Like virtually every other facet of IT, cloud disaster recovery is ripe for AI-driven innovation. As Gartner noted in a 2024 report on enterprise backup and recovery trends, AI is poised to “contribute to a simplified operational experience” in this domain.
But also like virtually every other facet of IT, cloud disaster recovery is not a responsibility that businesses can outsource to AI entirely. Although organizations can, and should, take advantage of AI to help accelerate disaster recovery planning and implementation, there are too many variables and unknowns to entrust disaster recovery operations to AI alone.
That, in a nutshell, encapsulates the role that AI stands to play in disaster recovery in 2025 and beyond. For the details, keep reading as we unpack the key benefits that AI brings to disaster recovery, while also highlighting its stark limitations in this domain.
The AI-powered revolution in cloud disaster recovery
Disaster recovery is nothing new. For years, organizations that take business continuity and data protection seriously have had disaster recovery plans and tools in place to help restore systems quickly from backups following a data loss event.
But traditionally, disaster recovery strategies require substantial manual effort. Teams had to develop playbooks by hand to spell out who would do what to restore systems following an outage. They also had to run disaster recovery drills and tests manually to validate that their plans would actually lead to successful restoration. And they had to hope that they had accurately forecasted the challenges that would arise during a data loss event so that their disaster recovery plans would prove adequate for recovering.
With the increasing sophistication of AI, however, businesses enjoy a host of new opportunities for making disaster recovery more efficient and reliable. Indeed, it’s not hyperbolic to call AI-assisted disaster recovery a “revolution” in the way organizations approach data protection and business continuity, given the substantial reduction in time and toil that AI enables in this context, combined with the boost in planning accuracy that AI helps deliver.
How AI can streamline disaster recovery in the cloud
Specifically, AI can enhance disaster recovery in the following key areas.
#1. Predictive failure analysis
One of the fundamental challenges of effective disaster recovery planning is predicting what you’ll need to recover. The list of potential outage scenarios is limitless, and if businesses experience a type of failure that they didn’t plan for, they may as well not have planned at all.
Historically, predicting which types of failures to prioritize in the context of disaster recovery was a manual exercise. Teams sat down, assessed their IT assets and the potential threats they faced, decided which types of outages posed the greatest risk, and formulated disaster recovery plans accordingly. Ideally, they also updated their plans regularly as their priorities and threats changed.
AI tools can streamline the process of predicting failures, while also adding a degree of comprehensive analysis that teams rarely achieve through manual predictive failure analysis. AI does this by parsing all relevant data about a business’s IT assets. It can also predict the types of threats or outages that a business is most likely to face based on characteristics like the business’s vertical, regions of operation, and cybersecurity trends.
The result is accurate, real-time insight into which failure scenarios to prioritize for disaster recovery. By leveraging AI for this purpose, businesses can not only save the time and effort associated with manual failure analysis; they may also discover outage risks that they had not previously considered.
#2. Intelligent backup and restoration
While many modern backup and disaster recovery tools offer automation features that can speed the process of creating backups and restoring systems based on them, AI promises to take backup and recovery operations to a new level of efficiency by enabling what you might call intelligent backup and restoration.
This means backing up and restoring data in a way that automatically prioritizes the assets that matter most to the business. For example, AI-assisted disaster recovery tools could analyze the importance of different servers to the business following an outage, and then recover the most mission-critical ones before moving on to others.
You could implement this type of prioritization manually, but doing so takes time – and when you’re in the midst of an unexpected outage, trying to figure out which systems were impacted and which ones you need to restore first, you typically don’t have a lot of time to spare. By making prioritization decisions faster, AI can help to minimize the impact of failures.
#3. Intelligent disaster recovery testing
In a similar vein, AI can help to drive intelligent disaster recovery tests. This entails using AI to assess simulated disaster recovery scenarios and automatically detect conditions or oversights – such as corrupted backups or missing network settings data – that might cause restoration to fail.
You could perform these tests manually, of course, but that’s time-consuming, especially if you want to validate your disaster recovery plans on a frequent basis (which you should because the constantly changing nature of IT estates means that plans that work one day may prove insufficient in the next).
You could also attempt to automate disaster recovery drills using simple rules-based testing routines that assess predefined conditions (such as whether all expected backup data exists) to validate your plan. But this is a simplistic approach that only allows you to test for your “known known” risks. AI-enabled disaster recovery tests can go deeper by evaluating issues you didn’t even think of, but that are reflected in the disaster recovery scenarios used to train AI tools.
The limitations of AI as a disaster recovery solution
Now that we’ve discussed what AI can do to streamline disaster recovery, let’s talk about why AI alone can’t handle all of your disaster recovery needs.
AI’s greatest limitation for disaster recovery is that AI can’t fully comprehend complex, nuanced situations – and most serious disaster recovery scenarios are complex and nuanced.
For instance, imagine that you’ve lost data due to a malicious insider attack, but you don’t know the extent of the attacker’s access or whether he or she is still active. To ensure that the internal attacker can no longer cause harm, you want to use cross-account disaster recovery capabilities to restore your systems to a “clean” cloud account that the malicious user can’t access.
AI tools might not provide ideal guidance in this situation because they’re unlikely to understand the nuances of your organization’s cloud account management policies. They will instead most likely treat the incident as a generic cyberattack. As a result, they may overlook the importance of recovering using a different account. In turn, they may restore data under an account that the attacker can still access, leading to the data being deleted once again.
Another key limitation of AI-assisted disaster recovery is the challenge of finding relevant data for training AI to understand the specific disaster recovery needs and priorities of a given company. Most organizations don’t experience disasters frequently enough to have vast volumes of data that they can use to train AI tools on how to respond to an outage. As a result, most AI software designed for disaster recovery is likely to be trained on generic data. It will be good at recognizing general patterns and best practices, but it may not be adept at understanding the unique priorities of a specific company – such as which departments to prioritize when restoring systems.
It’s worth noting, too, that backup and disaster recovery tools with advanced AI features remain in short supply. As of 2024, IDC described AI-assisted backup and recovery features as being “under construction.” The analyst firm also predicted that it will be a “period of years” before AI-enabled backup and recovery software becomes widely available.
Keeping humans in the cloud disaster recovery loop
For these reasons, it’s not realistic for businesses to expect AI to become capable of handling all of their backup and recovery needs anytime soon. AI tools can streamline key parts of disaster recovery planning and testing, as noted above. They can also help teams make more effective decisions during recovery operations.
However, humans will remain essential for critical tasks such as:
- Interpreting nuanced contextual factors that AI doesn’t know about or doesn’t assess accurately.
- Double-checking AI-generated recommendations or guidance to ensure they align with the business’s actual needs and priorities.
- Reacting to challenges that AI tools could not foresee because they were not represented in the data used to train AI tools.
- Coordinating with the various stakeholders impacted by recovery operations – which often involve not just technical teams, but also departments like legal (which may need to manage the compliance implications of an outage) and PR (to communicate with customers or the media following an incident).
So, for IT professionals who take pride in keeping data safe and businesses running even in the face of ransomware attacks and other outages, there will always be important roles to play in disaster recovery. AI-enhanced recovery capabilities are just the latest innovations within the long list of advanced data backup and recovery features (with others being cloud network cloning and cross-region recovery, to name just a couple of examples) that help human data protection experts do their jobs even better.
Chris Tozzi
Chris, who has worked as a journalist and Linux systems administrator, is a freelance writer specializing in areas such as DevOps, cybersecurity, cloud computing, and AI and machine learning. He is also an adviser for Fixate IO, an adjunct research adviser for IDC, and a professor of IT and society at a polytechnic university in upstate New York.
