The Future of Cloud Security: Behavioral Detection and Reducing Noise

When it comes to cloud security, one of the most common concerns we hear is about the potential for too much "noise" when using behavioral approaches for detection and response. 

This is a valid concern, given the constantly changing nature of cloud environments. However, the answer to whether a behavioral approach creates noise isn't straightforward—it's both yes and no.

What is Noise in Cloud Security?

“Noise” refers to irrelevant or extraneous data that can obscure or interfere with the accurate detection of security threats. Noise can come from various sources within a cloud environment, such as frequent updates and changes in configurations and deployments, high volume of data, false positives, and environments with interconnected sources. 

The Role of Signatures in Cloud Security

Before diving into the nuances of noise as it relates to behavioral detection, let's first understand the concept of signatures in security. In its simplest form, a signature is a predefined rule or pattern that looks for specific, known threats in the system. The rule is designed to detect and respond when a particular event or pattern is identified.

Future of Cloud Security: What Signatureless Threat Detection Looks Like

See how our signature-less method of threat detection reduces false positives and enhances threat detection, even in noisy settings.

The challenge with signature-based detection is its reliance on prior knowledge. The security rule needs to be highly specific and based on known threats, making it vulnerable to being bypassed by new or slightly altered threats. For instance, even well-known tools like Falco, an open-source runtime security tool, can miss threats if the rules aren’t explicitly tailored to detect subtle variations.

Why Signatures Can Fall Short in Cloud Security

Let’s take a practical example. A rule in Falco might be designed to catch a particular security vulnerability in a Linux environment using the sudo command. However, due to the nature of Linux and how sudo might be implemented, it’s possible for this rule to be bypassed, leaving the system vulnerable despite having a signature in place.

This limitation becomes even more evident in complex attacks, such as the infamous SolarWinds software supply chain attack or the PyTorch dependency confusion attack. These incidents highlight how signature-based detection can be insufficient when dealing with novel, sophisticated threats that signatures simply aren’t prepared to identify.

Moving Beyond Signatures: Behavioral Detection

Given these challenges with cloud security, the security community has increasingly turned to behavioral approaches for detection and response. Behavioral detection involves monitoring the typical operations of a system and flagging any deviations from the norm as potentially suspicious. This approach doesn't rely on predefined signatures but rather on understanding what "normal" behavior looks like in a given environment.

One method of behavioral detection is runtime verification, where the behavior of processes, programs, and files is continuously monitored. By establishing a baseline of what "good" behavior looks like, any deviations can be flagged for further investigation.

Is Behavioral Detection Too Noisy?

A key concern with behavioral detection is the potential for excessive noise— those false positives that occur when normal variations in the system are mistakenly flagged as threats. This concern is valid, especially in dynamic cloud environments where constant updates and changes are the norm.

However, our experience has shown that while cloud environments can indeed be noisy, behavioral detection can be effectively managed to minimize false positives. For instance, when comparing different versions of software, such as Redis or PySpark, the behavioral "fingerprints" often remain stable despite underlying changes. This stability allows for effective monitoring without overwhelming noise.

Conclusion: The Future of Cloud-Based Threat Detection is Behavior-Based 

Behavioral detection in cloud security offers significant advantages over purely signature-based approaches. While cloud environments can introduce noise, careful implementation and continuous refinement of behavioral baselines can lead to reliable and accurate threat detection.

At RAD Security, we’ve developed a comprehensive catalog of cloud-native workload fingerprints that can help you better understand and implement behavioral detection in your environment. You can explore our open-source catalog at catalog.rad.security to see these fingerprints in action.

Ultimately, while the noise in cloud environments is a reality, it shouldn't deter us from pursuing advanced behavioral detection techniques. By balancing both signature and behavioral approaches, we can achieve a more robust and resilient security posture in the cloud.