Rethinking MDR as Attackers and Defenders Embrace AI
MDR services struggle to keep pace with AI-driven attacks, leaving many alerts uninvestigated.
Managed Detection and Response (MDR) services are becoming insufficient as attackers leverage AI for faster, more sophisticated attacks. A significant portion of alerts, around 60%, go unreviewed due to alert volume, with nearly 1% of real threats originating from these low-severity alerts. Furthermore, the quality of investigations varies greatly depending on the analyst and time of day, leading to missed threats and inconsistent security coverage.
AI summary of the linked source. Not reviewed by an editor. How this works
The lesson from this incident
Full lessonThe overwhelming volume of security alerts is creating dangerous gaps in threat detection, with 60% of alerts going unreviewed and critical threats hiding in low-severity notifications. Human analysts cannot keep pace with AI-powered attacks that operate at machine speed, leading to inconsistent investigation quality and missed threats. This alert fatigue combined with variable analyst performance creates systematic blind spots that attackers can exploit, demonstrating the urgent need for AI-enhanced detection and automated response capabilities.
What helps against this
How we list vendors- Endpoint detection and response (EDR and XDR)
Malware and hands-on-keyboard attackers on laptops and servers get spotted and stopped.
- Security monitoring (SIEM)
Logs from everywhere end up in one place, with alerts that someone actually sees.
- Managed detection and response (MDR)
A team watches and responds around the clock when you cannot.
Indicators of Compromise
Extracted by AI from the linked source. Not reviewed by an editor. How this works
- mitre_attack — T1566
- mitre_attack — T1071
- mitre_attack — T1027