From Potential to Performance: Maximizing AI's Impact with Modern NDR

Artificial intelligence (AI) and machine learning have become foundational components for effective network detection and response (NDR), fundamentally reshaping how organizations defend against modern threats.

According to the 2024 Global Cyber Confidence Index, 38% of security decision-makers said using AI and machine learning to help manage and mitigate cyber risk is a top priority for their organization this year.

Despite this clear priority, enterprises are only just beginning to really understand the transformational benefits that intelligent NDR solutions can offer.

AI Applications in NDR Today

From real-time anomaly detection and behavioral analytics to automated incident triage and even predicting emerging attack patterns, AI-driven NDR platforms are making significant strides, empowering SOC teams to operate with unprecedented speed, precision, and efficiency.

Accelerate threat hunting

Many NDR platforms are already leveraging AI and machine learning to proactively hunt for subtle anomalies and deviations—like unusual data flows, suspicious communication patterns, or hidden command-and-control activity—that could indicate a sophisticated threat before it escalates.

In an era marked by a widening skills gap, security teams are increasingly challenged to interpret the sheer volume of data and anomalies surfaced by AI. Proactive hunting for deviations is critical, but it's no longer sufficient for those who need to quickly identify real concerns.

With the ExtraHop AI Search Assistant, security analysts can use natural language queries to hunt for threats, eliminating the need for complex syntax or deep specialized knowledge. Instead of just flagging an anomaly, the AI Search Assistant empowers teams to simply ask questions like “Which workstations are not running an endpoint agent?” or “Which devices have attributes associated with a known security threat?”— receiving clear, actionable insights that bridge the gap between raw data and rapid, effective threat detection.

Prioritize alerts

Analysts are constantly bombarded with alerts, many of which turn out to be low-priority or even false positives. This "alert fatigue" isn't just a minor annoyance; it's a critical operational challenge that leads to missed threats, delayed responses, and ultimately, analyst burnout.

To combat this, NDR platforms use AI to surface high-risk threats, guiding analysts through the noise.

The Smart Triage feature from ExtraHop acts as an intelligent co-pilot to pinpoint the most critical detections. Automatically prioritizing detections based on factors like their involvement with high-value assets, the presence of "top offender" devices, the rarity of the detection type, and alignment with known threats, ExtraHop ensures security teams focus only on what truly matters, drastically reducing noise and accelerating response to critical threats.

Investigate an attack

While detection prioritization is a crucial first step, it's merely the beginning of the battle. Once an alert is flagged as high-priority, analysts face the daunting task of investigating it. This manual and often fragmented process severely diminishes their ability to identify critical issues swiftly, giving threat actors more time to escalate their attacks.

ExtraHop’s Smart Investigations is specifically designed to accelerate this critical response phase. By automatically generating comprehensive investigations, it aggregates all relevant detections that match a high-risk attack pattern, quickly stitching together disparate pieces of information to convey the full story of an attack. This cuts down hours of manual correlation, empowering analysts to understand the scope and nature of a threat in seconds.

Scalable AI and Machine Learning for Superior Threat Detection and Response

AI shows immense promise in cybersecurity, but many organizations are challenged by the sheer volume of data being generated by diverse sources — cloud environments, containers, IoT devices, and more.

This data explosion makes traditional on-premise AI and machine learning increasingly limiting.

Constrained by finite local resources, most systems struggle to collect the comprehensive data needed to ensure models are truly accurate and effective, directly hindering your ability to quickly detect and respond to threats.

To truly harness the power of AI for modern security, organizations need cloud-scale machine learning.

Cloud-scale machine learning provides the elastic compute power and expansive storage necessary to train sophisticated models on the massive, ever-growing datasets required for accurate and real-time threat detection.

Unlike other solutions that strain on-premise network sensor appliances with heavy AI workloads, ExtraHop moves these compute-intensive tasks to a dedicated cloud. Here, we leverage nearly unlimited compute and storage, providing 10x more computational power across your entire hybrid environment, whether it's on-premise or in the public cloud.

With this approach, you can run more sophisticated AI models, leading to more accurate detections, faster analysis, and deeper insights into complex, multi-stage attacks.

More models translate directly to:

• Expanded Threat Coverage: A larger number of diverse models allows the platform to cover a much wider spectrum of threats with tailored precision. This also creates a multi-layered detection approach; If one model misses a subtle indicator, another, specialized model can catch it, increasing the overall probability of detection for complex, multi-stage attacks.
• Increased Accuracy and Reduced False Positives: The power of multiple models lies in their ability to cross-validate findings. When independent models corroborate suspicious activity, the confidence in an alert soars, drastically cutting down on false positives. Each model also contributes unique contextual insights, building a richer understanding of behaviors for more precise and actionable alerts.
• Adaptability to Evolving Threats: As threat actors constantly evolve their tactics, techniques, and procedures (TTPs), a platform with many sophisticated models can adapt more quickly. New models can be developed and deployed to target emerging threats without having to overhaul a single, monolithic AI engine.

Smarter Security that Scales

AI holds immense promise for NDR, but enterprises must be strategic in its adoption. To truly deliver smarter security that scales, it must grow alongside your data, rather than being limited by it.

AI is only as good as the data it gets.

That's precisely why our cloud-scale machine learning processes a massive volume of network telemetry, ensuring our AI catches everything and provides the most precise insights possible. This approach means nothing gets missed, leading to faster, more comprehensive security that effectively neutralizes threats, keeping your team agile and your organization secure.

See why ExtraHop is a Leader in the Gartner® Magic Quadrant™ for Network Detection and Response.