Joe is the VP of Product Marketing at MixMode. He has led product marketing for multiple cybersecurity companies, with stops at Anomali, FireEye, Neustar and Nextel, as well as various start-ups. Originally from NY, Joe resides outside Washington DC and has a BA from Iona University.
Network detection and response (NDR) is a critical component in the fight against sophisticated cyber threats. The Gartner® Market Guide for Network Detection and Response sheds light on the key trends and recommendations for security and risk management leaders looking to leverage NDR capabilities.
MixMode was listed as a representative vendor within the market guide, offering advanced AI capabilities that enhance network detection and response.
Overview of the NDR Market
The Gartner report notes that the NDR market continues to grow, with organizations increasingly seeking solutions that can provide comprehensive visibility across hybrid network environments, including on-premises infrastructure, cloud deployments, and operational technology (OT) systems. Recognizing this shift, the report emphasizes the importance of NDR solutions that can adapt to these diverse network scenarios.
Notably, the guide also underscores AI’s transformative impact, particularly in the form of self-supervised and explainable techniques. This aligns perfectly with MixMode’s pioneering work in third-wave AI, which enables MixMode’s platform to continuously learn and adapt to the unique dynamics of each customer’s network without relying on predefined rules or extensive training data.
Example Gartner Key Findings:
- NDR is commonly used as a complementary detection and response technology, working with other security operations center (SOC) tools like SOAR, SIEM, and EDR.
- -The emergence of “AI-augmented” analytics, in the form of SOC assistants, will benefit the NDR market by providing aggregated and summarized insights from the wealth of network data.
Example Gartner Key Recommendations:
- Start small by implementing NDR to detect abnormal behaviors and provide investigation capabilities for post-breach activity, gradually expanding across different network types.
- Identify how NDR’s behavior-based detections can augment your SOC’s ability to respond to incidents faster and more accurately.
What Makes a Robust NDR Solution?
At the core of an effective NDR solution are several critical capabilities:
1. Behavioral Analytics: NDR products must be able to model normal network traffic patterns and highlight unusual activity that falls outside the expected range. This includes using machine learning (ML) and advanced analytics to detect network anomalies.
2. Alert Aggregation and Incident Response: NDR solutions must aggregate individual alerts into structured incidents, facilitating threat investigation and enabling automatic and manual response capabilities to address detected malicious network traffic.
3. Hybrid Network Visibility: As organizations embrace a hybrid approach, encompassing on-premises infrastructure, cloud environments (IaaS), and operational technology (OT) networks, NDR solutions must adapt to provide comprehensive visibility across these diverse network scenarios.
4. Low False Positive Rate: After initial tuning, NDR solutions must maintain a low false positive rate to become a trustworthy source of insights and support automated response use cases.
How AI Enhances Network Detection and Response
The power of AI, particularly self-supervised and explainable AI, is transforming the NDR landscape. MixMode, a leading provider of AI-driven cybersecurity solutions, has pioneered the use of Third-wave AI to redefine network detection and response that includes:
Self-Supervised Learning: Unlike traditional ML approaches that require large amounts of labeled data, self-supervised learning enables NDR solutions to learn and improve continuously without relying on labeled data. This is a game-changer in the constantly evolving world of cyber threats, where acquiring labeled data for every potential attack is virtually impossible.
Anomaly Detection: Self-supervised learning algorithms in NDR solutions can detect anomalies by learning the underlying patterns and regularities in network data. This allows them to identify deviations from the norm, even when faced with zero-day attacks or previously unknown vulnerabilities.
Interpretability: The transparency and explainability of AI-powered NDR solutions are crucial for security analysts to understand and trust the decision-making process. Self-supervised learning algorithms provide insights into the features and representations learned by the AI, enabling better collaboration between humans and machines in identifying and responding to threats.
Predictive Capabilities: MixMode’s AI-driven NDR solution can detect anomalies and predict future events based on its learned understanding of the network environment. This proactive approach empowers security teams to anticipate and mitigate threats before they can cause significant damage.
NDR: A Key Component of Defense in Depth
A defense-in-depth strategy is essential to keeping pace with an ever-evolving threat landscape. NDR solutions increasingly incorporate additional security capabilities to enhance their detection and response capabilities.
Some NDR providers have re-added IPS-like modules, combining threat intelligence and traditional pattern matching to complement their behavioral analysis. This approach creates a more comprehensive security sensor capable of detecting a broader set of anomalies, including data exfiltration and command-and-control communication.
This “defense in depth” scenario can be particularly appealing to large security operation teams searching for a customizable and multipurpose network security solution. However, it’s essential to balance the added capabilities with the need to maintain NDR’s reputation as a “turnkey” and “low-noise” product, highlighting only the most critical anomalies.
Evaluating Advanced NDR Solutions
When evaluating NDR vendors, some of Gartner’s suggestions for security and risk management leaders include:
1. Pure-play NDR vs. NDR as a Feature:
Assess the cost, deployment requirements, complexity, and efficiency of the NDR detections before deciding between a standalone NDR solution or one integrated feature within another technology (e.g., SIEM or XDR).
2. Hybrid Network Visibility:
Ensure the NDR solution can provide comprehensive coverage across various network environments, including on-premises IT, cloud (IaaS), operational technology (OT), and even remote/home user networks.
3. Detection Capabilities:
Understand the detection techniques employed by the NDR solution, such as machine learning, threat intelligence, and signature-based approaches. Evaluate the overall effectiveness in detecting critical incidents.
4. False Positive Management:
Assess the NDR vendor’s track record in helping organizations minimize false positives through customizable thresholds, fine-tuning behavioral biometrics, and effective incident prioritization.
The Emergence of Augmented NDR
The cybersecurity landscape is experiencing a transformative shift with the emergence of large language model (LLM) features in security operation tools. This development is poised to disrupt the dynamics of the detection and response market.
With their inherent network visibility, NDR solutions are well-positioned to leverage these new AI techniques for incident summarization and analysis. By integrating LLM-powered SOC assistants, NDR providers can offer enhanced incident reporting, threat hunting, and response capabilities that complement their core network detection and analysis functionalities.
However, as these AI-augmented NDR solutions emerge, they must differentiate themselves from the growing XDR market, where vendors also incorporate similar AI-powered analytics and assistant features.
Fortify Your Defenses with AI-Driven NDR Solutions
Network detection and response (NDR) has become crucial to a robust defense-in-depth strategy. By leveraging the power of AI, particularly self-supervised and explainable techniques, advanced NDR solutions like MixMode are redefining how organizations detect, respond to, and predict cyber threats.
As the NDR market evolves, security and risk management leaders must carefully evaluate NDR vendors based on their ability to provide comprehensive hybrid network visibility, effective behavioral analytics, low false positive rates, and seamless integration with existing security workflows. By embracing the transformative potential of AI-driven NDR, organizations can enhance their cybersecurity posture and stay one step ahead of the ever-changing threat landscape.
Download Gartner’s complete Market Guide for Network Detection and Response to learn more, or reach out to MixMode to see how we can help.
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