Machine Learning and Artificial Intelligence (AI/ML): The Secret Sauce Behind XDR
What Is XDR?
Extended Detection and Response (XDR) is a security solution that combines multiple detection and response technologies across different security domains, such as endpoint protection, network security, and cloud security. The goal of XDR is to provide a more comprehensive view of an organization’s security posture, making it easier to recognize and respond to various security threats.
The main features of XDR solutions typically include:
- Multi-layered threat detection: XDR solutions combine data from different security domains, such as endpoint protection, network security, and cloud security, to provide a more comprehensive view of an organization’s security posture. This allows for the detection of threats that may not be visible to a single security system.
- Advanced analytics: XDR solutions use advanced analytics techniques supported by machine learning (ML) models, to identify potential threats and to automate response actions.
- Automated response: XDR solutions can automatically block or quarantine malicious files and alert security teams to potential incidents.
- Single pane of glass view: XDR solutions provide a unified view of all security events and incidents, making it easier for security teams to investigate and respond to threats.
- Endpoint protection: XDR solutions also provide endpoint protection and management, which help to detect and prevent malware, ransomware, and other types of attacks on endpoint devices.
- Cloud security: XDR solutions also provide cloud security, which help to detect and prevent threats in the cloud environment.
- Compliance and governance: Some XDR solutions also provide compliance and governance features, which help organizations to meet regulatory requirements and adhere to security best practices.
XDR vs. Other Detection and Response Technologies
XDR is a more comprehensive security solution than traditional detection and response technologies. The main differences include:
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XDR solutions provide a broader view of an organization’s security posture by integrating data from different security domains, such as endpoint protection, network security, and cloud security. Traditional detection and response technologies, such as Security Information and Event Management (SIEM) and Endpoint Detection and Response (EDR), typically focus on a single security domain.
XDR solutions use advanced analytics and automation to identify potential threats and take response actions, such as blocking or quarantining malicious files. Traditional detection and response technologies may rely more on human intervention to analyze and respond to threats.
XDR solutions integrate data from different security systems and provide a single pane of glass view of all security events and incidents. Traditional detection and response technologies may require manually integrating data from different systems.
AI-powered XDR solutions can be more effective than traditional detection and response technologies that rely on rule-based or signature-based detection methods, as they can detect unknown or zero-day threats.
By using machine learning, an AI-driven XDR solution can continuously learn, adapt and improve its threat detection and response capabilities. This can help to reduce the workload on security teams and improve the organization’s overall security posture. XDR uses both supervised and unsupervised ML techniques:
- Supervised machine learning: XDR solutions can identify various entities on a network, including Windows computers, Android smartphones, or email servers. Using known malicious or suspicious security events as the training data, it is possible to train a large supervised algorithm on certain traffic related to a set of threats or entities. The algorithm can then perform inference to recognize suspicious activity during runtime. Using this method can significantly reduce false positives.
- Unsupervised machine learning: XDR solutions often leverage unsupervised ML algorithms to establish a baseline of users’ and devices’ behavior and identify groups of users (peers). This baseline determines what behavior is expected for each entity. Models can compare past, current, and peer-user behavior to detect data exfiltration, malware, lateral movement, and command-and-control communication.
Here are several ways in which AI-powered XDR solutions use ML algorithms to detect and respond to security threats:
- Anomaly detection: ML algorithms are used to analyze data from different security domains to identify patterns of behavior that deviate from the norm. This can help to detect unknown or zero-day threats that may not be visible to traditional detection and response technologies.
- Threat intelligence: ML algorithms can also be trained on historical data to learn the organization’s security environment and to identify new threats. This can help to improve the accuracy of threat detection over time.
- Tuning and adaptation: ML algorithms can be used to adjust the sensitivity of threat detection and response based on the organization’s security environment and the number of false positives or false negatives.
- Correlation: ML algorithms can also be used to analyze data from multiple sources, such as logs, network traffic, and endpoint data, to correlate different events and incidents, and to identify the full scope of a potential threat.
- Prioritization: ML algorithms can also be used to prioritize security incidents, so security teams can focus on the most critical threats first.
Machine learning models enable XDR solutions to do extensive and accurate analysis on data from across a range of technologies to accurately detect and respond to threats. AI-powered XDR solutions can aggregate data beyond the scope of SIEM to increase visibility and respond to many threats automatically.
Using ML-based prioritization capabilities and automated response, XDR can significantly reduce the burden placed on human operators and improve the organization’s security posture. Common ML techniques include unsupervised and supervised machine learning, but these are just a few broad examples to demonstrate the possibilities this technology presents.
About the Author
Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP, Imperva, Samsung NEXT, NetApp and Check Point, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership. Today he heads Agile SEO, the leading marketing agency in the technology industry. Connect on Linkedin.
Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.