8 ways to use AI for data backup

1. Resource planning and optimization

AI aids with resource planning and optimization by examining an organization's current backup infrastructure and comparing it with existing backup and recovery options from application vendors and cloud service providers.

AI can advise administrators on cloud storage, on-premises storage and network optimization to ensure backup plans are comprehensive and efficient. It also helps with backup scheduling to avoid periods of high network and disk I/O, keeping the backups from affecting other services.

2. Risk assessment

Many backup and disaster recovery teams already use AI for various aspects of risk assessment, including network misconfigurations and operating system or application vulnerabilities. By turning AI's attention to the organization's backup strategy, administrators can quickly determine weaknesses and data sets left uncovered by the current backup design. The IT team can then address the risks directly or accept input from the AI tool on how best to correct the issues.

3. Storage optimization

AI tools can optimize storage by examining the available data types and storage options to determine the best combination of efficiency and cost-effectiveness. This optimization includes selecting hot, warm and cold storage options on-premises or in the cloud. It also identifies resources requiring multiple copies for immediate and nearby recovery. Storage optimization via AI balances storage costs and satisfactory recovery options, especially when the organization uses overlapping designs based on the 3-2-1 rule or its variations.

4. Monitoring for suspicious activity

AI can be an excellent monitoring tool, enabling administrators to keep an eye on services without overloading them. AI can monitor backups for suspicious activities, warning administrators of these anomalies or acting independently to address problems. AI's ability to learn and recognize patterns enables it to understand the organization's unique backup infrastructure and needs.

5. Predictive analysis for data loss prevention

Analysis of historical data helps predict data loss, enabling users to address risks before they become incidents. This proactive approach, which covers misconfiguration, hardware failures, software vulnerabilities and other concerns surrounding data loss, can give IT teams greater confidence in the backup strategy. They no longer have to guess at mean time before failure and mean time to recovery data -- with predictive analytics, AI handles that and provides mitigation opportunities.

6. Backup monitoring

Backup monitoring can generate vast amounts of information. By placing AI between the monitoring utility and the recovery team, backup administrators can benefit from its real-time analysis. The analysis provides classification to help IT teams prioritize and address issues quickly and accurately, relieving them from a potentially overwhelming monitoring and analysis burden.

7. Data prioritization and classification

Not all data needs to be backed up the same way. Some resources rarely change, while other data types update continuously. System configuration files are a great example of files that don't usually change frequently, while customer and purchase databases update constantly. AI classifies data to identify the most critical files. Classification can be based on data type, but it can also consider access frequency, location and lifespan to determine the best backup choices.

8. Compliance monitoring and reporting

Regulatory compliance is a key issue for today's IT teams. AI can provide compliance monitoring and reporting to ensure an organization satisfies these requirements. It can identify areas where organizations are out of compliance so that they can address issues before being penalized. This compliance is particularly critical with data sovereignty laws and privacy concerns.

Is AI a practical choice for backup?

AI is a helpful tool for many tasks, but it's not perfect for everything. Using AI to optimize backup processes and protect business assets more effectively is valuable when done correctly.

AI tools are quickly becoming easier to use and more specialized, meaning employees can get more use from them.

However, organizations must be cautious of data privacy concerns and ethics around AI use. Businesses must carefully manage personal identification information and intellectual property that backup jobs might contain. The industry and public continue to learn more about AI's effects on privacy, so expect this area to continue to develop, and keep an eye on any emerging concerns.

Another area of AI-assisted backup management that users must carefully control includes data residency and data sovereignty. This is particularly essential when combining AI backup management with cloud-based storage. Administrators must ensure the AI does not decide to store backup jobs outside data sovereignty boundaries.

As with any technology, AI users must regularly monitor and review a tool's configuration and results. An AI tool and the backup infrastructure it interacts with need to provide useful information quickly and easily to determine whether backup jobs succeed and where backups are stored. This information helps organizations decide the best way to recover data after an incident.

Many backup utilities exist, and many AI choices are available. Combining these two crucial tools enables organizations to protect their data better and get the most out of data storage investments.

Damon Garn owns Cogspinner Coaction and provides freelance IT writing and editing services. He has written multiple CompTIA study guides, including the Linux+, Cloud Essentials+ and Server+ guides, and contributes extensively to TechTarget Editorial and CompTIA Blogs.