Unlocking the Power of Behavioral Cloud Native Threat Detection and Response

Behavioral detection and response is not a new concept, and the top three detection and response players command a combined market capitalization of $100 billion. But the rise of cloud native environments has presented both opportunities and challenges in this area. As organizations increasingly embrace microservices architecture, containers, and orchestration tools like Kubernetes to build scalable and resilient applications, the need for effective threat detection and response mechanisms has become paramount. So what does your organization need to know about behavioral cloud native threat detection and response?

Understanding Cloud Native Environments

Before diving into behavioral threat detection, it’s crucial to grasp the essence of cloud native environments. Unlike traditional legacy applications that are tightly bound to specific servers or VMs, cloud native applications are designed to be agile, flexible, and adaptable to cloud infrastructures. They leverage microservices, containers, and orchestration tools to achieve scalability and resilience, making them well-suited for dynamic cloud environments.

However, this flexibility comes with its own set of challenges, especially in terms of security. A study revealed that a staggering 90% of teams using containers and Kubernetes experienced security incidents in their environments, highlighting the urgent need for robust threat detection and response strategies tailored to cloud native ecosystems.

The Evolution of Threat Detection

Traditional threat detection methods, such as signature-based approaches, have proven inadequate in cloud native environments. Signature-based methods rely on predefined rules to detect known threats, but they struggle to keep pace with the rapid onslaught of new threat actors and require thousands of signatures to every known threat. This leads to high false positive rates and an inability to catch sophisticated attacks that exploit legitimate processes or user permissions.

Similarly, black box anomaly detection, while promising at the outset, lacks transparency and struggles with a lack of input into cloud native attacks. Millions of such attacks would be needed to create a truly accurate detection model with this approach. These limitations underscore the necessity for a paradigm shift in threat detection methodologies tailored specifically for cloud native environments.

Introducing Behavioral Threat Detection

One of the key pillars of behavioral threat detection is the concept of workload fingerprints that capture the hierarchy of processes, programs, and files of a running workload. Workload fingerprints serve as a baseline for normal behavior within an environment, allowing organizations to detect any deviations or drifts from this baseline. In this approach, the more appropriate usage of AI is not in the detection itself, but in the classification of what has been detected, if it is part of a known attack.

Operationalizing Behavioral Threat Detection

Implementing behavioral threat detection involves several crucial elements:

1. Baseline Creation: Establishing a baseline of normal behavior through workload fingerprints, capturing the expected behavior of containerized workloads.
2. Detecting Anomalies via Drift: Continuously monitoring and analyzing workload behavior for deviations from the established baseline, leveraging AI-driven analysis to identify potential threats.
3. Apply Detection to the Software Supply Chain: Verifying the integrity of software throughout the SDLC by comparing baselined behavior with current behavior, akin to an SBOM for runtime behavior.
4. Real-time Posture and Context: Applying real-time context across identity, infrastructure, and workloads to attackers’ behavior

Embracing Innovation in Cloud Native Security

The evolution of threat detection and response in cloud native environments demands innovative approaches that can adapt to the dynamic nature of modern applications. Behavioral threat detection, with its focus on understanding patterns of behavior, offers a promising avenue for enhancing security posture and staying ahead of emerging threats. By leveraging workload fingerprinting technology, organizations can take a proactive approach to detection, so that when the next zero day in their cloud environment comes around, they have access to an ultimate source of truth.

About the Author

Jimmy Mesta is the Founder and Chief Technology Officer at RAD Security. He is responsible for the technological vision for the RAD Security platform. A veteran security engineering leader focused on building cloud-native security solutions, Jimmy has held various leadership positions with enterprises navigating the growth of cloud services and containerization. Previously, Jimmy was an independent consultant focused on building large-scale cloud security programs, delivering technical security training, producing research and securing some of the largest containerized environments in the world.

You can connect with Jimmy on Linkedin (https://www.linkedin.com/in/jimmymesta/) or by visiting https://rad.security/