Compromised Snapshot Recovery via Baseline and Intermediate File Selection
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Solution Overview
Problem
Traditional methods for recovering from ransomware and malware attacks are inefficient, time-consuming, and prone to errors, often resulting in substantial data loss and operational disruptions due to reliance on manual processes and the restoration of entire compromised snapshots.
Innovation Solution
An AI-driven data platform that analyzes file metadata, content, and behavior patterns to identify clean files within compromised snapshots, automating the recovery process by selecting clean files from multiple snapshots, particularly those taken later in time, and restoring them in a secure, isolated environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual recovery methods are used, then specialized expertise can identify clean files, but the process is time-consuming and error-prone
Solution Approach 1:
The system performs self-diagnosis and self-recovery by automatically analyzing snapshots, identifying compromised files through AI/ML models, and restoring clean versions without requiring specialized human expertise. The automated platform executes the entire recovery process independently, from detection to restoration.
Solution Approach 2:
Manual expert analysis is replaced with AI-driven automated systems including machine learning models and natural language processing. The system uses computational algorithms to analyze file metadata, content, and behavior patterns, substituting human expertise with intelligent automation that operates faster and without fatigue.
2Reliability
If entire compromised snapshots are restored, then complete system recovery is achieved, but compromised data is also reintroduced
Solution Approach 1:
The recovery process segments the snapshot into individual files and analyzes each one separately. Instead of treating the snapshot as a monolithic unit, the system divides it into discrete components, identifies which specific files are compromised, and selectively restores only the clean files while excluding infected ones.
Solution Approach 2:
The system extracts and removes compromised files from the snapshot by identifying them through AI analysis and excluding them from the restoration process. Clean files are extracted and restored while compromised files are left behind, effectively separating the useful data from the infected data.
3Measurement precision
If multiple snapshots are analyzed to find clean files, then recovery accuracy improves, but the complexity of the recovery process increases
Solution Approach 1:
The AI-driven platform performs multiple functions within a single unified system: it analyzes snapshots, detects compromised files using machine learning models, identifies clean files across multiple time points, and executes restoration. This multi-functional approach consolidates what would otherwise require separate tools and processes into one universal recovery platform.
Solution Approach 2:
The system uses feedback loops where AI models analyze snapshot data, identify patterns of compromise, and continuously improve their detection accuracy. The platform learns from each analysis, refining its ability to distinguish clean from compromised files across multiple snapshots, with results feeding back into improved future detections.
4Productivity
If automated AI-driven recovery is implemented, then recovery time is reduced, but the need for specialized security platforms decreases
Solution Approach 1:
The system merges data protection, security analysis, and recovery functions into a single integrated platform. By combining snapshot management with AI-driven threat detection and automated restoration capabilities, the system eliminates the need for separate specialized security tools and manual intervention, achieving both speed and self-sufficiency.
Data Source
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AI summary
Techniques are described for recovery of compromised snapshots. An example method comprises identifying, by a data platform implemented by a computing system, a baseline snapshot from a plurality of snapshots of protected data, wherein the baseline snapshot comprises one or more files that each exhibits no indication of compromise; for each file in an anomalous snapshot of the plurality of snapshots, identifying, by the data platform, from one or more intermediate snapshots between the anomalous snapshot and the baseline snapshot in the plurality of snapshots, a clean version of the file; and storing, by the data platform, a clean snapshot comprising the respective clean versions of the respective files identified for the files in the anomalous snapshot.