Cyber attacks are getting harder to spot, and N-able is responding with a new wave of AI-driven detection capabilities designed to uncover threats that slip past traditional tools.
The company has introduced advanced AI-based detections within its Security Operations Centre, powered by Adlumin Managed Detection and Response. These updates are focused on identifying suspicious activity that blends into normal system behavior, something increasingly common in modern cyber attacks.
Why Traditional Detection Isn’t Enough
Attackers today aren’t always using obvious malware or triggering alarms on endpoints. Instead, they’re leveraging trusted system tools and legitimate network services to stay under the radar.
According to N-able’s 2026 State of the SOC Report, nearly half of observed attacks never even touched endpoint devices. Instead, they operated across networks, cloud environments, and identity systems, areas that older, endpoint-focused defenses often struggle to monitor effectively.
What’s New in N-able’s AI Detection
The latest update introduces three key detection capabilities, each targeting a different layer of potential attack activity.
PowerShell Behavior Analysis
The system now analyzes every PowerShell execution across monitored environments. This helps detect subtle misuse, even when the activity appears legitimate. It is particularly useful against living-off-the-land techniques, where attackers exploit built-in tools rather than deploying malware.
AI-Driven DNS Monitoring
A machine learning model examines DNS traffic for unusual patterns. This includes identifying behaviors linked to command-and-control communication, beaconing, and distributed denial-of-service activity, threats that often go unnoticed by endpoint security tools.
Single-Event Process Execution (SEPE)
This model evaluates Windows process activity at a granular level. Each event is analyzed based on attributes like process name, path, and parent processes, giving security analysts deeper context into what is happening behind the scenes.
The Rise of Stealth Attacks
These updates reflect a broader shift in cybersecurity. Instead of relying solely on rule-based alerts or known threat signatures, organizations are increasingly focusing on behavior across multiple layers.
Modern attackers are skilled at blending in. They mimic everyday operations, making malicious activity look like routine business processes. This makes early detection far more challenging and far more important.
As Troels Rasmussen, VP of Security at N-able, explained, today’s fastest-growing threats do not look suspicious at first glance. They look like normal activity. By correlating signals from PowerShell, DNS traffic, and process behavior, AI can uncover patterns that traditional tools might miss.
Why AI Matters in Modern Security
AI is becoming a key component in managed detection and response services. Unlike static rules, AI models can adapt to new behaviors and identify anomalies in real time, even when attackers change tactics.
For security teams, this has two major benefits:
- fewer false alarms, reducing alert fatigue
- more meaningful insights, helping analysts focus on real threats
This is especially important as organizations deal with growing volumes of data without significantly increasing their security staff.
A Shift Toward Layered Monitoring
Ultimately, N-able’s latest enhancements highlight an industry-wide move toward layered monitoring. Instead of watching just endpoints, security systems now need visibility across networks, cloud services, and user identities.
With over 500,000 organizations relying on its platform, N-able is positioning these AI-driven detections as a way to close gaps in visibility and improve early threat detection, especially against attacks designed to remain hidden for as long as possible.
