How AI is transforming data recovery for the modern era

What is AI-powered data recovery?

Businesses use a variety of data restoration strategies to recover lost applications, data and file systems. These approaches include replication through multiple copies, scheduled full and incremental backups, and snapshot-based methods that capture workload or data state changes for continuous data protection.

Traditional data recovery methods rely on reverse engineering, cooperation from vendors and complex manual processes that can take data recovery specialists weeks or longer to complete. Modern AI data recovery tools increasingly integrate ML algorithms and AI models to analyze file systems, data structures, historical patterns and emerging threats. These tools use backup anomaly detection to identify unusual changes in data size or behavior and apply pattern recognition to continuously improve self-learning data recovery and intelligent data restoration.

By the end of this decade, 90% of backup and protection tools will incorporate GenAI -- including chatbots and natural language processing -- to enhance management and support functions, compared with fewer than 25% in 2025, according to Gartner's June 2025 report on backup and data protection platforms. Additionally, 35% of enterprises are expected to implement autonomous backup systems driven by agentic AI, compared with less than 2% in 2025.

Businesses must ensure backup systems are capturing and protecting all critical data, including system configurations, databases, SaaS application data, websites and user files, so it can be reliably restored in the event of a system failure or outage. Unfortunately, many organizations set up backup systems but fail to test them. As part of this process, it's important to detect misconfigurations as well as corrupt or incomplete data files before the systems and data need to be restored. New tools can help ensure data integrity and availability, so users can access trusted information when needed.

"In recovery, a lot of the problem is prioritizing what needs to get recovered quickly," said Jon Brown, senior analyst for data protection, ops and sustainability at Enterprise Strategy Group, now part of Omdia. Some companies, Brown added, find AI helps them decide: "What order should we do these operations in? What should we restore first? The other big area is just being able to test more, being able to use AI to test your restores and to automate some of that process."

The application of AI to improve automated backup validation across enterprise, SaaS and cloud environments is critical, especially as threats like ransomware show no sign of slowing down. AI and ML are increasingly integrated into security systems to monitor environments and take actions when risks such as viruses and malware are detected. More than half (51%) of 375 IT and data professionals from midmarket and enterprise companies in North America believe AI and ML will enhance their ability to recover data after ransomware attacks, according to Enterprise Strategy Group's 2024 report "Reinventing Backup and Recovery With AI and ML."

AI tools can help streamline a "messy and complicated" task by enabling IT professionals to pinpoint compromised data infrastructure and file systems and restore them to their original state before the attack. A ransomware attack can go on for months without detection, especially if the threat actor is stealthily encrypting older files.

"Generally, in recovery, you are dealing with a date: 'Here's when everything went bad,'" said W. Curtis Preston, a data protection veteran (aka Mr. Backup) and author, whose latest book, Learning Ransomware Response & Recovery, is available in early release. "In a curated recovery, the AI can say, 'Let's just look at all the different files and let's automatically select the most recent version of every file before it got encrypted,'" Preston said. "Doing that manually is a giant pain in the butt, but doing it automatically should be a relatively easy task. Don't try to restore your OS, rebuild that stuff. Just try to restore your database and all your files."

How is AI changing the data recovery lifecycle?

The data recovery lifecycle is a key component of data lifecycle management -- a strategy that governs data handling from creation to deletion through policies and protections for data storage and usage. During data recovery, businesses identify and assess the scope of a data loss event, then implement a recovery plan to restore the data to its original state using backups and other recovery tools.

The type of recovery -- such as file system recovery, backup restoration, disk or partition recovery, and raw data recovery -- depends on the nature and severity of the event. It's essential to validate that the restored data is complete, accurate and fully functional before resuming operations. Businesses also need systems in place to monitor and secure the data and prevent further loss.

Increasingly, AI tools enable organizations to move beyond basic restoration by forecasting system failures, automating recovery workflows and optimizing backup strategies as well as resource allocation. Although many of these capabilities are still evolving, AI-powered tools can support data recovery in several ways.

Predicting failures

Data security tools use historical and real-time data, along with statistical and ML algorithms, to perform predictive analytics. These technologies analyze logs, historical data, performance information, documentation and real-time sensor data to build equipment models and detect potential risks and failures. AI-powered tools such as predictive data recovery systems also enable organizations to automate backups and failovers to safeguard data.

Detecting anomalies

AI-driven tools monitor network traffic using ML and data analytics to analyze normal behavior and patterns based on historical data, enabling the software to detect deviations that might indicate signs of a potential security incident, malware or data breach.

"A lot of the ability to recover from ransomware attacks is being able to identify them sooner," Brown noted. More products are embedding AI-based anomaly detection. It's not just static, signature-based ransomware detection AI, but dynamic, behavior-based analysis, he said, adding that AI capabilities integrate with security tools to help answer critical questions, such as: "Is someone trying to do something to our backup data?"

Automating recovery workflows

AI and ML can enhance the ability to classify, access and recover backup data more efficiently. Enterprise Strategy Group reported that 46% of IT professionals surveyed also expect GenAI to assist in producing data recovery plans, streamlining what has traditionally been a manual and time-consuming process. A GenAI tool, for example, could automatically create a step-by-step recovery plan based on the enterprise's infrastructure, data classification policies and recent backup activity -- identifying priority systems, recommending restore points and flagging potential gaps in the backup coverage.

Most of these plans have key performance indicators. Recovery point objective defines the maximum acceptable data loss, while a recovery time objective refers to the downtime a company can tolerate without significant disruption. During the planning process, IT professionals can compare vendor-provided aggregate data, Brown said. They can also use synthetic AI data to run their own automated disaster recovery with AI scenarios.

In the future, AI recovery tools could autonomously orchestrate end-to-end restoration workflows, selecting optimal recovery points, provisioning infrastructure, validating data integrity and executing failover with minimal human oversight.

Optimizing backup scheduling and prioritization

AI tools can help organizations allocate resources more efficiently by analyzing backup strategies and infrastructure, such as network usage and cloud versus on-premises storage, and comparing them with performance metrics and data from application and cloud service providers. These strategies can include data deduplication, data compression and tiered storage options. Amazon Web Services, Microsoft Azure and other hyperscalers offer scheduled backup and recovery services for virtual machines, databases and files, along with support for tiered storage to optimize cost and performance.

Validating data integrity

Organizations implement various controls, technologies and processes to maintain data integrity throughout its lifecycle. Many businesses follow the ISO/IEC 27001 framework, which emphasizes the confidentiality, integrity and availability of information. To verify the integrity of backup data before it's needed for restoration, backup administrators may move select files to another location and compare them against the originals. To verify the validity of data once it's restored, hash matching, checksums and AI validation tools can ensure the data is complete and accurate.

"In larger cloud-native environments, we are starting to see artificial intelligence being used to automatically spin up recovery environments. It's called data rehydration," said Bill Kleyman, CEO and co-founder of AI platform provider Apolo and executive chair of Informa/AFCOM programs for data center and IT professionals. AI tools can help restore archived or infrequently accessed data to a higher performance tier, essentially rehydrating the data to validate its integrity.

"Basically, what used to take hours to do manually can now be done in a few minutes -- the entire orchestration for a site, for an edge location, anything along those lines," Kleyman explained. "AI can flag risks. It's not there to replace people, so human validation remains critical. You've got to have a human in the loop. You can't just give the keys to AI entirely."

Challenges and risk factors of AI in data recovery

Many organizations are worried about the data privacy and compliance risks associated with AI-driven data recovery. Integrating AI into data recovery processes presents technical and operational risks, including the need for high-quality training data to ensure accuracy, the risk of AI-generated hallucinations and concerns about the security practices of third-party vendors. Businesses can encounter several challenges when adopting AI-powered tools, including the following:

• High-quality model training data. Like all AI systems, AI-driven data recovery relies on ML algorithms and high-quality training data, including labeled data sets and real-world failure scenarios, to ensure recovery processes are accurate, effective and reliable. "Relying on the vendors' expertise and their ability to see thousands of environments is a value add," Brown noted. Enterprise Strategy Group reported that 59% of IT and data professionals believed they would face high costs to recreate AI models, due to data loss, corruption or changes in infrastructure. These challenges might lead some businesses to reevaluate their vendors, particularly when backup systems fail to protect the training data essential for maintaining and retraining AI models.
• Risk of AI hallucinations. ML algorithms could fabricate or wrongly identify data, causing the AI-powered tool to inaccurately reconstruct and restore files. If the training data in the ML model is biased or incomplete, the data recovery tool could learn and adapt based on compromised inputs. AI hallucinations can also lead to AI-driven disaster recovery plans that prioritize the wrong data backup and system restoration processes.
• Supply chain and third-party vendor security risks. Data recovery is more complex when it involves third-party vendors, especially if their controls fail, resulting in a breach or compliance violation. Businesses remain responsible for data privacy and security violations caused by third-party vendors that interact with their data through AI models and related tools.
• Skills gap. In addition to the hardware and software costs of implementing AI for backup and data recovery, setting up and maintaining these systems requires expertise in AI and machine learning -- skills that many IT and data center professionals currently lack. This gap can pose a significant barrier to the adoption and effective use of AI in data recovery strategies.
• Compliance with data recovery laws. The use of AI data recovery tools must conform to data protection and compliance regulations. Businesses must be able to identify and classify sensitive data and comply with industry regulations, including HIPAA, PCI DSS, GDPR, CCPA and the Sarbanes-Oxley Act for publicly traded companies. And under strict guidelines set by the EU's Digital Operational Resilience Act (DORA), organizations must meet recovery requirements for data backup, duplication, retention and deletion, as well as implement redundancy measures to maintain operations during system failures or disasters.

What is the future of AI-driven data recovery?

Vendors are embedding AI-driven tools for anomaly detection, predictive failure analysis and policy optimization into their platforms across hybrid environments -- on-premises systems and public cloud services -- using APIs to help automate and orchestrate backup and recovery.

Gartner reported that 75% of enterprises will use a unified backup and recovery system for on-premises and cloud data by 2029, up from 25% in 2025. "We're entering a new era of information -- one with models, checkpoints and logs," said Kleyman. "AI isn't just powering the business; it now has its own backup and recovery needs." AI training data, especially for GenAI, must be backed up to protect against data loss and safeguard model development.

But nearly two-thirds of IT and data professionals reported that their organizations are backing up only half of their AI-generated data, according to Enterprise Strategy Group. Key reasons for backing up this data include privacy, compliance, redundancy and the ability to validate and test it. This AI data backup gap poses serious security risks, especially for companies lacking process automation, since data loss can lead to costly model retraining.

Businesses are essentially planning for the unknown when it comes to backup and restore, Brown surmised. The nature of the next breach can't be predicted, he said, adding that AI can serve as a trusted advisor as it acquires practical experience and deep institutional knowledge about the organization. "The issue is that we have four massive fire drills a year when it comes to data protection on average," Brown acknowledged. In those situations, he added, it helps to have AI that's "seen it before" and can assist in managing the response.

Kathleen Richards is a freelance journalist and industry veteran. She's a former features editor for TechTarget's Information Security magazine.