On the evening of September 26, 2025, the alarm at the South Korea National Intelligence Resource Management Institute in Daejeon blared. In the surveillance footage, server racks were engulfed in flames, the cooling system failed, and the automatic fire-extinguishing device couldn’t respond in time.

A few hours later, the fire was put out, but the data was completely burned away.

A total of 858TB of government cloud data vanished overnight. Work-related documents of about 750,000 South Korean government employees over the past approximately seven years became lost historical archives.

This was a “memory loss” moment for a national-level storage system. Since the backup was in the same area, the redundancy mechanism failed to activate in time, and all the data was irrecoverable.

The fire exposed a fact: modern storage systems are not weak in hardware but in lacking early warning, decision-making, and self-rescue capabilities.

At that moment, the industry suddenly realized that the competitive logic of storage technology was no longer just about more expensive hard drives and more copies. Instead, it was about endowing storage with the ability to “see, predict, and make decisions,” which is unique to AI.

For a long time, we’ve only seen the market opportunities AI brings to storage, ignoring that AI is actually the lifesaving medicine for storage memory crises.

So, what AI panaceas are hidden in the new era of storage technology? In which scenarios has the intelligent upgraded storage been implemented?

01 Storage Shortcomings Exposed by a Fire

The data center fire in Daejeon, South Korea, is regarded as a national-level data crisis.

Overnight, 647 sets of government business systems went down collectively. Airport passengers were stranded due to the invalidity of electronic IDs, and the bank payment network collapsed…

From this incident, we can also glimpse the weaknesses in storage technology that urgently need to be overcome in the digital age.

Firstly, there is a lack of early warning capabilities. The fire did not erupt instantly. Signs such as abnormal temperatures, power fluctuations, and cooling failures had already appeared. However, the system had neither a sensing mechanism nor the ability to convert these signals into risk judgments. It just operated passively until it completely collapsed.

Secondly, it cannot dynamically allocate resources. When the main storage node was under threat, the system failed to automatically migrate critical data to a safe area or a backup site. All data was “locked” in the same physical space. Although there were backups, they were destroyed together because they were in the same location, making the redundancy useless.

Thirdly, it does not have self-rescue mechanisms before risks occur. A truly intelligent system should actively isolate high-risk areas, freeze sensitive data, and initiate remote snapshot recovery procedures at the critical point of danger. But the storage in this incident was more like a passive container waiting for instructions rather than a guardian that can think and act.

And the digital age has significantly amplified these shortcomings.

The data center fire in Daejeon caused such a huge crisis partly because there is more and more data in this era, and it is becoming increasingly important. Ten years ago, the loss of a piece of paper-based file might only affect one department. Today, citizens’ identity information, medical records, financial transactions, and travel trajectories are all stored in the depths of hard drives in data centers in binary form, along with confidential documents of hospitals, transportation, banks, and other national institutions. A data fire is like pulling a single thread and causing the whole fabric to unravel.

A sudden fire has exposed the most vulnerable trump card of the digital age, and similar crises are constantly unfolding around the world.

In 2022, a Google data center in Iowa, the US, suddenly exploded. A few hours later, core services such as Google Search and Maps were interrupted, affecting the daily use of millions of users.

In 2024, a fire broke out at the DigitalRealty data center in Loyang, Singapore. The fire lasted for more than 36 hours, affecting some businesses of Alibaba Cloud and ByteDance, as well as multiple service providers such as DigitalOcean, Coolify, and Cloudflare.

In November 2025, a major failure occurred at Cloudflare, a well-known global network infrastructure service provider. Many well-known Internet services including ChatGPT, X, and Spotify experienced a collective outage worldwide.

These seemingly isolated incidents reveal a deep-seated crisis in storage memory in the digital age: current storage technology is too fragile and passive. From national policy documents to a citizen’s single trip, a single failure can trigger a series of chain reactions.

Faced with such severe challenges, it is urgent to upgrade the core capabilities of storage. So, who can repair these structural shortcomings and enable the system to see, judge, and even act before risks appear?

02 AI: The Lifesaving Medicine for Storage Systems

Data is expanding at an unprecedented speed.

According to the “Data Age 2025” report released by IDC, by 2025, about 491EB of data will be generated globally every day. If calculated based on each photo being 3MB, 491EB is equivalent to 175 trillion mobile phone photos.

At the same time, business demands are changing rapidly, resource loads are fluctuating like tides, and network attacks are lurking everywhere, with ransomware, data tampering, and zero-day vulnerabilities emerging endlessly.

The ever-increasing data, constantly changing resources, and hard-to-defend security crises are forcing storage to upgrade.

Among various technologies, AI has become the most suitable one. Because among all the technological options, only AI can simultaneously endow storage with three capabilities:

Intelligent Operation and Maintenance: AI Enables Storage to See Risks

In the past, system failures were often discovered only after a collapse. Now, AI can capture abnormal signals in advance by continuously learning the operating status of hardware and software. For example, NetApp’s ActiveIQ platform analyzes telemetry data from millions of devices in real time, including changes in I/O patterns, latency fluctuations, power status, and even cabinet temperature trends. It combines machine learning models to dynamically assess risk levels and actively push repair suggestions.

Intelligent Scheduling: AI Eliminates Manual Resource Allocation

Data can be “hot” or “cold,” with vastly different access frequencies. Traditional storage relies on administrators to manually tier data, which is inefficient and lagging. Modern storage systems equipped with AI engines can achieve fully automatic intelligent scheduling. For example, the intelligent data management engine built into Huawei’s OceanStor Pacific series can track file access popularity in real time. It automatically migrates frequently accessed hot data to high-performance SSD layers and sinks long-idle cold data to high-density, low-cost HDDs or object storage. The entire process requires no human intervention, improving resource utilization by more than 30% while significantly reducing costs.

Proactive Security: AI Enables Storage to Have Preemptive Reactions

Along with the digital age, hackers’ attack methods are becoming increasingly diverse. Passive defense is no longer effective against the increasingly rampant ransomware attacks. The new generation of AI-driven security mechanisms enables storage to have an immune response. Take IBM Storage Defender as an example. It continuously monitors data access patterns through a behavioral analysis model. Once it detects typical attack characteristics such as abnormal encryption, large-scale file renaming, or permission mutations, the system will immediately automatically isolate the infected data volumes, cut off the propagation path, and seamlessly switch to read-only snapshots or secure copies to continue operations, ensuring business continuity and data integrity.