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Data resilience is the “immune system” of the AI era.
Why?
An increasing number of real-world cases have shown that without a strong data resilience foundation, even the most advanced large AI models and ambitious digital transformation strategies can collapse instantly due to a cyberattack, system failure, or even a simple human error.
Today, we have entered a new era where data is both productivity and a core strategic asset. Enterprises often focus on the impressive capabilities and business value of AI while overlooking a harsh reality: the more powerful AI becomes, the more dependent it is on data; and the more valuable data becomes, the higher the cost of attacks, corruption, leakage, or outages.
Back in 2015, Professor Seamus Ross from the University of Toronto delivered a speech titled Digital Resilience and the Renewable Data Economy at Wuhan University, where he systematically introduced the concept of “digital resilience.” He defined data resilience as “the ability of data to remain complete, available, and trustworthy during disasters or attacks.”
Over the past decade, especially with the explosive rise of generative AI, data resilience has evolved from a purely technical disaster recovery capability into a top-level strategic issue for the digital economy and AI industries, directly impacting the survival and development of enterprises and industries alike.
This shift is the inevitable result of escalating contradictions among growing data value, rising risks, increasingly complex architectures, and stricter compliance requirements.
Ms. Zhao Lirong, Senior Vice President of Info2soft, believes that before data resilience became a strategic priority, traditional enterprise backup and disaster recovery strategies were generally characterized by budget sensitivity, homogeneous solutions, and a focus on basic protection levels.
Although implementation details varied among enterprises of different sizes, the underlying logic remained highly consistent: passive defense centered around “preventing data loss,” without addressing deeper business continuity requirements.
Scholars from disaster recovery associations also recognize this trend, especially among small and medium-sized enterprises. Many organizations assume that “having backups means being protected,” while neglecting backup validation and underestimating the importance of recovery speed and business continuity assurance.
Even large enterprises that proactively explored advanced disaster recovery architectures still largely remained within the framework of traditional thinking. Their focus stayed on business continuity rather than integrating disaster recovery with broader data value creation.
As a result, enterprise backup and disaster recovery systems have long centered on “passively responding to risks and meeting minimum operational requirements,” lacking the resilience mindset of proactive defense, rapid recovery, and continuous assurance.
This industry-wide limitation became increasingly apparent in the AI era. As generative AI exploded and data became the core production factor, the weaknesses of traditional backup and DR architectures were amplified. Data resilience is no longer optional — it has become a mandatory capability for surviving in the intelligent digital era.
As enterprise AI adoption accelerates, data resilience is evolving from an important technical safeguard into a critical factor determining the success or failure of AI strategies.
Because of AI-driven changes in data roles, risk levels, and technological architecture, enterprises now face much higher resilience requirements.
High-Quality Data Supply Is the Lifeline of AIHigh-quality data is essential for AI model training and deployment. Data resilience must ensure continuous, large-scale, accurate, complete, and trustworthy data supply while preventing “dirty data” or invalid data from entering AI pipelines and causing hallucinations, bias, or model failures.
Ensuring Business Continuity and ComplianceThe core objective of data resilience is to ensure uninterrupted business operations and controllable risk.
On one hand, enterprises must respond effectively to disruptions such as data outages or historical data loss to prevent operational downtime. On the other hand, organizations must prevent data leakage and tampering while meeting increasingly strict compliance and audit requirements in AI-driven environments.
As a result, data resilience is evolving from a passive “cost center” into a strategic foundation for stable business operations and long-term growth.
From Static Backup to Intelligent Data Management
Ms. Zhao Lirong pointed out that resilience construction must evolve from traditional “static backup” toward “intelligent data management.”
Modern resilience platforms must overcome the limitations of traditional security protection in areas such as scale, granularity, agility, real-time responsiveness, adaptability, business continuity assurance, and measurable ROI.
The future requires an integrated “perception-decision-execution” resilience architecture capable of proactive defense rather than reactive remediation.
From Data Protection to Data Value Activation
Protection is not the ultimate goal — value realization is.
As enterprises increasingly recognize data as a strategic asset, resilience systems must move beyond simply “protecting data” toward enabling data circulation, utilization, and business innovation.
In the AI era, resilience platforms should become intelligent foundations supporting large-scale AI deployment and enabling data value to continuously flow into business innovation.
In the AI era, disaster recovery is the baseline, but resilience is the upper limit.
Gartner predicts that by 2027, 70% of enterprises will adopt data resilience as a core IT metric, replacing traditional RTO/RPO indicators. Under this trend, traditional backup and disaster recovery vendors that fail to transform may gradually become marginalized.
The transition from traditional disaster recovery to data resilience represents a paradigm shift — from passive defense to continuous assurance and intelligent self-healing.
Industry experts believe resilience is not simply an extension of disaster recovery, but rather a comprehensive reconstruction of architecture, philosophy, products, and business models.
To become true data resilience platform providers, backup and disaster recovery vendors must evolve beyond standalone tools and build core data governance platforms.
Info2soft is among the earliest disaster recovery vendors in China to clearly define and implement a data resilience strategy. With data replication technology at its core, the company focuses on business continuity, trusted data, intelligent self-healing, and continuous optimization to build enterprise resilience foundations.
Info2soft’s technical teams discovered through practical deployments that resilience construction is far more than simple disaster recovery integration. It is a comprehensive engineering system involving data collection, transmission, storage, backup, and recovery across multiple dimensions.
Modern enterprise IT architectures must become more adaptive and resilient — not only ensuring efficient backup and secure storage, but also enabling rapid recovery during incidents while minimizing downtime and data loss.
IDC predicts that between 2025 and 2029, the compound annual growth rate of the data resilience services market will reach 28.3%, far exceeding that of the traditional disaster recovery market.
In response, China’s disaster recovery market is shifting from tool procurement toward platform construction. Info2soft aims to build a unified data resilience platform powered by three integrated engines:
This engine integrates continuous data protection with threat detection modules. Through machine learning, it analyzes IO frequency, file extension changes, and disk queue anomalies in real time.
When ransomware behavior is detected, the system can trigger predictive isolation within three seconds, automatically locking infected hosts and generating clean recovery points before encryption fully spreads.
Combined with whitelist and permission management, testing shows ransomware interception rates can reach 100%.
Built on a high-availability framework, this engine can automatically select optimal standby nodes based on CPU and memory load within 30 seconds.
In real customer environments, core transaction systems achieved VIP failover within 28 seconds, with RPO approaching zero and RTO below 30 seconds, effectively achieving zero business interruption and zero data loss.
With preset scripts and orchestration capabilities, large-scale disaster recovery management becomes highly automated, significantly reducing manual operational workload.
Powered by a real-time data streaming platform, this engine supports physical servers, VMware, OpenStack, Kubernetes, and cloud-native architectures.
Predefined intelligent policies enable real-time synchronization across platforms, regions, clouds, and databases.
Combined with semantic-level parsing, unnecessary fields can be filtered at the source, reducing bandwidth consumption by 40% while supporting enterprise cloud-native transformation and data supply for AI and streaming analytics scenarios.
Data resilience platforms have already been implemented across multiple industries and scenarios.
Ms. Zhao Lirong shared a recent example involving a leading university in China, which used Info2soft’s intelligent backup solution to build protection barriers for AI training data.
The solution delivered:
These capabilities ensured the continuity of AI research and educational operations.
Backup Is the Beginning, Resilience Is the Destination
From the evolution of data protection and business security, backup is only the starting point, recovery is the process, and resilience is the ultimate outcome.
In the AI era, enterprises increasingly realize that simply being able to recover systems is no longer enough. Organizations now demand systems that are trustworthy, verifiable, and self-healing.
Starting from AI-driven data resilience platforms, Info2soft is working together with partners to deliver mature and replicable data management solutions for AI enterprises and critical industries.
From backup software provider to business continuity service provider and ultimately to enterprise-wide resilience enabler, companies like Info2soft are transforming across strategy, cloud-native innovation, AI enablement, platformization, and ecosystem localization.
In the AI era, explosive data growth and increasingly common ransomware attacks have made data resilience the cornerstone of digital asset protection and business continuity.
Enterprises must move beyond traditional passive disaster recovery thinking and integrate resilience throughout the entire business lifecycle. Priority should be given to AI-driven early warning systems, immutable storage, and rapid self-healing capabilities.
At the same time, traditional disaster recovery vendors must accelerate transformation toward cloud-native and AI-enabled integrated resilience platforms.
In the future, data resilience will become deeply integrated with AI workflows, hybrid cloud architectures, and localized IT ecosystems, evolving into infrastructure-level capabilities that support intelligent digital transformation.
In short, resilience is not merely about recovery — it is about moving forward more intelligently.
Source: Adapted from coverage by a leading technology media outlet
