Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Security teams are being asked to operate at machine speed while still making decisions they can trust. Attackers move faster. Exposure changes continuously. Manual workflows struggle to keep up. Following the recent announcement of the Aurora Superintelligence Platform and Aurora Agentic SOC, Arctic Wolf continues to advance its portfolio with new capabilities that help teams see risk clearly, prioritize what matters, and act with confidence.

In May 2026, Arctic Wolf observed a cluster of malicious activity affecting endpoints managed by FortiClient Endpoint Management Server (EMS). The malicious payload was disguised as a fake Fortinet endpoint patch, but it was actually a credential stealer. We named this payload EKZ Infostealer, based on internal symbol names extracted from decrypted code.

One of the most important shifts AI enables in detection engineering is changing where engineers spend their time. Traditionally, a significant portion of detection development effort is consumed by implementation details: writing complex SQL queries, building enrichment pipelines, handling edge cases, tuning rule logic, writing tests, documenting detections, and repeatedly iterating on detection logic. Those tasks are necessary, but they are also time-consuming.

Across every industry, one thing has become abundantly clear: Cloud security has never been more critical, nor more complex. Organizations are scaling cloud environments faster than ever, but the explosion of identities, configurations, and services has created an attack surface that traditional approaches simply can’t keep up with. Teams are drowning in alerts, struggling to identify which issues matter, and facing increasing pressure to respond to threats with limited resources.

Modern security environments generate enormous volumes of telemetry. Authentication events from identity platforms, API activity from cloud services, endpoint security logs, email interactions, and network traffic can all flow into centralized systems. For most organizations, the challenge is no longer data collection. The real problem is extracting meaningful insight from that data without overwhelming analysts or introducing operational friction.