Against the backdrop of global enterprises accelerating the application of artificial intelligence, the Asian digital asset financial service group HashKey Group is also systematically promoting the implementation of its AI strategy.

Recently, HashKey Group officially established a “Group Technology Coordination Committee,” which is directly managed by the company’s leadership to coordinate the overall planning and implementation path of AI and cutting-edge technologies. According to internal notifications, this committee will be responsible for the top-level design of the group’s technical architecture, AI strategic planning, and technical collaboration across business lines, accelerating the organization’s transformation towards greater intelligence and automation.

This organizational move also signifies that HashKey's exploration in the direction of AI has entered a new stage. Previously, AI existed more as personal tools and localized trials within the company; as the capabilities of models improved and organizational awareness gradually matured, HashKey Group began to systematically promote AI at the group level and attempted to integrate it into internal operations, R&D processes, and user service systems.

HashKey Group CTO Devin Zhang recently shared his observations and judgments regarding the company’s AI layout, security framework, and application prospects in the digital asset industry.

Devin believes that we have now entered a critical point for enterprises to introduce AI on a large scale. On the one hand, the capabilities of foundational models have reached a relatively high level and can support more enterprise-level applications; on the other hand, after years of market education and actual contact, the readiness at the organizational and talent levels is also gradually improving. Against this backdrop, HashKey's understanding of AI is more aligned with upgrading organizational capabilities across the entire process.

Q: Why is HashKey systematically promoting AI at this stage?

Devin Zhang: In the past one or two years, AI has already entered the actual usage stage within the company, especially among the R&D teams, where using AI to assist in programming has become quite common. The real change happening now is that the role of AI is transitioning from an efficiency tool at the individual level to a capability system systematically introduced at the company and group levels. There are two main reasons for this becoming a critical node. First, the foundational capabilities of large models have become relatively mature; although they are still rapidly iterating, the underlying capabilities can already support enterprise-level applications. Second, the readiness of organizations and talents is also gradually maturing; after several years of exposure and use, they have developed a foundational understanding and accumulated experience. For HashKey, the goal of promoting AI is to combine human judgment with AI’s execution and efficiency improvement capabilities, supporting larger-scale business growth under compliance and controllable premises.

Q: What is the most direct value of AI for institutions like HashKey?

Devin Zhang: I think it mainly manifests in two aspects. One is the systematic improvement of internal operational efficiency, and the other is the continuous upgrading of user experience. For internal operations, we are currently focusing on two main lines. One is improving the efficiency of the R&D chain, gradually introducing AI capabilities from demand analysis, design, development, testing to online delivery. The other is the non-R&D chain, including teams in human resources, legal, finance, compliance, marketing, and public relations. Many departments are now using AI, but it is still largely point-based use. What is really important is the full-chain AI integration, which enhances the overall collaborative efficiency of the organization. For the user side, many future financial service interactions will also shift from operation-driven to intention-driven, where users express what they want to do, and the system understands the intention, organizes the execution path, and confirms it with the user at key points.

Q: What AI scenarios is HashKey prioritizing at this stage?

Devin Zhang: We are more focused on those scenarios that have clear business objectives, are time-consuming, repetitive, and where efficiency improvement results can be measured. In the R&D system, this idea is mainly reflected in the AI integration of the R&D CI/CD chain; in non-R&D departments, it is more about automating various highly repetitive and clearly defined processes.

At the same time, HashKey has also promoted deeper AI applications in infrastructure security and risk control. In terms of infrastructure security, HashKey has applied AI for threat hunting, case tracing, potential risk discovery, and IT asset management, helping to enhance the depth and breadth of overall security capabilities; in risk control, for anti-money laundering investigations, gang behavior identification, and some complex account issue judgments, intelligent agent collaboration mechanisms have also been introduced, where processes that previously required the risk control team to spend a long time processing can now be analyzed first by intelligent agents, which are then reviewed and delivered by relevant personnel. In terms of R&D, HashKey has also formed clear plans for AI integration in the end-to-end R&D process, with relevant explorations gradually advancing around demand understanding, code generation, testing, and pre-launch audits, and application security audits are also gradually being incorporated into the R&D AI chain.

Q: Why is a security framework the prerequisite for financial institutions to promote AI?

Devin Zhang: Because once AI enters business processes, the objects of governance have changed. In the past, people focused more on whether the model was strong or whether it provided accurate answers, but when the AI agent truly enters enterprise processes, it interfaces with system access, interface calls, data reading, process execution, and even external action triggers. At this point, what enterprises need to manage is a set of execution entities that can access resources, invoke permissions, and complete actions. For financial institutions, the risk focus is more on the agent architecture, permission management, and execution boundaries. To enable the agent to perform tasks, permissions need to be granted to it; once permissions enter real business flows, the boundaries of permission, key management, resource invocation rules, behavior tracking, and accountability need to be redefined. Only after establishing a tiered, decentralized, controllable, traceable, and auditable governance system can AI truly enter core business processes.

Q: As a compliant exchange, how will HashKey grasp the pace and boundaries of promoting AI?

Devin Zhang: We will systematically promote AI and believe that it will bring significant positive effects to the industry, company, and business. At the same time, the pace of promotion needs to be in line with the regulatory environment. Currently, the areas that are more suitable for priority implementation are internal efficiency, backend capabilities, risk management, and AI integration in the R&D chain, as these provide clear value, have high certainty in improving efficiency, and external risk exposure is also easier to control. As for user-side innovation, especially capabilities that directly enter the trading process and may significantly alter user trading frequency and behavior, the pace will be more prudent. Intention-driven interactions can help users simplify operations, but when intelligent agents further enter automated strategy execution or even replace users in making higher-frequency trading decisions, financial exchanges need to more adequately evaluate the boundaries of responsibility, user protection, and regulatory requirements. Whether such capabilities enter the product system is more suitable for gradual promotion under the premise of alignment with the regulatory environment.

At the infrastructure level, HashKey will also adopt a parallel approach of using external enterprise-level platforms and privatized deployment. At this stage, the company will more often use platforms that feature organizational data isolation, security responsibility definition, and multi-model invocation capabilities; for more sensitive scenarios, a privatized deployment path will be retained. HashKey itself will focus on building intelligent agent systems that align closely with its business on top of foundational models.

Looking at a longer cycle, HashKey's understanding of AI extends to the evolution of digital financial infrastructure. Group Chairman and CEO Xiao Feng recently mentioned in an interview that artificial intelligence and cryptographic technology are gradually moving towards deep integration. With the rapid development of AI agents, in the future, agents may gradually possess independent digital identities and payment capabilities, taking on more roles within the on-chain economic system. In this trend, blockchain technology may become an important infrastructure for managing and coordinating AI agents.

Devin believes that AI will gradually change the way financial service institutions interact with users, their backend capabilities, and technical architecture. For HashKey, the short-term goal is to enhance efficiency and experience, the mid-term focus is on strengthening backend capabilities and technical foundations, and the long-term hope is to participate in the evolution of the next generation of digital financial infrastructure. For a compliant digital asset institution, such a promotion path is more realistic.

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