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As AI gets smarter, why are trusted data and a secure foundation more essential than ever?

The People's Bank of China and eight other departments recently jointly issued the Notice on Strengthening the Development and Utilization of Data in the Sci-Tech Finance Sector, which explicitly calls for pilot programs for innovative development of trusted data spaces in sci-tech finance and for promoting the open sharing and integrated utilization of data resources in the sector.

The Notice proposes that, centering on data indicators such as enterprise lists, sci-tech innovation attributes, operations, R&D investment, intellectual property and innovation capability evaluation, the aggregation and sharing of public data, industrial data and other resources should be strengthened, and models such as authorized data utilization and joint modeling should be explored.

This means that the release of data value is entering a new stage.

As artificial intelligence moves from demonstrating model capabilities to real business applications, the questions companies focus on are also changing. Once AI enters industry scenarios, it needs to handle more complex business data, connect to more business processes, and at the same time meet requirements for security, reliability and manageability.

As AI capabilities continue to improve, trusted data and a secure operating environment are becoming an important foundation for driving the large-scale application of artificial intelligence.

Over the past few years, large models have developed rapidly, and AI's understanding, generation and reasoning capabilities have continued to improve.

In real applications, AI not only needs to understand general knowledge, but also needs to combine a company's own data and business experience to complete specific tasks.

However, enterprise data is usually scattered across different systems and business links, involving operational information, customer data and internal knowledge. As Agents begin to call tools, access data and execute tasks, new problems facing AI applications have gradually emerged.

In recent AI security incidents, hackers used large model capabilities and permission management vulnerabilities to carry out attacks, once again showing:

The closer AI gets to real business, the more important trusted operation capabilities become.

When enterprises deploy AI, they focus not only on model performance, but also need to address issues such as data usage boundaries, training and validation environments, and application security governance.

This is also why trusted data spaces have attracted attention.

Trusted data spaces enable the secure release of data value

It is against this backdrop that trusted data spaces have attracted attention.

Through authorization management, security mechanisms and trusted circulation methods, trusted data spaces can promote the collaborative utilization of data resources among different entities, releasing data value while safeguarding data security.

The pilot programs for innovative development of trusted data spaces in sci-tech finance promoted by the nine departments this time are precisely intended to further advance the integrated utilization of public data, industrial data and enterprise data, and improve sci-tech finance service capabilities.

For artificial intelligence, trusted data spaces can provide a more reliable data foundation for model training, business analysis and agent applications.

The release of data value needs to be built on secure and trusted circulation mechanisms.

In the AI era, security capabilities become an important foundation

As Agents gradually enter enterprise processes, artificial intelligence is taking on more and more real tasks.

Compared with traditional question-and-answer applications, Agents need to call tools, connect to business systems, and complete continuous tasks according to goals.

Recent AI security incidents, including intrusions involving GPT systems, also remind the industry that security governance must keep pace as AI gains stronger capabilities to execute tasks.

In the process of applying AI, enterprises need to pay attention to data usage boundaries, model operation risks, and security control during task execution.

The deeper AI goes into the core of business, the higher the requirements for trusted operation capabilities.

CloudWalk Technology: Building trusted AI operation infrastructure

The development of trusted data spaces aligns closely with CloudWalk Technology's long-standing focus on AI infrastructure.

Over the past decade and more, CloudWalk Technology has continued to accumulate core artificial intelligence technology capabilities, from computer vision and multimodal understanding to large models and agent systems, driving AI capabilities from perception and understanding toward task execution.

As AI enters more industry scenarios, what enterprises need is not only model capabilities, but also data, training and security systems that support the continuous operation of AI.

CloudWalk Technology continues to develop capabilities that address these needs.

At the data level, the company explores the building of trusted data spaces, promoting the circulation and utilization of data resources under conditions of authorization, security and traceability.

At the training and validation level, relying on the Wuhan National Cybersecurity Base integrated training and inference center, the company builds a controlled training environment for large models and agents, allowing models and Agents to complete testing and optimization before entering real business.

The company is also developing security capabilities such as content review models, providing risk identification, security checks and governance support for AI-generated content and intelligent applications.

Together, these capabilities form important support for AI from data utilization and training validation to business application.

From trusted data to trusted Agents

For AI to truly enter enterprises, it needs to go through a complete process from data foundation to business application.

Trusted data provides reliable input for the space, the controllable training environment helps models and Agents improve stability, and security governance capabilities safeguard the application process.

On this basis, Agents that have undergone training and validation can enter specific business processes.

Around the Xingyun Agentic OS and Mofang Agent Lab, CloudWalk Technology is exploring enterprise-level Agent applications.

Xingyun is responsible for the organization, scheduling and operation of the AI team, enabling Agents to work collaboratively around goals; Mofang accumulates industry knowledge, job skills and business processes to help Agents adapt to specific scenarios.

The development of artificial intelligence is entering a new stage.

Model capabilities determine what tasks AI can complete, while data, training, security and operation systems determine whether AI can truly enter industries.

CloudWalk Technology will continue to develop AI infrastructure, agent systems and industry applications, bringing AI into more areas such as finance, manufacturing, marketing and urban governance.

Let data flow better, let AI be more reliable, and let intelligence truly serve business.

Artificial intelligence is moving from "being able to think" to "working reliably."