Unify AI Resilience Securely
Metallic AI is Commvault Cloud's built-in AI layer — using machine learning to detect malware inside backup data, predict threats, optimize job scheduling, and surface risk signals across hybrid environments. All AI models run locally within each customer's environment and do not interact with backup data, delivering AI-enabled resilience aligned to NIST AI Risk Management Framework 1.0.
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100,000+ customers worldwide
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14x Leader in Gartner Magic Quadrant
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SOC 2, ISO, FedRAMP Certified
Key Capabilities
Built into Commvault Cloud, Metallic AI uses ML and intelligent automation to improve protection operations, reduce risk, and accelerate clean recovery.
Discover and classify sensitive data
AI-enabled contextual learning identifies and classifies sensitive files across hybrid infrastructure, adds risk scoring, and supports quarantine or deletion for compliance.
Detect malware inside backups
Threat Scan uses signature-based scanning, AI algorithms, and file comparisons to detect encrypted or altered content and pinpoint compromised backup files.
Predict threats with insights
Threat Scan Predict uses Avira threat insights to deliver ML threat detection within backups to help detect zero-day and polymorphic malware already present.
Optimize job schedules intelligently
Smart Job Scheduling uses time-series ML to predict runtimes, optimize sequencing, and prioritize workloads by RPO—reducing protection windows without manual intervention.
Baseline behavior, flag anomalies
Anomaly Detection uses ML to establish baseline behavior per machine and identify abnormal changes, issuing automated alerts to help administrators respond faster.
Forecast capacity, auto-scale resources
ML supports predictive forecasting for storage needs and auto-scaling/load balancing to adjust compute resources up or down to meet defined SLAs.
Faster time to value
Activate trusted AI features
AI capabilities are built into Commvault Cloud—configure policies, enable detection, and validate recovery workflows to strengthen resilience without adding separate tools.
Connect and configure access
Connect protected workloads and set credentials, encryption, and role-based access. Confirm AI models remain local to your environment and align with policy.
Enable ML protections
Turn on Threat Scan and Anomaly Detection, then establish baseline behavior and alerting so administrators can respond quickly to abnormal change.
Validate and operationalize
Test restores and clean recovery workflows, review Active Insights recommendations, and monitor forecasts for capacity and auto-scaling to maintain SLAs.
Frequently Asked Questions
What is Metallic AI?
Metallic AI is Commvault’s intelligent control layer in Commvault Cloud. It uses AI-enabled and ML-assisted automation to improve operational efficiency, strengthen cyber resilience, and help teams identify risk signals and accelerate clean recovery across hybrid environments.
Do you train on backups?
No. Commvault Cloud AI features use AI models local to each customer’s environment. Those models do not interact with customer backup data, and Commvault does not use your backup data to train models.
Can AI features be disabled?
Some can. Certain background-running ML used to flag anomalies cannot be disabled. Generative AI is used for Arlie, which is optional for installed software and can be activated or deactivated based on customer preference.
What does Threat Scan detect?
Threat Scan detects threats within backups using a built-in signature-based malware engine, ML, and file comparisons. It pinpoints compromised content, including files that have been encrypted or significantly altered. Threat Scan Predict adds ML detections using Avira threat insights.
Is Arlie optional?
Arlie is an AI assistant in the Commvault Cloud console that helps users learn the product and troubleshoot errors. In SaaS, it’s enabled by default and doesn’t collect data in the background when not used; only Arlie Chatbot is available.
How does ML improve operations?
ML improves day-to-day operations through Smart Job Scheduling that predicts runtimes and optimizes sequencing by RPO. It also supports anomaly detection baselining, auto-scaling and load balancing to meet SLAs, predictive forecasting for storage capacity, and semantic search in the console.
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Put trusted AI to work
AI-enabled resilience with NIST AI RMF guidance, local models, and optional AI assistant controls—built for secure recovery.
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Guided by NIST AI Risk Management Framework (RMF) 1.0 best practices
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AI models are local to each customer’s environment
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AI assistant is optional for installed software; SaaS doesn’t collect data in the background when not used
