Operations | Monitoring | ITSM | DevOps | Cloud

How to build governance into autonomous security agents from the architecture up The industry has moved fast on capabilities. Agents now triage alerts, investigate endpoints, create detection rules, and enrich indicators, and they are even capable of performing most actions we as security operators can perform. The architecture patterns are maturing, as are the models, but governance is not keeping pace.

Incident response often depends on connecting two kinds of context: what changed in the environment and what the logs say happened next. Through a new integration with Elastic, Anyshift’s AI agent, Annie, can read from a customer’s Elasticsearch deployment to search logs, surface error and warning spikes, and correlate log evidence with infrastructure change history.

Gigamon COO Gareth Maclachlan on deep observability, the Elastic AI Ecosystem partnership, and AI traffic governance for security teams.

New research reveals why Australian businesses are entering the new financial year with bigger AI budgets and the same unsolved problem. One in three Australian businesses exceeded their AI budget last year. Yet, half of them plan to increase AI spending again this year. Yet the behaviour that caused those budget overruns remains largely unaddressed.

Making custom Elastic integrations better with both Automatic Import improvements and Integration Skills Elastic 9.4 shipped two tools for building custom integrations: Both are available today.

By benchmarking five GCP machine types across both Logstash and Kafka, LivePerson's observability team found that infrastructure selection (not just pipeline configuration) is one of the highest-leverage cost optimization decisions at scale.

Focusing on GenAI and logs for IT efficiency Efficiency is everything for managing today’s digital systems. Technology is constantly transforming and expanding operations are driving an explosion in data. Consequently, data ingest and storage costs have soared. But it’s not just storage data costs that keeps teams behind.The challenge of managing all that observability data forces IT teams to choose between efficiency and the bottom line.

How do you make search faster, smarter, and more scalable? During our recent webinar, I sat down with Luke Patterson, senior product manager at WP Engine, and Delphin Barankanira, independent software vendor partner engineering lead and data & AI specialist at Google Cloud, to answer that question. We dug into the mechanics behind WP Engine’s ability to deliver near-instant updates to over 2 million users.

AI is moving fast. Enterprise adoption needs to move with purpose. Over the past year, one thing has become clear: Organizations are not looking for more AI hype. They are looking for a path to production — one that connects infrastructure, data, and intelligence in a way that delivers real business value. That is exactly what the Elastic AI Ecosystem is built to do. At Elastic, we believe AI is only as powerful as the data foundation behind it. Great models matter.

The demands on financial services companies are intensifying rapidly. They must not only deliver seamless system performance but also control costs, secure sensitive data, and maximize the value of their observability investments. To navigate these converging pressures, leaders are evolving their approach to system monitoring and telemetry. The 2026 State of Observability in Financial Services research report reveals a fundamental shift in how organizations manage their digital infrastructure.