How can AI assist in disaster recovery? That may sound like a simple question, but the answer depends on whether you mean what AI is capable of doing today versus what it could do in the future.
Leveraging AI for backup and disaster recovery is still in its very early stages. Cloud users can use AI models and perform preliminary tasks like analyze recovery plans and generate playbooks for disaster recovery drills, however, there are still significant gaps and limitations.
How AI Will Transform Disaster Recovery
- Identify problems with AI-driven predictive analytics.
- Automatically fixing a data crash with self-sealing systems.
- Setting recovery priorities.
- Intelligent backup and restoration.
- Providing automated compliance monitoring.
While AI’s capabilities in disaster recovery are limited today, there are still reasons to get excited about its potential in this space. There are plenty of ways in which AI could dramatically boost the efficiency and effectiveness of disaster recovery tools.
1. AI-Driven Predictive Analytics
One way AI could benefit disaster recovery is making recovery unnecessary in the first place by identifying problems before they happen.
AI-driven predictive analytics features would analyze historical data about outages and suggest which types of issues might lead to future incidents. In turn, predictive analytics would allow businesses to mitigate their risks before new outages occur.
Sophisticated predictive analytics tools already exist, so disaster recovery vendors likely wouldn't need to build them from scratch. For example, companies are already exposing their models to a mapping of files and databases to suggest recovery steps.
Making a feature like this work in practice would mainly require enough data about historical outages trends to enable AI tools to identify meaningful patterns and provide relevant guidance to help IT teams mitigate issues proactively.
2. Self-Healing Systems
If a system still fails despite an IT team’s best efforts to react to warnings about potential issues, AI could also help in recovery by automatically healing the issue. For instance, if an application crashes, AI could determine why it crashed, then resolve the issue, without requiring explicit guidance from humans. The result would be faster recovery with lower effort on the part of engineers.
The notion of AI-powered self-healing systems is not especially new; it has been discussed for years in the context of AIOps. But most discussions have focused on the theoretical, because to date, the ability of AI tools to resolve problems on their own has been limited. AI might be able to fix simple issues, like broken network settings that prevent successful copying of data from backup storage to a production system during recovery operations. But AI in its current form is less likely to be able to do something like fix a memory leak bug inside an application's source code, then recompile and redeploy the app.
That said, it’s not impossible to envision use cases like this. AI is already capable of generating code and processes like compiling and deploying apps are easy to automate. It’s just a matter of linking capabilities together in a way that enables true AI-driven self-healing.
3. Setting Recovery Priorities
In most businesses, some IT assets are more important to operations than others. This means that in the event of an outage, teams will ideally recover the most mission-critical systems first, then move on to restoring less important ones.
Currently, an organization’s ability to decide what to prioritize is a manual affair. Engineers must interface with other stakeholders within the business to understand which systems matter most from a business perspective. They must also take into account the technical requirements of each system and the complexity of restoring it following an outage. And they need to translate all of this into a disaster recovery plan that makes it easy for technicians to decide what to prioritize.
With AI, however, the process of setting priorities could become much more automated. This can prove to be extremely beneficial to companies as their businesses scale their cloud providers and increasingly rely on multicloud systems. Instead of manually collecting information from business users about the importance of each system, AI-powered chatbots could collect the data and then assess it alongside technical information to generate disaster recovery plans accordingly. A capability like this would require a blend of analytical AI and generative AI technology.
4. Intelligent Backup and Restore
AI tools also have the potential to formulate more intelligent backup and restoration strategies. By intelligent, I mean plans that are designed to be as efficient as possible by avoiding redundancies or wasted time.
For example, a business might currently operate two instances of the same database in order to increase the reliability of the database. The data inside the two database instances is identical, so the database only needs to be backed up, and in the event of an outage, recovered once in order to restore normal operations. But because the organization's backup and recovery tools are configured to back up and restore all assets, each database instance is handled separately. This leads to redundancies in backup data. It could also slow down recovery because the same database would effectively be restored twice.
AI tools designed to identify redundancies in backup and recovery strategies could flag an issue like this as a source of inefficiency. They could even potentially update backup and recovery configurations automatically to mitigate the issue.
Here again, the AI capabilities necessary to build a feature like this exist in the form of sophisticated analytics tools. But to enable those tools to help optimize backup and restore strategies, the algorithms that power the tools would need to be customized to recognize trends related to data backup and recovery. They might also need to be trained using data sets that reflect good backup and recovery configurations.
5. Automated Compliance Monitoring
Today, ensuring that backups meet whichever compliance obligations a business faces is a largely manual affair. It involves demonstrating to auditors that backups are in place, and that they adequately address compliance risks.
With AI, however, this process has the potential to become much more automated. Compliance requirements could be defined using code, and AI tools could automatically verify that the requirements are met.
An approach like this would not only save time on the part of auditors. It would also reduce the burden placed on IT staff to supply compliance information. And it would add consistency to compliance reviews because the compliance process would be automated, eliminating the inconsistencies that can arise when one human auditor interprets compliance rules or evidence differently from another.
Compliance as code is not a new idea. However, few tools or frameworks currently exist for expressing compliance mandates using code. AI tools can be designed to interpret code-based compliance rules and monitor systems in response to them.
Don’t Wait on AI for Disaster Recovery
Tomorrow's technology isn’t protecting your business today and as data losses, outages and ransomware attacks increase, strategies must be in place today.
Businesses can brace for AI advancements in disaster recovery, and at the same time, begin to implement additional efficiency and reliability to their operations. While AI-powered features may offer exciting possibilities for the future, there are current automation tools ready to deliver sophisticated data backup and recovery capabilities that businesses need right now to ensure rapid and reliable disaster recovery.
Cloud users looking to optimize their operations now should be on the look out for the following key features in a backup tool:
- A cloud-native IaaS approach to backup to provide a secure and streamlined solution that allows the user to maintain control of their data within their own environment.
- Ability to recover data across multiple cloud regions, accounts, and platforms.
- Automated network configuration management, ensuring that network configurations remain consistent and can be replicated quickly across different accounts, regions and clouds.
- Ability to leverage proven disaster recovery drilling and reporting automation features without waiting for AI advancements
- Seamless API Interactions which avoid less secure SaaS options and slower file-by-file backups
- Automated and scheduled disaster recovery testing to ensure an immediate and healthy failover. This is paramount for increasing compliance requirements and audit trails.
- Smart long-term archiving which takes advantage of automated archiving to the most cost-effective storage tiers with fast restoration capabilities. This ensures optimal cost savings and minimal cloud waste.
Take a smart approach to achieving success even before AI advancements are upon us. Since each company has unique evolving needs, you should regularly review and update your strategy by using the most innovative disaster recovery management tools available in order to operate efficiently and cost effectively.
