Disaster Recovery Journal Spring 2026

Once this AI-powered software can do more than create viable DR plans based upon collected data. The enterprise data pro tection software can transform and serve to automate DR throughout the enterprise. By continually analyzing an enter prise’s IT environment, it can dynamically adapt its configu rations to create a sustainable DR solution. Enterprise DR Today: Costly, Restrictive, and Labor Intensive Smaller organizations (typi cally 500 people or less) rep resent the lucky ones today – at least when it comes to DR. They can obtain viable DR solutions that can perform recoveries for many workloads on premises, in the cloud, or both. Their IT environments generally remain more static and simpler, permitting them to keep their DR solution run books reasonably current. Enterprises face much greater challenges in keep ing their DR runbooks and IT infrastructure up to date and viable over time. They often have applications that run in both physical and virtual environments. These applica tions may then use different underlying operating systems, hypervisors, and potentially containers. They may also host applications on premises, in the cloud, or in hybrid environ ments. These complexities only multiply when considering their underlying networking, server, and storage hardware gathered,

n Operational insights .

and their configurations. The known and unknown depen dences that exist between applications further contribute to creating a complex, difficult to maintain DR environment. Documentation, runbooks, and regular DR exercises do address some challenges for some applications and work loads. However, few if any enterprises can maintain DR plans for all their applications and workloads, always put ting some at risk. This does not account for scenarios where only some applications and workloads fail, and others remain online. Even if an enterprise by some herculean effort manages to successfully implement a viable DR solution, its sustain ability remains questionable. Enterprise IT environments and business conditions change daily, if not by the hour or minute. Failing to capture and account for all these changes calls into question the viability of any DR solution. These ever-changing com

plexities and business require ments would seem to make the possibility of creating a sus tainable, enterprise-ready DR solution unlikely. However, the introduction of AI into enterprise data protection has already begun to change this viewpoint. AI in Enterprise Data Protection Software Today Enterprises that use an enterprise data protection solution may already see evi dence of AI’s presence in it. Enterprise data protection solutions may leverage AI in one or more of the following ways to analyze backup stores: n New business insights . Large enterprises may have years, if not decades, of backups. By analyzing this data, enterprises may gain new business insights into how they can improve or market their product. However, this represents an iffy use case for AI for most enterprises since it brings no guarantee of useful results.

Enterprise data protection software may use AI to analyze backups and backup metadata to improve technical operations. This process results in it identifying if backups have failed, become corrupted, or cannot be successfully restored. It can then alert the enterprise to these events. The data protection software may even act on these alerts, including rerunning backups or fixing the data corruption. This represents a growing use case for AI. data protection software has had to introduce anomaly and ransomware detection features to meet enterprise demands. Enterprises now use data protection software to help maintain the security of their IT environment. This functionality represents a capability every enterprise data protection software offers in some capacity.

n Detect anomalies and ransomware . Enterprise

DISASTER RECOVERY JOURNAL | SPRING 2026 31