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Integrating Network Detection and Response (NDR) tools with other cybersecurity solutions enhances an organization’s threat detection and response capabilities. NDR tools monitor network traffic to identify suspicious activities and patterns, making them an excellent complement to Endpoint Detection and Response (EDR) and Security Information and Event Management (SIEM) systems. NDR helps to fill the gaps left by EDR, expanding visibility from individual devices to network-wide threats, offering a broader perspective on attacks that may bypass traditional endpoint defenses.

NDR tools can also be integrated with firewalls to provide enriched threat intelligence respond to network threats. When unusual traffic patterns or network anomalies are detected, NDR can alert the firewall, which may then block or restrict access based on predefined rules. Additionally, NDR integrates well with SIEM systems, feeding network-level data into the SIEM for centralized monitoring and comprehensive analysis of potential threats across an organization. By combining these technologies, security teams gain holistic visibility and can correlate data from various sources for a stronger defense against multi-stage attacks.

Artificial Intelligence (AI) has become crucial in network security, enabling faster and more accurate threat detection, investigation and response. AI-powered network security tools, such as NDR solutions, often leverage machine learning algorithms to analyze vast amounts of data and identify patterns of abnormal behavior. This is particularly useful in detecting zero-day threats, malicious insiders and advanced persistent threats (APTs) that traditional network security technologies may miss.

When applied correctly AI can help reduce alert fatigue by filtering out false positives, which is a common issue in traditional network monitoring. By automating routine threat detection tasks, AI allows security analysts to focus on investigating high-priority incidents, enhancing both efficiency and accuracy.

  

Most NDR vendors on the market and traditional solutions such as Intrusion Detection Systems (IDS) or Intrusion Prevention Systems (IPS) rely on detecting known attacks with historical data such as external threat intelligence, signatures and detection rules, leaving organizations vulnerable to novel threats. This legacy approach to threat detection means that at least one organization needs to fall victim of a novel attack before it is formally identified. It also produces a vast number of false positives, since most vendors apply models that are trained globally and are not specific to a particular organization or the context of their unique environment. 

When choosing an NDR solution, it’s essential to evaluate specific features that enhance network visibility, threat detection, investigation and response capabilities. A robust NDR solution should include advanced anomaly detection powered by AI and unsupervised machine learning, enabling it to detect deviations from normal network behavior. Darktrace / NETWORK uses Self-Learning AI to learn what is normal behavior for your organization, detecting any activity that could cause business disruption, and autonomously responds to both known and previously unseen threats in real time. 
  
Another key feature is integration with other cybersecurity tools, such as SIEM, EDR, and firewalls. This compatibility ensures that the NDR solution can feed valuable data into a centralized system, providing a comprehensive view of potential threats across the organization. Additionally, autonomous response capabilities are crucial, allowing the NDR to take immediate action and choose the most effective action without causing business disruption, such as isolating infected devices, forcing a user to reauthenticate or blocking suspicious IP addresses.

An NDR solution should also offer scalable architecture to support growing network environments, along with real-time alerting and detailed forensic capabilities. These allow security teams to investigate incidents thoroughly and trace the origin of attacks. Unlike chat or prompt-based LLMs that create static incident summaries, Darktrace’s Cyber AI Analyst continually analyzes and contextualizes every relevant alert in your network, autonomously forming hypotheses and reaching conclusions just like a human analyst would, saving your team a significant amount of time and resources. 

  

NDR (Network Detection and Response) and EDR (Endpoint Detection and Response) serve complementary roles in cybersecurity, but they focus on different areas. NDR tools monitor network traffic to detect threats that traverse or originate within the network, such as lateral movement by malicious actors. In contrast, EDR focuses on endpoints—such as laptops, servers, and mobile devices—detecting suspicious activities at the device level, such as unauthorized file modifications or unusual program executions.

While EDR is adept at identifying threats targeting individual endpoints, NDR provides a broader perspective, focusing on network-wide patterns and behaviors. For example, an NDR tool can detect an attacker moving across the network or attempting to establish command-and-control communications. When combined, NDR and EDR offer a more comprehensive defense, with NDR handling network-level anomalies and EDR focusing on endpoint-specific threats.

NDR (Network Detection and Response) and NTA (Network Traffic Analysis) both monitor network traffic, but they differ in functionality and purpose. NTA tools primarily focus on passive monitoring and analysis of network traffic to help detect unusual patterns, and performance-related issues, while NDR is focused on security and adds a response capability. NTA systems can detect anomalies in network traffic but typically do not provide automated response capabilities, which is what NDR solutions are designed to do.

NDR and SIEM (Security Information and Event Management) serve distinct but complementary functions in cybersecurity. NDR tools focus specifically on analyzing and responding to network traffic, providing deep insights into network-based threats like lateral movement or abnormal traffic flows. SIEM systems, on the other hand, aggregate and analyze data from multiple sources, including network traffic, endpoints, and applications to provide a centralized view of an organization’s security posture.

Threat detection and response (TDR) is essential for organizations as it provides the ability to identify and mitigate cyber threats in real-time, reducing the risk of data breaches, service interruptions, and financial losses. With the increasing sophistication of cyber-attacks, timely TDR is crucial to prevent threats from escalating and spreading across the network.

  

TDR encompasses tools like NDR and EDR, which offer network and endpoint protection. By monitoring both network traffic and endpoint activity, these solutions enable security teams to detect threats that bypass traditional perimeter defenses. Network detection and response, in particular, plays a key role in identifying threats like lateral movement, ransomware, command-and-control activities, and insider threats.