Most security investments focus on the perimeter, like firewalls, endpoint agents, and SIEM alerts. Yet one of the most abused channels in enterprise attacks barely gets a second look: DNS. Before malware is executed, before data is exfiltrated, and before a lateral movement attempt begins, DNS is involved. Attackers use it to find footholds, establish command-and-control (C2) channels, and quietly map internal infrastructure.

We're proud to announce that Frost & Sullivan has named CrowdStrike a Leader for the fourth consecutive time in the 2026 Radar for Cloud-Native Application Protection Platforms. This recognition validates our continued investment in combining posture management with real-time detection and response, and reinforces our leadership in stopping cloud attacks.

On April 22, 2026, Google's Threat Intelligence Group and Mandiant disclosed a campaign by a threat actor they're tracking as UNC6692. The group breached enterprise networks by impersonating IT helpdesk staff over Microsoft Teams, ultimately exfiltrating Active Directory databases and achieving full domain compromise. What's notable about UNC6692 is what they didn't do. They didn't use a zero-day. They didn't exploit a software vulnerability.

Arctic Wolf has identified a targeted intrusion against a North American Web3/cryptocurrency company, which we attribute with a high confidence level to BlueNoroff, a financially motivated subgroup of DPRK’s Lazarus Group.

In early February 2026, users of Qinglong (青龙), a popular open source timed task management platform with over 19,000 GitHub stars, began reporting that their servers were maxing out CPU usage. The cause was a cryptominer binary called.fullgc, deployed through two authentication bypass vulnerabilities that allowed unauthenticated remote code execution. The attacks went largely unnoticed in the English-speaking security community.

Last week, OpenAI released Privacy Filter, an open-weight model for detecting and redacting PII in text. It is a thoughtful release: Apache 2.0 licensed, able to run locally, designed for high-throughput workflows, and built to go beyond regex-based detection. This is good news for everyone building enterprise AI. Privacy at the model layer is getting real attention. What we liked most was how clearly OpenAI described the role of the model.