Operations | Monitoring | ITSM | DevOps | Cloud

A recent IDC Spotlight report based on a survey of 685 public sector respondents found that 72% describe scaling AI from pilot to production as "very" or "somewhat" difficult.¹ Choosing the right model is only part of the challenge. Agencies also need to get their data ready for AI.

The new columnar metrics engine in Elastic Observability enables public sector IT teams to combine logging, metrics, and traces in one platform. As a result, SREs can improve uptime while protecting taxpayer dollars in the process. Public sector site reliability engineers (SREs) operate under a distinct set of pressures, whether that’s supporting a federal agency, a health department, a public university, or a transit authority.

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.