CyberSense is built to help organizations recover from ransomware quickly and confidently by validating the integrity of backups, snapshots, and critical data before restore. With every release, we update and validate our AI-powered corruption detection to stay aligned with how ransomware is changing in the real world.
The latest CyberSense release includes updates and validation of our machine learning models based on ransomware variant trends observed in the CyberSense Research Lab.
Our model updates are driven by the CyberSense Research Lab’s patented research and model-validation process (U.S. Patent #12248574). The lab simulates real-world ransomware attacks through live detonations, generates millions of datasets to test different attack scenarios, and validates our AI/ML models against those datasets. Through this automated process, 3,500+ real-world ransomware samples are ingested and analyzed daily to retrain our models and ensure reliability, helping CyberSense maintain 99.99% accuracy as attacker techniques evolve.
This continuous improvement is core to how CyberSense delivers trusted ransomware corruption detection and trusted recovery guidance, especially as attackers increasingly use techniques designed to evade surface-level detection.
In Q4 of 2025, the CyberSense Research Lab observed several notable ransomware trends.
1.) Increased use of shadow encryption techniques (intermittent, partial, and slow encryption)
The lab observed an increased use of shadow encryption techniques, including intermittent, partial, and slow encryption. Approximately 80% of ransomware variants analyzed in the last quarter exhibited these behaviors, reflecting one of the dominant techniques in today’s ransomware landscape.
Why it matters: These approaches are designed to reduce obvious signals (like sudden, large spikes in activity or entropy) and extend the time it takes teams to confidently identify when corruption began or how far it spread, which can drastically extend recovery timelines.
2.) Greater prevalence of polymorphic ransomware behaviors
The lab also saw a greater prevalence of polymorphic ransomware behaviors, including variants that replace legitimate files with executable content. Approximately 90% of ransomware samples analyzed in the lab were polymorphic.
Why it matters: Polymorphism can complicate identification, investigation and recovery, especially if relying on signature-based tools. It also increases the risk of reinfection if teams restore from data that appears normal but contains attacker-modified content.
3.) A subtle rise in wiper-style ransomware
The lab noted a subtle rise in wiper-style ransomware, where the primary goal is destructive corruption rather than extortion. CyberSense customers remain supported through existing raw disk corruption detection capabilities (available in CyberSense 8.10+).
Why it matters: While less common than traditional ransomware, wiper behavior can remove recovery options by irreversibly destroying data.
4.) Emerging variants that encrypt or corrupt directory structures
The lab also noted emerging variants that encrypt or corrupt directory structures rather than individual files.
Why it matters: This behavior is intended to maximize disruption and complicate investigation by impacting large, logically grouped sets of data at once. CyberSense’s content-based analysis remains effective against these techniques because detection is based on deep inspection of file and data content rather than surface-level indicators such as metadata, file activity, or directory structures.
Across these trends, the common thread is clear: attackers are optimizing for maximum disruption with minimum obvious signal.
This has two practical impacts for recovery teams:
Even after an attack is detected and the investigation begins, these subtle encryption techniques may already have compromised backup or snapshot data. In many environments, tools that rely primarily on surface-level indicators (e.g., metadata changes, file activity spikes, or high-level entropy heuristics) can struggle to detect these techniques consistently, especially when attackers are intentionally minimizing obvious signals. The result is a slower, less certain recovery process: teams either spend more time validating restore points or risk restoring compromised data.
CyberSense takes a different approach. It analyzes data at the content level, inspects changes at the byte level, and compares versions over time—snapshot to snapshot or backup to backup—to identify when corruption began and which recovery points remain safe to restore. This evidence-based analysis reduces guesswork and helps teams make recovery decisions with 99.99% confidence.
As with every release, these model updates help ensure CyberSense continues to deliver accurate detection of ransomware-induced corruption and trusted recovery guidance as ransomware tactics evolve. As a best practice, customers are encouraged to stay current with CyberSense software updates whenever possible to maintain 99.99% confidence and ensure continued alignment with the evolving threat landscape.
For a deeper dive on how CyberSense’s AI engine validates data integrity across storage and backup environments with 99.99% acccuracy, reach out to your Index Engines representative or contact us for a walkthrough of the latest release and model enhancements.
To learn more about the CyberSense Research Lab and the patented process that trains our AI engine, explore the following resources:
