Ransomware Detection

Use AI to detect and respond to ransomware threats in real time across IT infrastructure

Business impact

• Threat detection rate — Increases by identifying ransomware attacks earlier and more accurately
• Incident response time — Decreases due to real-time AI-driven alerts and automated responses
• False positive rate — Reduces by leveraging AI to distinguish legitimate from malicious activity
• Operational resilience — Improves by minimizing downtime and data loss from ransomware incidents
• Customer satisfaction — Increases as data security and service reliability are enhanced

Data requirements

• Storage system telemetry (Structured) — Monitors file access and modification patterns to detect anomalies
• CPU and memory telemetry (Numeric) — Analyzes runtime behavior for suspicious activity indicative of ransomware
• Network traffic logs (Structured) — Detects unusual data exfiltration or command-and-control communications
• File metadata and content hashes (Text) — Identifies known ransomware signatures and suspicious file changes
• Threat intelligence feeds (Text) — Provides updated ransomware signatures and behavioral indicators

AI methods and techniques

• Predictive AI — Predicts ransomware attacks by analyzing behavioral patterns and anomalies
• Agentic AI — Automates response actions such as quarantining or alerting security teams
• Symbolic AI — Applies expert rules to complement machine learning for precise detection

AI models and model families

GPT-4, Claude, Custom ML models for anomaly detection, Variational Quantum Classifier (experimental)

Industries

Real-world evidence

2 documented case studies on record.

Companies using this: Dell, Trend Micro.