April 30, 2024

Essential AI-Enhanced Use Cases for Managed Detection and Response (MDR)

Total Article Read Time: 2 min

When speaking with companies that have fewer than 300 employees, it's common to find that they lack a large enough team to operate a Security Operations Center (SOC). As these organizations grow and begin to prioritize cybersecurity more seriously, they often choose to partner with a Managed Detection and Response (MDR) provider.

A frequent question in these discussions revolves around which use cases should be prioritized when first implementing MDR. This paper aims to address that question, focusing on use cases that offer the maximum return on investment (ROI) in terms of risk reduction relative to deployment effort.

Caveat: This discussion will primarily focus on detection and response technologies rather than protection technologies. While protection is critical—with foundational measures like firewalls, two-factor authentication, and robust access management—it's essential these are established before exploring detection and response options.

With this in mind, let's explore the foundational use cases for MDR:

Endpoint Detection and Response (EDR)

EDR is crucial if you lack a comprehensive solution, as threats such as malware can compromise your enterprise through just one infected endpoint. The latest EDR solutions offer significant value for the investment, particularly when enhanced by AI.

AI Integration:

  • AI dramatically improves EDR by automating the detection processes, using algorithms to swiftly identify threats from patterns and anomalies in endpoint data, thereby reducing response times and increasing accuracy.

Cloud Detection and Response

As cloud infrastructure has become more complex, the risk of misconfigurations that expose sensitive data has increased. Cloud providers offer extensive telemetry data accessible via APIs, yet many companies lack effective mechanisms to detect data breaches or unauthorized access.

 AI Integration:

  • AI tools can analyze this telemetry to spot unusual activities, using advanced analytics to bolster the detection of sophisticated attacks and unauthorized data extraction.

Access and Authentication

Audit data generated by most SaaS solutions and Identity and Access Providers is pivotal for detecting account takeovers. Many teams, however, do not have the resources to monitor and respond to these threats effectively.

AI Integration:

  • AI enhances these processes by analyzing behavioral patterns and identifying anomalies that may indicate unauthorized access attempts, thus bolstering overall security.

Phishing

Despite advances in phishing protection, no solution offers complete prevention, and it only takes a few emails to breach a company's defenses.

AI Integration:

  • AI helps by analyzing incoming emails for phishing indicators, using natural language processing and machine learning to enhance filtering and block potentially harmful communications before they reach end users.

Network Detection and Response

Network data, while not wholly revealing on its own, is vital for triaging and investigating alerts. It can help determine if an attacker has used one compromised endpoint to pivot to others within the network.

AI Integration:

  • AI processes network traffic in real-time, identifying attack patterns and correlating data across the network to prioritize threats and streamline responses.

Vulnerability Management

Vulnerability management extends beyond traditional detection and response but is crucial for proactive security posture. It involves identifying vulnerabilities like Log4J quickly and determining which services need patches.

AI Integration:

  • AI automates this process, continuously scanning for vulnerabilities and prioritizing them based on the risk they pose, thereby facilitating quicker and more effective responses.

Application Logs

For companies that develop and deploy their applications, maintaining extensive logs is essential, especially during incidents like the Log4J vulnerability.

AI Integration:

  • AI aids in managing these logs by parsing and correlating data across applications to detect anomalies and streamline investigative processes.

While more mature security teams may have additional use cases in place, the ones listed here provide a solid foundation for small to mid-sized enterprises looking to enhance their cybersecurity capabilities. Integrating AI into these MDR strategies can significantly improve an organization's ability to detect, investigate, and respond to threats more efficiently and effectively.

Summary

While more mature security teams may have additional use cases in place, the ones listed here provide a solid foundation for small to mid-sized enterprises looking to enhance their cybersecurity capabilities. Integrating AI into these MDR strategies can significantly improve an organization's ability to detect, investigate, and respond to threats more efficiently and effectively.

To learn more, please request a conversation with an AirMDR cybersecurity analyst or sign up for a demo.

Kumar Saurabh
AUTHOR: Kumar Saurabh

Kumar Saurabh is the CEO and co-founder of AirMDR. He's leveraged his 20+ years of experience in enterprise security to develop an AI-powered virtual assistant for security operations to help address shortages in cybersecurity talent resources. Kumar's expertise spans from his engineering leadership at ArcSight to co-founding a company that offers cloud SIEM services and his role as CEO of LogicHub, where he focuses on SOAR/MDR.


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