Best Practices for Maximising the Use of Redborder NDR
Network Detection and Response (NDR) has become an essential tool for protecting IT infrastructures against advanced cyber threats. redborder’s NDR solution combines artificial intelligence, network traffic analysis, and automation capabilities to provide robust and effective defence. Below, we explore the best practices for maximising the use of these solutions and key use cases that highlight their value.
Best Practices
- Strategic implementation
- Comprehensive monitoring: Ensure that all critical parts of your network are monitored. This includes servers (to detect potential vulnerabilities or unusual activities), endpoints (such as workstations and mobile devices which could be entry points for threats), and IoT devices (the proliferation of IoT devices in the network can introduce significant risks. It is crucial to include them in monitoring to identify anomalous behaviours that might indicate a security compromise). Broad coverage allows for the detection of threats at any point in the network.
- Integration with other systems: redborder NDR integrates with Endpoint Detection and Response (EDR) and Extended Detection and Response (XDR) systems to provide a more robust defence. This facilitates data correlation from various sources, enhancing the ability to detect and respond to complex threats. It offers a comprehensive view and coordinated response to security incidents. Integration also provides a unified view of the network, improving the capability to detect attack patterns and conduct more effective forensic analysis. Additionally, it ensures a more coordinated and effective response to security incidents, making sure all parts of the system collaborate to mitigate risks.
- Optimization of traffic analysis
- Real-time analysis: Utilise Redborder NDR’s capability to analyse network traffic in real time. This helps quickly identify suspicious behaviours, which is crucial for reducing exposure time. The ability to adjust alert thresholds and policies based on real-time analysis ensures a more precise and contextualised response.
- Use of artificial intelligence: redborder NDR employs artificial intelligence (AI) to identify malicious behaviour patterns. AI can detect anomalies and patterns that might be missed by traditional methods, enhancing the response to emerging and sophisticated threats. Leverage machine learning capabilities to continuously adjust detection algorithms based on new threats and observed behaviours. This allows the system to adapt and improve its accuracy over time.
- Automation and response
- Response automation: define response policies that enable the system to take automatic actions in response to common incidents. This can include isolating compromised devices, interrupting suspicious connections, or implementing containment measures to minimise the impact of an attack. Redborder NDR coordinates incident response across multiple systems and teams, allowing for centralised and coordinated management of responses, improving effectiveness and reducing the time needed to resolve incidents.
- Telemetry and data collection: Implement the collection and analysis of network traffic metadata to gain a detailed view of network behaviour. Telemetry provides valuable data for post-incident analysis, allowing for thorough investigation and identification of recurring patterns. The collected data is used to review and adjust security policies and operational procedures. Information gathered from events and alerts facilitates continuous improvement in protection and response strategies.
Implementing and optimising the use of Redborder NDR not only strengthens an organisation’s security posture but also ensures proactive protection against a wide range of advanced threats. By applying these strategic practices and leveraging Redborder’s advanced technology, one can significantly enhance the effectiveness of detecting, analysing, and responding to security incidents.
