Threat intelligence research from major technology firms indicates that offensive uses of AI are expanding rapidly. Security teams have observed AI tools capable of bypassing established safeguards, automatically generating malicious scripts, and evading detection mechanisms with minimal human involvement. In some cases, AI-driven orchestration has been used to coordinate multiple malware components, allowing attackers to conduct reconnaissance, identify vulnerabilities, move laterally through networks, and extract sensitive data at machine speed. These automated operations can unfold faster than manual security workflows can reasonably respond.
What distinguishes these attacks from earlier generations is not the underlying techniques, but the scale and efficiency at which they can be executed. Credential abuse, for example, is not new, but AI enables attackers to harvest and exploit credentials across large environments with only minimal input. Research published in mid-2025 highlighted dozens of ways autonomous AI agents could be deployed against enterprise systems, effectively expanding the attack surface beyond conventional trust boundaries and security assumptions.
This evolving threat landscape has reinforced the relevance of zero trust principles, which assume no user, device, or connection should be trusted by default. However, zero trust alone is not sufficient. Security operations teams must also be able to detect abnormal behavior regardless of where it originates, especially as AI-driven attacks increasingly rely on legitimate tools and system processes to hide in plain sight.
As a result, organizations are placing renewed emphasis on network detection and response technologies. Unlike legacy defenses that depend heavily on known signatures or manual investigation, modern NDR platforms continuously analyze network traffic to identify suspicious patterns and anomalous behavior in real time. This visibility allows security teams to spot rapid reconnaissance activity, unusual data movement, or unexpected protocol usage that may signal AI-assisted attacks.
NDR systems also help security teams understand broader trends across enterprise and cloud environments. By comparing current activity against historical baselines, these tools can highlight deviations that would otherwise go unnoticed, such as sudden changes in encrypted traffic levels or new outbound connections from systems that rarely communicate externally. Capturing and storing this data enables deeper forensic analysis and supports long-term threat hunting.
Crucially, NDR platforms use automation and behavioral analysis to classify activity as benign, suspicious, or malicious, reducing alert fatigue for security analysts. Even when traffic is encrypted, network-level context can reveal patterns consistent with abuse. As attackers increasingly rely on AI to mask their movements, the ability to rapidly triage and respond becomes essential.
By delivering comprehensive network visibility and faster response capabilities, NDR solutions help organizations reduce risk, limit the impact of breaches, and prepare for a future where AI-driven threats continue to evolve.
