Did you know that Network Detection and Response (NDR) has become the most effective technology for detecting cyber threats? Unlike SIEM, NDR offers adaptive cybersecurity with reduced false alarms and efficient threat response.
Are you familiar with Network Detection and Response (NDR) and how it has become the most effective technology for detecting cyber threats?
NDR dramatically improves your security through risk-based alerts, prioritizing alerts based on the potential risk to your organization’s systems and data. As? Well, NDR’s real-time analytics, machine learning, and threat intelligence provide immediate detection, reducing alert fatigue and enabling better decision making. Unlike SIEM, NDR offers adaptive cybersecurity with reduced false positives and efficient threat response.
Why use risk-based alerts?
Risk-based alerts are an approach in which security alerts and responses are prioritized based on the level of risk they pose to an organization’s systems, data, and overall security posture. This method allows organizations to focus their resources on addressing the most critical threats first.
The benefits of risk-based alerts include efficient resource allocation and more:
- By prioritizing alerts based on risk, organizations can allocate their resources more efficiently, as they save time.
- High-risk alerts can be addressed promptly, while low-risk alerts can be handled more systematically and with less expenditure of resources.
- Security teams often feel fatigued when dealing with a large number of alerts, many of which may be false positives or minor issues. Therefore, risk-based alerts help reduce alert fatigue by allowing teams to focus on alerts with the greatest potential impact. This can be critical to preventing or minimizing the effects of security incidents.
- Prioritizing alerts based on risk allows for better decision making. Security teams can make informed decisions about which alerts to investigate first and how to allocate resources based on the potential impact on the organization.
- It also promotes the integration of threat intelligence into decision-making. By considering the context of threats and understanding their potential impact, organizations can better assess the severity of alerts.
3 steps to define your risk-based cybersecurity strategy
1. The role of NDR in risk-based alerts
Network Detection and Response (NDR) plays a key role in facilitating or enabling the implementation of risk-based alerts within an organization’s cybersecurity strategy.
NDR solutions are designed to detect and respond to threats on the network and provide insights into potential risks of various activities or incidents: Analyze network traffic patterns and behavior to detect anomalies that indicate potential security risks.
With this contextual information about the activity of the network, the different weights of the analyzers in the network and an aggregation of various alarms up to the alarm threshold, they can define different alert levels depending on the weighting of the evidence. Furthermore, specific critical zones can be defined in resource management. This context is key to assessing the severity and potential impact of security alerts, in line with the risk-based approach.
2. Leverage threat intelligence feeds for better risk assessment
Because NDR solutions are integrated with threat intelligence feeds, they enrich the data used to analyze and categorize network activity. Criticality can potentially be improved by OSINT, Zeek or MITER ATT&CK information. This integration improves the ability to evaluate the risk associated with specific alerts.
Some NDR systems offer automated response capabilities, helping organizations respond quickly to high-risk alerts. This is in line with the goal of risk-based alerts to immediately address critical threats:
- Detected events or alerts are assigned a risk score based on various factors, including the severity of the detected activity, the context in which it occurred, the resources or systems affected, and historical data. The purpose is to evaluate the potential damage or impact of the detected event.
- In the risk booster, the different elements that influence the risk assessment are weighted differently. For example, activities involving critical resources or privileged accounts may receive a higher risk score. Events that deviate significantly from baselines or established patterns may also be weighted more heavily.
- Correlated alerts play a crucial role in uncovering hidden attacks as part of normal network activity. Greater alert correlation significantly reduces analysts’ workload by minimizing the number of individual alerts they have to deal with.
3. Automate responses to high-risk alerts
Strategic use of automation is of utmost importance to strengthen network defenses against potential attacks, especially considering the large volumes of daily communication within networks that attackers could exploit.
Since user and entity behavior analytics are already integrated into the NDR to analyze the behavior of users and entities (e.g., devices) within the network, insider threats, compromised accounts, or suspicious behavior of users can be detected more easily and used for risk assessment.
Because risk scores are not static but change over time, they can be modified as new information becomes available or the security landscape evolves. If an originally low-risk event turns into a higher-risk event, the risk score is adjusted accordingly.
Leverage NDR with machine learning for dynamic risk assessment and enhanced cybersecurity
Machine learning algorithms can sift through large volumes of data to establish standard models or baselines of network behavior. These baselines serve as a point of reference to identify deviations that could signal suspicious or malicious activity. Automation allows security teams to focus their efforts on investigating and mitigating high-risk alerts, improving overall efficiency. Machine learning algorithms can continually learn and adapt to new patterns and threats, making your security system more adaptive and capable of addressing emerging risks. Continuous learning is invaluable in the rapidly evolving cybersecurity landscape.
By integrating NDR capabilities with machine learning, organizations can dynamically assess the risk associated with various activities on the network. Machine learning algorithms can adapt to evolving threats and changes in network behavior, contributing to more precise and responsive risk assessment.
Examples and use cases: More detection, fewer false alerts
As an organization uses a Network Detection and Response (NDR) solution to monitor its network traffic, the organization evaluates risk scores for detected events based on their potential impact and contextual information.
1. Unauthorized access attempt:
An external IP address attempts to gain unauthorized access to a critical server. The risk factors are the affected asset: a critical server containing sensitive customer data.
Abnormal behavior: The IP address has no history of accessing this server. The risk score is high. The NDR system assigns the alert a high risk score due to the involvement of a critical asset and the detection of anomalous behavior, suggesting a potential security breach. High risk alert is promptly escalated for investigation and response.
2. Software Update:
This advisory describes a routine software update event, in which an internal device initiates an update from a trusted source. Risk factors include the affected resource (a non-critical user workstation) and the routine behavior of updating from a trusted source, resulting in a low risk score.
The NDR system assigns a low risk rating to this alert, indicating that it is a non-critical asset and that the behavior is routine and expected. As a result, this low-risk alert can be logged and monitored but does not require immediate attention.
Bottom line: Here’s why it’s superior to SIEM
NDR is considered superior to Security Information and Event Management (SIEM) for risk-based alerting because NDR focuses on real-time analysis of network traffic patterns and behaviors, providing immediate detection of anomalies and potential threats, while SIEM relies only on log analysis, which may be delayed and may miss subtle network-centric threats, as well as creating a multitude of (even false) alerts.
Last but not least, NDR incorporates machine learning and threat intelligence, improving its ability to adapt to evolving risks and reducing false positives, leading to more accurate and timely risk assessments than traditional SIEM approaches .
So, are you ready to upgrade and improve your detection skills? If you’re still wondering, download our new security detection whitepaper to learn more about how risk-based alerts can save you time and costs and dramatically reduce false alerts.
