Five ways AI is revolutionising the data backup industry

By Ian Wood, Senior SE Director at Commvault.

  • 1 month ago Posted in

Data is the lifeblood of nearly every business today, so in the event of an unforeseen emergency, such as a data centre going down or a cyber attack striking, the last thing executives want to be worrying about is whether or not their data backups are up-to-date and easily recoverable. 

Fortunately, advances in technology are revolutionising what’s possible when it comes to modern data backups. In particular, artificial intelligence (AI) and machine learning (ML) have quickly emerged as two pivotal new technologies, helping businesses of all sizes to implement faster, more efficient backup processes, better evaluate their backup histories, and even spot data security events before they occur. Should the worst happen, AI and ML can also be used to get businesses back up and running as quickly as possible, minimising potential lost revenues and reputational damage that can result from prolonged periods spent trying to recover key data.

Simply put, AI and ML are far more than just novel innovations for employees to play around with; they are vital assets that can keep data safe and ensure businesses are prepared for any eventuality. Below are five ways that IT teams are already benefitting from these technologies in relation to their data backups:

1) Rapid detection of cyber attacks

Data backup goes hand in hand with cybersecurity. Cybercriminals know that effective data backup can greatly diminish the impact of an attack, which is why they will often try to compromise both productive data and backup files simultaneously. However, AI can increasingly be used to identify data anomalies created by attackers and correctly flag them as indicators of an attack in progress, alerting businesses much earlier than would otherwise be possible through manual analysis. 

2) Effective recovery of both data and infrastructure

IT teams can use AI and ML to define optimal recovery time objectives (RTOs) and recovery point objectives (RPOs) with minimal information loss and rapid re-availability, as well as receive alerts when predefined SLAs on data availability may no longer be met. The benefits don’t end there, either. AI can also help define the necessary recovery steps in advance of a disaster. A clean, malware-free recovery in a cloud cleanroom benefits from AI- and ML-powered definition of the last clean backup in a dataset. 

3) Optimisation of scheduled backup tasks

Traditional backup plans typically rely on static rules and schedules, which can lead to complicated configurations and inefficient processes. However, by using time series-based ML to predict job run times, AI- and ML-powered data management platforms constantly improve the job calendar through optimal sequencing. Cyber-resilient data protection platforms calculate best possible RPOs for cyber-resilient data protection and prioritise recovery workloads based on availability targets. 

At the same time, AI can be used to minimise the time windows necessary for data backup. What’s more, all this can be done autonomously, meaning IT team members don’t have to spend time manually intervening at any point.

4) Streamlined data monitoring

AI can be used to continuously collect and analyse performance data from thousands of daily tasks and backup operations, which would be virtually impossible to do manually. If any anomalies are detected, they can be classified according to severity, type, and frequency in real-time, making it much easier for the IT team to identify which ones need immediate attention and which are less urgent. Without this capability, many critical errors go undetected for much longer periods of time, increasing the risk of backups failing at inopportune moments. 

5) Data prioritisation in the event of a data loss incident

Finally, AI and ML can help IT teams quickly determine which data should be restored first in the event of an attack or data loss incident taking place. Teams can train AI tools to identify the document types that are particularly critical to business operations, based on key factors such as access and sharing frequency. Should an incident occur, the tools will then know to prioritise these documents as part of any restoration processes taking place, helping to minimise disruption caused. 

A new benchmark for data backup capabilities

The emergence of AI and ML has rapidly reshaped the data backup industry, giving businesses potent new tools to protect their sensitive data in an increasingly hostile digital landscape. These technologies have set a new benchmark when it comes to backup capabilities, from spotting cyber attacks in progress and facilitating effective data recovery, to streamlining monitoring processes and optimising backup tasks. 

While innovations like generative AI tend to grab the media headlines, AI as a technology stands to revolutionise almost every aspect of business operations over the next few years. Data backups are just one part of this, but in the event of a cyber attack or data centre outage, they can quickly become critical to business continuity, or even survival in some cases. Consequently, it’s well worth exploring the benefits of AI-powered data backup before it’s too late.  

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