMs in testing, ML-driven QA, next-gen performance testing, performance automation, performance engineering, performance engineers, performance lifecycle, performance monitoring, performance optimization, predictive performance analysis, predictive testing, requirements as code, self-healing systems, software reliability, SRE automation, testing transformationDiscover how AI and large language models (LLMs) are revolutionizing performance testing—shifting from reactive load testing to predictive, continuous assurance powered by intelligent agents and automation ...
The Future of Observability: Predictive Root Cause Analysis Using AI
Karthikeyan Uthirapathy | | AI in DevOps, AI observability, AI-driven RCA, AI-powered monitoring, anomaly correlation, anomaly detection, artificial intelligence, automated incident response, cloud environments, devops, distributed systems, intelligent observability, kubernetes, logs metrics traces, machine learning, microservices, observability tools, Predictive Analytics, predictive monitoring, predictive root cause analysis, proactive observability, rca, self-healing systems, system reliabilityIn the past few years, systems have become more complex than ever. Microservices, Kubernetes, cloud environments and distributed application programming interfaces (APIs) have changed how we build and manage software. However, this complexity has also made it harder ...
Why Your SLO Dashboard is Lying: Moving Beyond Vanity Metrics in Production
Muhammad Yawar Malik | | API latency, business context, business metrics, business resilience, business-aligned SLOs, context-aware monitoring, customer impact, data-driven DevOps, devops, engineering reliability, enterprise customers, enterprise reliability, error budget management, error budgets, incident management, incident prioritization, observability, performance monitoring, reliability engineering, reliability framework, reliability strategy, revenue-weighted SLOs, service availability, Service Level Objectives, site reliability engineering, SLA optimization, SLIs, SLOs, SRE, uptime, uptime metrics, user segmentationDiscover how redefining service level objectives (SLOs) around business impact — not vanity uptime metrics — reduced incidents by 75% and saved $2.3M in lost revenue ...
