In case you somehow missed it, businesses are embracing AI to add efficiency and consistency to a wide array of processes – and cloud disaster recovery is no exception. Although AI was not traditionally a core component of most disaster recovery tools or operations, forward-thinking organizations are now realizing that AI can help speed recovery and reduce costs, while also reducing the risk of errors that could prevent successful restoration of data following a breach.
Keep reading for a deep dive into how AI can streamline cloud disaster recovery, along with tips on how to get started adding AI to disaster recovery plans. We’ll focus on what AI is currently capable of doing – as opposed to how AI tools might theoretically evolve in the future, a topic we cover in “The Future of Streamlining Disaster Recovery Procedures with AI”.
The role of AI in cloud disaster recovery
Disaster recovery is the process of restoring data and workloads following a data loss incident – such as a ransomware attack or accidental deletion of data – that renders them inoperable.
Traditionally, disaster recovery processes didn’t depend on AI. Instead, businesses either carried out recovery operations manually by copying data from backups into production systems, or they used basic scripts to automate the process.
AI, however, offers new opportunities to streamline disaster recovery routines. As IDC notes in a report about the future of Disaster Recovery/Business Continuity (DR/BC):
“The next step for DR/BC will require healthy doses of AI. Even if hardware, applications, or entire sites fail, executives will have greater confidence in recovery if their systems have built-in intelligence that will capture the issue, act on it, remediate it, and keep the business running. AI will play a pivotal role in getting closer to that reality.”
Use cases for AI in disaster recovery
How, exactly, can AI assist in disaster recovery? Key examples of leveraging AI in this context include:
- Using AI to analyze backup data and assess whether information might be missing or corrupt. This helps to prevent scenarios where recovery fails due to deficiencies in data backups.
- Applying data analytics to recovery plans as a way of identifying opportunities to optimize the plans. For instance, AI tools might be able to analyze data about the success (or failure) of past recovery plans to find patterns that help businesses determine which recovery practices work well, which don’t and how they can improve recovery strategies going forward.
- Automatically developing runbooks or playbooks to guide disaster recovery efforts. Generative AI tools could do this by analyzing an organization’s backup data and the systems it needs to support, then producing guidance on how best to restore systems following an outage.
- Scanning systems following a recovery operation to identify problems that engineers might have overlooked, such as misconfigured permission settings or data assets that were not successfully recovered.
We should note that not all of these use cases necessarily require AI. You could analyze backup data or recovery plans manually to look for oversights or opportunities for enhancement. However, AI helps to streamline processes like these because AI tools can identify patterns much faster than humans. They’re also better at analyzing large volumes of information.
Benefits of integrating AI into disaster recovery plans
By integrating AI into disaster recovery plans, businesses can benefit in several key ways.
Improved accuracy and efficiency
Simply backing up data is not enough to guarantee that systems are safe against failure. Problems with backups, such as critical information that backup tools didn’t collect because of a misconfiguration or data that becomes corrupted due to disk input/output (I/O) issues during the backup process, can cause recovery operations to fail.
Traditionally, organizations have relied on recovery tests and drills to help mitigate this risk. Tests and drills allow them to carry out simulated recoveries as a way of confirming that they’re actually able to recover successfully based on their backups.
Recovery tests and drills remain important. However, by adding AI to the mix, businesses gain another tool for proactively identifying issues that could cause recoveries to fail. For instance, by automatically comparing backups to production systems, AI tools might detect small differences that could reflect data corruption within the backups. In turn, they can alert engineers so that they can address the problem. This is faster and simpler than having to perform a recovery test or drill to identify the issue.
Reduced downtime and faster recovery
AI tools have the potential to deliver dramatic boosts in the speed of data recovery, which translates to lower overall downtime.
AI can do this in several ways. One is optimizing recovery plans in order to make them more efficient prior to an actual recovery event. Another is helping to determine which assets to prioritize in the midst of a recovery by, for instance, helping teams to assess quickly which systems failed and which didn’t so that they can focus on recovering only those systems that actually need to be recovered. On top of this, automatically validating that data was successfully recovered is another way that AI can speed recovery operations.
Cost savings through optimization
AI also has the potential to lower the total cost of backup and disaster recovery operations. By optimizing backup routines and recovery plans, it can reduce waste that might bloat costs; for instance, AI tools could help teams identify redundant data within their backups so that they can reduce the overall size of backup data and, by extension, reduce storage costs.
In addition, enabling faster recovery with less manual effort on the part of engineers can free staff to focus on other endeavors that create more value for the business. This also effectively reduces costs by decreasing expenses related to staffing recovery teams.
How to get started with AI-driven cloud disaster recovery
To date, few data backup and disaster recovery vendors have integrated AI features directly into their products – and technology buyers should be wary of those who have slapped the “AI” label on their tools because in some cases, vendors are using the term loosely by claiming that any kind of automation is a form of AI, which is not the case.
And, lest someone accuse us of being overly critical of our competitors here, allow us to cite IDC, whose report on AI in disaster recovery states that “it’s still early days for comprehensive AI in disaster recovery and business continuity solutions, although most vendors have some type of technology they position as AI, even if it doesn’t fit the strict definition.” IDC adds that AI-powered features are not likely to become a major part of the disaster recovery tooling landscape until at least 2025.
This means that integrating AI into cloud disaster recovery strategies requires more than just purchasing a tool that claims to be AI-ready and calling it a day. What businesses can do, however, is take advantage of generic AI technology and apply it to disaster recovery scenarios.
Following are the basic steps for doing so.
#1. Identify AI use cases
First, determine what you want to do with AI in the context of disaster recovery. Is your goal to increase the accuracy and reliability of recovery operations because you’ve experienced issues in the past? Is it to save money because you’re facing budget constraints? Is it something else?
Figuring out what you want your AI solution to do is important for deciding how you’ll do it.
#2. Choose an AI tool or platform
Next, select an AI tool or platform capable of supporting your intended use cases. In general, any of the so-called generative AI foundation models – such as OpenAI’s GPT models and Google Gemini – can perform tasks related to AI-driven disaster recovery, such as analyzing recovery plans or generating playbooks. The advantage of these solutions is that they’re pretrained and easy to use.
That said, it’s also possible – though not easy – to build your own AI model, or customize an existing open source model, provided you have access to the software development resources and expertise necessary to do so.
#3. Expose models to relevant data
Once you’ve chosen an AI tool or platform to use, you’ll need to feed it the data it needs to understand your use cases. For example, if your goal is to generate a playbook, you could expose the model to a mapping of your files, directories and databases, then ask it to suggest recovery steps. Or, you could feed in a mapping of your backup data structure and your production systems and request advice on how to improve backups to increase the chances of successful recovery.
Keep in mind that exposing sensitive business data to third-party AI tools and platforms can present some privacy risks. To mitigate them, choose a model that offers strict guarantees and controls over how it manages user data. Or, if feasible, avoid exposing sensitive information altogether by instead sharing information like file directory structures, rather than the directories themselves.
#4. Train and update AI-driven workflows
Because your backup and recovery needs are likely to change frequently, you’ll also want to update the AI-powered workflows that help drive your operations. For instance, if you deploy a new application or database, you may want to generate updated playbooks or reassess your recovery strategy to ensure that you’re factoring in the change.
Implementing a comprehensive disaster recovery strategy with N2WS
In the years to come, AI is poised to become an increasingly important tool in organizations’ efforts to optimize disaster recovery. But even once AI-based features in disaster recovery tools have fully matured, they’ll remain just one of many types of capabilities necessary to enable successful disaster recovery.
Businesses will also require features like automated disaster recovery testing, as well as the ability to immediately recover data across cloud regions, across cloud accounts and across entire cloud platforms – all of which you can already do with N2WS. This means that even as they experiment with AI-enabled disaster recovery, organizations will also need tried-and-true solutions like N2WS at their disposal to deliver core disaster recovery capabilities.
To see for yourself how N2WS delivers fast, reliable and efficient disaster recovery, request a free trial.
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.
