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Graylog MCP Integration: Real-Time LLM Access to Your Data

Graylog V7.0 supports integration with the Model Context Protocol (MCP), which allows large language models (LLMs) to access and interact with Graylog data and workflows in real time. Graylog exposes an MCP-compatible endpoint for LLM clients, such as Claude and LM Studio. MCP integration allows Graylog users to interact with their data through LLMs. With MCP, an LLM can connect directly to Graylog as a remote tool interface, performing queries, retrieving system information, and assisting with common administrative or investigative tasks. This capability may make it possible to.

Introducing the Splunk Technology Add on for Ollama Illuminating Shadow AI Deployments

Without strong visibility and governance, local LLMs risk replicating the fragmented, unsupervised sprawl once seen in shadow IT, complicating security postures and making it difficult for organizations to ensure proper oversight and compliance as these powerful AI tools become embedded in daily workflows. To address this challenge, The Splunk Threat Research Team has released the Splunk Technology Add-on for Ollama that provides comprehensive monitoring and observability capabilities specifically designed for local LLM deployments.

Top 8 AI Editing Software That Can Change a Person's Voice in 2025

Having worked extensively in audio production and voice-based media, I evaluate every voice changer with a professional, meticulous testing process. I focus on realism, interface usability, and editing precision. Over the years, I've tested most major desktop voice editors, examining how accurately they reproduce natural tones and avoid robotic or distorted outputs. Only a few programs truly balance advanced functionality with user-friendly controls.

Our Engineering in the Age of AI: 2026 Benchmark Report finds AI is making engineering faster, but not necessarily better

Everyone's talking about how AI is transforming software development. Teams are shipping more code, deploying more frequently, and getting features to market faster than they could a year ago. The productivity gains are real. But we kept hearing a different story from engineering leaders. Yes, velocity is up. But incidents are climbing, resolution times are getting longer, and code review processes are struggling to keep up.

Conquer Complexity, Accelerate Resolution with the AI Troubleshooting Agent in Splunk Observability Cloud

The digital landscape has transformed dramatically, and with it, the demands on our systems have grown exponentially. Traditional monitoring tools struggle to provide sufficient insight into complex, distributed, cloud-native environments. Observability is the answer, moving beyond merely knowing "what" is happening to understanding "why" it's happening, and its impact on user experience and business outcomes.

If it Wanted to, it Would: The Bitter Lesson for LLM Users

There’s a viral saying folks use about flaky crushes, spouses, and forgetful friends: "if he wanted to, he would." The idea is straightforward: when someone cares, they make the effort. As it turns out, the same principle applies surprisingly well to AI. Systems, like people, have things they "want" to do. Each model has patterns of reasoning and synthesis it performs naturally.

The AI Visibility Problem: When Speed Outruns Security

Harness surveyed 500 security practitioners and decision makers responsible for securing AI-native applications from the United States, UK, Germany, and France to share findings on global security practices. The State of AI-Native Application Security 2025 dives deep into AI visibility and the changing landscape of security vulnerabilities. If 2024 was the year AI started quietly showing up in our workflows, 2025 was the year it kicked the door down.

Weaving AI into the fabric of the company | incident.io

At incident.io, we’ve spent the past year shifting how we work to incorporate the AI into both how we build and what we build. The result? AI has become a fundamental pillar of our company. This is the story of how we built reliable AI for reliability itself — reshaping how teams manage and resolve incidents. From early experiments to a company-wide culture of building with AI, this is how we’re redefining incident response for the future.