Machine learning watching your backups: anomalous encryption, mass deletions and suspicious patterns surface in hours — with the blast radius already computed.
Modern ransomware works in silence: it encrypts at low intensity, deletes the copies, then strikes. Anomaly Detection analyzes every backup with ML models trained on entropy, change rates and deletion patterns: when something is off, the alert arrives with the exact list of files and systems involved — the blast radius — and the last clean recovery point already identified.
The model learns the normal rhythm of YOUR data: fewer false positives, more signal.
Alerts to SIEM/SOAR and our NOC: the anomaly enters the process, not a lost inbox.
Which VMs, which shares, which files: the damage count ready for incident response.
The last healthy snapshot pointed out by the platform: recovery starts without debates.
At every snapshot the platform computes per-file and per-system features: content entropy (encrypted data is statistically distinguishable), create/modify/delete ratios, new extensions and mass renames; models compare against each workload's historical baseline and flag deviations; analysis is retroactive across the catalog: you see WHEN the anomaly began, the exact perimeter and the recovery point immediately before it.
Low-intensity encryption discovered in hours.
Mass deletions: seen, traced, reversible.
Blast radius and clean point: the count ready.