Machine learning and predictive analytics improve backup reliability and efficiency by analysing logs, predicting failures, and selecting optimal backup times and storage targets. Generative AI assists customers with setup and management of backup tasks, while agentic AI promises deeper automation for configuration and recovery testing. AI is already used for disaster recovery planning, resource allocation, inventory updates, and mapping data usage across networks and applications. Backup tools typically apply AI to setup and configuration, process optimisation including tiering, and anomaly detection. Rising ransomware threats have increased emphasis on detecting and securing backups against compromise.
Software supplier use of AI and machine learning (ML) in this space is not new. Automation has been a feature of backup software for years. Backup providers use machine learning and predictive analytics, in particular, to make backups more reliable and efficient. This includes use of ML to analyse logs to predict where a backup might fail, and to pick the best times and storage targets for backups.
Suppliers also use generative AI (GenAI) to help customers set up and manage backup tasks. Meanwhile, agentic AI systems hold the promise of further automation, in areas such as system configuration and recovery testing. But the most common application for AI is in ransomware detection and remediation. IT teams already use AI for tasks that include disaster recovery (DR) planning and resource allocation.
When it comes to backup tools, most use AI in three ways. These include helping users with setup and configuration, optimising the backup process - including storage optimisation and tiering - and anomaly detection. Suppliers and chief information officers see anomaly detection as an increasingly important feature of any backup tool. The growth in ransomware and ransomware gangs' increasing proficiency at targeting backup has forced IT teams to look again at how they secure backup volumes.
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