Rubrik Anomaly Detection Adds Behavioral Analysis to Dynamically Identify Ransomware Extensions

Ransomware detection is changing—fast.

Over the last few years, there has been a notable shift in observed ransomware extensions. These used to follow predictable naming conventions, but instead, have been mutating constantly. In several recent incidents across the industry, attackers generated random, never-before-seen file extensions to avoid signature-based tools entirely. Sometimes, these extensions are unique to each victim, ensuring that indicators identified in one attack cannot be used to protect other organizations.

For security and backup teams trying to protect large, fast-moving environments, this shift has created a new problem: how do you reliably identify malicious encryption when the indicators keep changing?

At Rubrik, our goal is to give customers confidence in the middle of uncertainty. That requires detecting malicious encryption reliably, even when attackers alter their patterns. One of the most visible challenges is the way ransomware modifies or appends extensions during an attack.

This is what led us to rethink how Rubrik evaluates ransomware extensions — not by what they are, but by how they behave.

Why a behavior-first approach matters

A static list of suspicious file extensions can catch obvious cases. But this approach tends to fall short in real environments:

• Enterprise tools often use extensions that look unusual or rare, leading to false positives.
  
  

• Attackers constantly create new extensions to bypass signature-based tools and allow lists.
  
  

• Modern ransomware encrypts in rapid bursts—a behavioral pattern, not a string match.
  
  

Instead of asking, "Is this extension on a list of known bad actors?" our new engine asks: "Is this extension acting like ransomware in this specific environment?"

How It Works: The Signals Behind the Analysis

To achieve this, the detection engine evaluates several signals simultaneously to build a high-fidelity picture of the event:

• Appended extensions: If an existing file suddenly gets a new extension (for example, report.pdf → report.pdf.xyz123), the system captures and analyzes the pattern.
  
  

• Burst activity: Ransomware modifies or creates many files in a short time. We analyze these sharp spikes in activity as a primary behavioral clue.
  
  

• Intelligent Baselining: We evaluate the rarity of an extension by analyzing its history within your environment alongside its prevalence in the broader threat landscape. This creates a localized baseline that understands your normal, reducing false positives from internal tools.
  
  

• Ensemble scoring: We synthesize the above distinct signals into a single confidence score, filtering out benign anomalies (like a nightly patch update) to ensure high-fidelity alerts.
  
  

This is focused behavioral analysis tuned specifically for data encryption patterns.

A Practical Example: Catching the "Unknown"

Imagine an attacker launches a new ransomware variant that appends a never-before-seen extension like .xyz123 to every encrypted file.

A static list won’t help here—no one has documented .xyz123 yet. However, Rubrik’s Behavioral Anomaly Analysis doesn't rely on prior knowledge. Instead, it observes:

• Existing filenames being changed to include a new extension
  
  

• A sudden surge in files ending with that extension
  
  

• Zero history of that extension in this environment
  
  

• Low prevalence in the broader threat landscape
  
  

• A pattern that looks identical to how ransomware behaves
  
  

Based on that combined behavior, the system raises an alert early, long before that extension ever becomes “known.”

This is exactly the type of scenario where dynamic analysis shines.

Fewer False Alarms, Higher Confidence 

Static approaches often generate a lot of noise because environments are full of legitimate tools that use odd extensions. When those tools run as part of normal operations, the system shouldn’t treat them as ransomware.

Rubrik Anomaly Detection, with this new enhancement, suppresses this noise in two ways:

1. It learns what is normal in each environment: If a workflow routinely creates .custompkg files every night, the model recognizes this as safe and does not raise a flag.
   
   

2. It spots genuinely unusual behavior: If something out of the ordinary happens, it clearly stands out. For example if .xyz123 is new to the environment and is being associated with mass file additions, and those files are having high entropy, this behavior could be associated with ransomware patterns.

This means the SecOps teams get fewer false positives and more actionable signals.

Here’s a quick overview of how dynamic file extension analysis measures up against the legacy method of comparing with a list of known malicious file extensions: 

Why this matters for CISOs and SecOps

This shift in approach from static lists to dynamic behavioral analysis provides key advantages for security leaders and operational teams tackling today's advanced threats, including:

• Reduced alert fatigue: Fewer, higher-fidelity alerts that focus teams on real encryption activity.
  
  

• Threat visibility as tactics evolve: Behavior-driven signals surface true events even when extensions are brand new or customized.
  
  

• Recovery with confidence: Quickly identify which snapshots and data were impacted to guide clean recovery and limit blast radius.
  
  

• Adaptive, resilient detection: Move beyond signatures and static lists to an approach that adapts to your environment and attacker changes.
   

This moves Rubrik Anomaly Detection beyond pattern matching towards a more adaptive approach that matches how ransomware actually behaves today.

Indeed, attackers constantly change how they name encrypted files. So we changed how we look at those names. Rubrik gives teams a more reliable, more resilient way to spot ransomware activity and recover with confidence by focusing on the behavior of extensions rather than the strings themselves.