How to Achieve the Best Risk-Based Alerting (Bye-Bye SIEM)

Did you know that Network Detection and Response (NDR) has become the most effective technology to detect cyber threats? In contrast to SIEM, NDR offers adaptive cybersecurity with reduced false alerts and efficient threat response.

Are you aware of Network Detection and Response (NDR) and how it’s become the most effective technology to detect cyber threats?

NDR massively upgrades your security through risk-based alerting, prioritizing alerts based on the potential risk to your organization’s systems and data. How? Well, NDR’s real-time analysis, machine learning, and threat intelligence provide immediate detection, reducing alert fatigue and enabling better decision-making. In contrast to SIEM, NDR offers adaptive cybersecurity with reduced false positives and efficient threat response.

Why Use Risk-Based Alerting?

Risk-based alerting is an approach where 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 enables organizations to concentrate their resources on addressing the most critical threats first.

Benefits of risk-based alerting include efficient resource allocation and more:

1. By prioritizing alerts based on risk, organizations can allocate their resources more efficiently, since they save time.
2. High-risk alerts can be addressed promptly, while lower-risk alerts can be managed in a more systematic and less resource-intensive manner.
3. Security teams often face alert fatigue when dealing with a high number of alerts, many of which may be false positives or minor issues. So, risk-based alerting helps reduce alert fatigue by allowing teams to focus on alerts with the greatest potential impact. This can be crucial in preventing or minimizing the effect of security incidents.
4. Prioritizing alerts based on risk enables 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.
5. It also promotes the integration of threat intelligence into the decision-making process. By considering the context of threats and understanding their potential impact, organizations can better assess the severity of alerts.

3 Steps to Establishing 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 your network and provide insights into the potential risks of various activities or incidents: they analyze the patterns and behavior of network traffic to detect anomalies that indicate potential security risks.

With this contextual information about network activity, different weights of 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. Additionally, specific critical zones can be defined in asset management. This context is crucial for evaluating the severity and potential impact of security alerts, aligning with the risk-based approach.

2. Leveraging Threat Intelligence Feeds for Enhanced Risk Assessment

Since NDR solutions are integrated with threat intelligence feeds, they enrich the data used for the analysis and categorization of network activity. Criticality can potentially be enhanced by OSINT, Zeek, or MITRE ATT&CK information. This integration enhances the ability to assess the risk associated with specific alerts.

Some NDR systems offer automated response capabilities, serving organizations in responding quickly to high-risk alerts. This aligns with the goal of risk-based alerting to address critical threats immediately:

• A risk score is assigned to detected events or alerts based on various factors, including the severity of the detected activity, the context in which it occurred, the affected assets or systems, and historical data. The aim is to assess the potential damage or impact of the detected event.
• In the risk booster, different elements influencing risk assessment are weighted differently. For example, activities involving critical assets or privileged accounts may receive a higher risk score. Events deviating significantly from established baselines or patterns may also be weighted more heavily.
• Correlated alerts play a crucial role in uncovering hidden attacks within the background of normal network activities. Increased correlation of alerts significantly reduces the workload for analysts by minimizing the number of individual alerts they must address.

3. Automating Responses to High-Risk Alerts

The strategic use of automation is of utmost importance in strengthening network defenses against potential attacks, particularly considering the substantial daily communication volumes within networks that attackers could exploit.

Since user and entity behavior analysis is 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 user behavior can be detected more easily and used for risk assessment.

Because risk scores are not static but change over time, they can be adjusted as new information becomes available or the security landscape evolves. If an originally low-risk event escalates to a higher-risk event, the risk score is adjusted accordingly.

Leveraging NDR with Machine Learning For Dynamic Risk Assessment and Enhanced Cybersecurity

Machine learning algorithms can sift through large volumes of data to establish standard patterns or baselines of network behavior. These baselines act as a benchmark for identifying deviations that could signal suspicious or malicious activity. The automation allows security teams to concentrate their efforts on investigating and mitigating high-risk alerts, enhancing overall efficiency. Machine learning algorithms can continuously learn and adapt to new patterns and threats, making the security system more adaptive and capable of tackling emerging risks. The continuous learning is invaluable in the rapidly evolving landscape of cybersecurity.

By integrating NDR capabilities with machine learning, organizations can dynamically evaluate the risk associated with various activities on the network. Machine learning algorithms can adapt to evolving threats and changes in network behavior, contributing to a more precise and responsive risk assessment.

Examples & Use Cases: More Detection, Less False Alerts

Given an organization utilizes a Network Detection and Response (NDR) solution to monitor its network traffic, the organization assesses 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.

Anomalous behavior: The IP address has no prior history of accessing this server. The risk score is high. The NDR system assigns a high-risk score to the alert due to the involvement of a critical asset and the detection of anomalous behavior, suggesting a potential security breach. The high-risk alert is promptly escalated for investigation and response.

2. Software Update:

In this alert, a routine software update event is described, where an internal device initiates an update from a trusted source. The risk factors include the affected asset (a non-critical user workstation) and the routine behavior of the update from a trusted source, resulting in a low-risk score.

The NDR system assigns a low-risk score to this alert, indicating that it involves a non-critical asset, and the behavior is routine and expected. As a result, this low-risk alert may be logged and monitored but does not require immediate attention.

Conclusion: That’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, whereas SIEM relies on log analysis only, which may have delays and might miss subtle, network-centric threats as well as creating multitudes of alerts (false ones too).

Last but not least, NDR incorporates machine learning and threat intelligence, enhancing its ability to adapt to evolving risks and reducing false positives, leading to more accurate and timely risk assessments compared to traditional SIEM approaches.

So, ready to upgrade and enhance your detection capabilities? If you’re still contemplating, download our new Security Detection whitepaper for a deep dive into how risk-based alerting can save you costs and time and drastically reduce your false alerts.