Disaster Recovery Journal Spring 2026

agement does, however, represent a priority for data protection provid ers. Multiple providers have acquired software in recent months to augment and build up their software capabilities in this area. By protecting iden tity management platforms, it positions data protection soft ware to take on even broader responsibilities going forward. An Interface Customized to Each User Enterprise data protec tion software’s use of AI to improve operations and detect anomalies represents its initial foray into AI. However, pro viders have more ambitious goals for their respective data protection software and its use of AI. They want to see it take on even more day-to-day data center operations. To do so: they must use AI to next solve two more persistent data pro tection challenges: 1. Simplifying the management of the data protection software itself. 2. Centralizing and automating DR for more enterprise applications and data. Providers have found sim plifying the management of the enterprise data protection soft ware itself a vexing problem. In trying to protect complex IT infrastructures, the software itself has, out of necessity, become complex to manage. One provider recently stated it has spent millions of dollars and many hours trying to solve this challenge. However, the management challenges users experience persist because platforms

each user utilizes its software differently. To solve this issue, provid ers have begun turning to AI. Data protection software will now initially present a standard interface to all users when they log in. However, this standard ized interface for each user then starts to evolve. After the user logs in, the software’s AI component tracks each user’s activity while he or she uses the software. The data protection soft ware’s AI component then examines the data each user looks at as well as their activ ity. Based on that information, the software creates and main tains a profile specific to each user. Now instead of present ing a standard interface to all users, the software presents a dashboard customized to each user. This customized interface does take some time for the data protection software to create. The time it takes will depend upon each user’s activ ity and how long he or she uses the software each time. However, once customized, each user can ideally more easily navigate the software to access the information they need and perform required activities. It should help reduce the time they need to get trained on the software as the software becomes less com plex to use. Automated Enterprise DR Next Up Using AI to detect ransom ware, validate user identities, and simplify data protection software’s management has

already begun in earnest. In fact, enterprises may already see evidence of these AI-powered features in the data protection software they use. Now, more providers plan to use their software’s AI to solve the thorny issue of auto mating DR in enterprises. Solving the challenge of automated enterprise DR mir rors the other challenges data protection software has already begun to address. To solve the challenges of automated enter prise DR, the data protection software needs access to more of the enterprise’s IT infra structure. To do so, enterprise must provide the software with access to the appropriate poli cies that govern the infrastruc ture’s management. It will take time to access, aggregate, and then analyze all this informa tion. Only then can enterprises look to automate DR in some capacity. Exactly how much time will pass before this automation of enterprise DR comes to pass varies by provider. However, this ideal of automating DR is not as far off as some skeptics may believe. DCIG expects by 2030 some providers will have some early-stage offerings in place with it only becoming more robust during the 2030s. v

Next Up: Identity Protection Having checked these boxes, data protection pro viders have now turned their attention to help further secure enterprise IT environments. Bad actors, whether hackers or nefarious individuals working inside of organizations, now seek to compromise corporate identity management frame works. They often attempt to hack into or gain access to identity management solutions such as Active Directory (AD) or lightweight directory access (LDAP). They do so by seek ing to obtain administrative or ideally root access to these identity frameworks. Once in, bad actors then hold the pro verbial keys to an enterprise’s data. This has made enterprise data protection software a new target for bad actors. Enterprise data protection software often needs administrative privileges to back up corporate data. By compromising the data pro tection software’s administra tive login and privileges, the bad actor gains access to and potentially control of corporate data. To detect and counter these attacks, data protection pro viders have again turned to AI. Details on exactly how they use AI to detect attacks on identity management plat forms remain sparse, for obvi ous reasons. Providers do not want to divulge how they use AI to detect attacks on these platforms. Detecting and repelling these attacks on identity man

Jerome Wendt, an AWS Certified Solutions Architect, is the president and founder of DCIG, LLC., a technology analyst firm. DCIG, LLC.,

focuses on providing competitive intel ligence for the enterprise data protection, data storage, disaster recovery, and cloud technology markets.

32 DISASTER RECOVERY JOURNAL | SPRING 2026