Operations | Monitoring | ITSM | DevOps | Cloud

Modern application security is struggling to keep up with AI-driven development and cloud-native scale, especially when security feels bolted onto CI/CD instead of built in. Harness SAST and SCA bring AI-powered application security testing natively into the Harness platform, reducing noise and alert fatigue. By identifying only vulnerabilities that are actually reachable in production code, teams get findings they can trust and act on faster.

This report presents status data for eight major AI systems during January 2026: Anthropic, Cohere, DeepSeek, Google Gemini, Groq Cloud, OpenAI, Perplexity, Replicate, and xAI. The data includes official incidents reported on provider status pages and detection information from IsDown's monitoring system.

Let’s be real: AI can hallucinate. And in observability, that feels risky. No one wants an assistant that sends your SREs chasing ghosts. At best, that burns expensive engineering time. At worst, it slows incident response in production and pushes teams toward the wrong remediation path. So here’s the big question: What makes Grafana Assistant different, and why should you trust it? Let’s start by acknowledging the fear. AI hallucinations are a real issue.

OpenClaw (née MoltBot, née ClawdBot) is taking over the world. Everyone is spinning their own, either on a VPS, or their own Mac mini. But here's the problem: OpenClaw is brand new, and its security posture is mostly unknown. Security researchers have already found thousands of publicly available instances exposing everything from credentials to private messages.

Today, we are pleased to announce the general availability of Elastic 9.3 as the latest version of the Elasticsearch Platform — the world’s most popular open source platform for transforming both structured and unstructured data into trusted answers and outcomes. In addition to including new features that help developers with context engineering and agent building, Elastic 9.3 introduces a broad set of new capabilities to Elastic Search & AI, Elastic Observability, and Elastic Security.

Organizations are increasingly using agentic AI applications powered by large language models (LLMs) to automate analysis, decision-making, and operational workflows. As these AI agents take on more responsibility, they gain access to internal tools and services and can interact with them in unintended ways.

It’s 2026, and developers have more tools at their disposal than at any point in the industry’s history: CI/CD platforms are richer; observability stacks are deeper; security, data, and AI tooling have exploded into crowded, competitive ecosystems. And yet, delivery is still slow, incidents are still noisy, workflows are still brittle. The problem is no longer tool scarcity or feature depth. It’s integration debt.

We know that AI has been transformational for engineering and it will continue to be, so stop me if this sounds familiar. Imagine an engineering lead opening a pull request for a critical security patch and finding five hundred lines of AI-generated code. While the solution is (mostly) usable, it follows a pattern no one on the team recognizes. This shift away from manually writing every line of logic has introduced a unique level of complexity for teams.

Complex environments don’t fail because teams lack data. They fail when teams can’t trust what the data is telling them. There are too many signals, too little time, and too much risk riding on every decision. That’s the reality Skylar Advisor is built for: delivering guidance teams can verify, so they can act faster without gambling on opaque, black-box answers.