Operations | Monitoring | ITSM | DevOps | Cloud

As Chief Legal Officer, I’ve personally navigated the complex, ever-shifting landscape where privacy compliance meets rapidly evolving technologies. Whether it’s the sweeping reach of a law protecting personal data in the EU, the specific demands of a law giving California residents more control over their personal information, or the critical protections of a law safeguarding sensitive patient health information in the U.S., one challenge remains.

Recommendation: Focus your next AI initiative on one high-impact workflow. Measure, iterate, and scale. Agentic AI has quickly become the next frontier of enterprise automation. Instead of static AI tools that wait for human prompts, agents act on behalf of users by autonomously reasoning, sequencing steps, and taking action within defined guardrails.

We’re expanding the Open 360 AI experience to more users with a modernized navigation and full access to Grafana and OSD dashboards. Your existing dashboards, alerts, bookmarks, and integrations remain unchanged, while new AI-powered capabilities provide deeper explanations and actionable insights. Existing customers can request early access through their account team.

In October’s episode of This Month in Datadog, Jeremy shows how you can use AI to query with natural language in DDSQL Editor, ensure teams stay continually updated during outages, mitigate the impact of flaky tests, and ingest OpenTelemetry Protocol (OTLP) metrics from serverless and third-party SaaS environments.

Security teams face a constant trade-off between detection coverage and alert fatigue. Broad, rule-based detection approaches surface every possible indicator of compromise (IoC) but generate unmanageable alert volumes. Narrow, tightly scoped rules reduce noise but risk missing critical signals. And while individual indicators of compromise can highlight suspicious behavior, they often lack the surrounding context needed to tell a complete story of how an attack unfolded.

The age of dashboards is ending, as observability has only created more noise for network teams to sift through. Kentik SVP of Product, Mav Turner, lays out why true network intelligence requires a clean, contextual data foundation to finally create a network that thinks.