Disaster Recovery Journal Spring 2026

EDITOR’S NOTE : DCIG empowers the IT industry with actionable analysis that equips individuals within organizations to do supplier and product evaluations. DCIG delivers informed, insightful, third-party analysis, and commentary on IT technology. As industry experts, DCIG provides comprehensive, in-depth analysis, and recommendations of various enterprise data storage and data protection technologies. The views, thoughts, and opinions expressed in all Disaster Recovery Journal articles belong solely to the author. The information, product recommendations, and opinions in this article are based upon public information and from sources DCIG, LLC. believes to be accurate and reliable.

C reating and imple menting a viable, effective, enter prise-wide and sustainable disas ter recovery (DR) solution often exceeds the capa bilities of many enterprises. The complexities, costs, and resources of implementing an enterprise-wide DR solu tion already gives them pause. Once they factor in the man agement overhead and costs to maintain it, they either stop planning or limit its scope. The introduction and growth of artificial intelligence (AI) within enterprise data protection software suggests this will soon change. This software already uses AI to analyze backup data stores to gain business insights and per form anomaly and ransomware detection. The next generation of this software’s use of AI prom ises to do much more. It will start by capturing data from multiple sources. This data may include application and user activity, data about the IT infrastructure, and organi zational policies and business rules.

Foundation for Automated Enterprise DR Using AI By JEROME WENDT

30 DISASTER RECOVERY JOURNAL | SPRING 2026