Operations | Monitoring | ITSM | DevOps | Cloud

Autonomous SOC: Moving Toward Self-Driving Security Operations

The idea of a fully autonomous security operations center (SOC) sparks fascination and skepticism in equal measure. Swimlane defines an autonomous SOC as a center that uses AI, machine learning and automation to handle a significant portion of security tasks, including threat detection, triage and even remediation, with minimal human intervention. The goal is to free analysts from repetitive tasks so they can focus on strategy and threat hunting. Although a completely selfdriving SOC remains aspirational, advances in hyperautomation, enterprise automation architectures and AI agents are bringing us closer.

European enterprises prioritise governance in AI deployments, as North America accelerates towards full autonomy

Digitate report reveals differing approaches to AI deployment between Europe and North America, but ROI remains consistent. Europe leading on governance while NA organisations show faster progress towards autonomous operations.

#AI Powered Data Protection Inside Cribl Guard

Cribl Guard uses an always running AI agent to spot sensitive data as it moves through your environment and recommend the right protections in real time. In this demo, you will see how the agent samples live events, identifies patterns like credentials and credit cards, and turns them into one click fixes that keep your destinations safe. Faster detection, smarter rule recommendations, and instant mitigation. This is what modern data protection looks like.

New agents in the Dojo: Expanded Sumo Logic Dojo AI

Back in September, we unveiled Sumo Logic Dojo AI, our agentic AI platform built to power intelligent security operations and incident response. With that launch, we introduced Mobot, our conversational interface, as well as our first agents designed to help automate routine tasks, streamline investigations, and give security teams the freedom and ability to focus on analyzing the highest value security issues facing their organization. Today, we’re excited to share the latest additions to Dojo AI.

Harnessing AI for Enhanced Digital Experiences

AI can significantly improve digital experiences when integrated into workflows. This proactive approach helps address issues and allows employees to focus on innovation. However, successful implementation requires strong foundations and rebuilt workflows. Many AI projects may fail, with predictions that 40% of generative AI initiatives will be canceled by 2026 due to misunderstandings. Clear objectives are essential to ensure AI is not pursued for its own sake.

9 Monitoring Tools That Deliver AI-Native Anomaly Detection

The observability market has moved beyond manual threshold-setting. Modern platforms use statistical algorithms, machine learning, and causal AI to detect anomalies automatically. Some work immediately after deployment. Others train on your data for better accuracy. Each approach has technical trade-offs worth understanding. This guide compares how nine monitoring solutions handle automated anomaly detection and root cause analysis.

Accelerate investigations with AI-powered log parsing

When debugging production issues, investigating security incidents, or analyzing network traffic, engineers and analysts need not only to find the right logs but to make sense of all the dense, unstructured data generated by different systems. Logs rarely ship neatly laid out in a way that facilitates filtering, faceting, or graphing for every possible scenario. As a result, teams often find themselves writing regular expressions or custom parsers on the fly, which can be error-prone and time-consuming.

Monitor Claude Code adoption in your organization with Datadog's AI Agents Console

AI coding assistants are quickly becoming a core part of software engineering workflows, helping developers write, refactor, and review code faster. But without effective monitoring, it can be difficult to know whether these tools are performing reliably and proving useful to engineers. As organizations scale their use of tools like Claude Code, key questions emerge.

AI Infrastructure Is Creating a New Wave of Incidents, And Why Enterprises Need a Modern On-Call Strategy

Over the last few years, AI has quietly shifted from a fascinating experiment to a core operational system. Enterprises aren’t just building prototypes anymore — they’re deploying LLMs into production environments where uptime directly affects customer interactions, revenue flows, and business continuity. AI has essentially become a new layer of critical infrastructure. Because of that shift, the definition of “reliability” is changing.

From FinOps for AI to AI-Native FinOps

One year ago, at AWS re:Invent, we launched CloudZero Advisor, a free, standalone AI assistant that enables anyone to ask questions about cloud spend in plain language. It was the first experiment of its kind in FinOps, a chance to see what people really wanted to know when cost data finally became conversational. Over the past year, Advisor has become a learning engine.