Digital Trust –企業的強心針

互聯網的無邊界令企業與顧客的關係日趨緊密,當網絡世界中的商戶、服務、產品及交易不勝其數,而虛假資訊、網路陷阱及駭客攻擊等同時又充斥著網絡,隱私及資訊安全無疑已成為顧客篩選商戶的重要考量,到底企業如何才能從中脫穎而出,除了取得顧客的信任及支持外,又能永續安全及穩定的營運模式?數位信任(Digital Trust)便是其中一支企業可予以考慮的「強心針」。

參考國際電腦稽核協會(ISACA)的官方定義,數位信任為「對一數位生態系統內消費者與供應商之間的誠信關係、互動及交易的信心」。比如說,由服務供應者(如雲端供應商、銀行機構)對企業,以至企業對顧客的整條數位供應鏈中,都由對彼此的數位信任環環相扣。一般而言,對顧客構成數位信任的關鍵可從以下指標作衡量:

  1. 「品質」(企業的服務或產品品質是否合符顧客的期望?)
  2. 「準確性」(資訊是否定期更新、準確以及可用的?)
  3. 「隱私及安全」(顧客的資訊是否受保護及保密?)
  4. 「道德及誠信」(企業的行為是否合符道德及正直?)
  5. 「透明度及誠實」(顧客會否被告知收集個人資訊的用途及實際情況?)
  6. 「適應性」(企業的運作是否穩定或恢復程度是否高?)

企業擁有良好的資訊安全管理系統固然能夠提升顧客的信任度,但相比於傳統的品質安全或資訊安全管理,數位信任的實踐更見利於強化顧客與企業間的聯繫、從不同層面改善企業運營表現、減少觸犯資安或隱私法律風險等等。

然而,數位信任的定義、指標、實踐框架等概念較廣泛,現時亦沒有特定的監管機構及標準,企業在數位信任實踐、監管、評估或稽核上要如何執行?相信數位信任為企業提升競爭力的一大機遇,同時亦是一大挑戰。

Theo Tam 

IT Consultant

More Updates

Further reading

Will AI Really Replace Entire Industries

The statement "Certain industries will be replaced by AI" is only half true. While AI will indeed replace a significant amount of "work content," it is rare for an entire industry to vanish across the board. Instead, industries are undergoing internal division of labor, restructuring, and upgrading. Replacing Functions, Not All Roles Multiple economic studies indicate that AI will impact approximately 40% to 60% of jobs. In these cases, some processes will be automated, while others will see productivity boosted by AI. Highly repetitive tasks—such as data entry, basic customer service, and routine report writing—are easily taken over by AI. However, the same industry will simultaneously create new roles focused on supervising AI, designing processes, and integrating systems. The Risk is Real, But It’s Not Doomsday Analysts estimate that AI and automation may "expose" hundreds of millions of jobs to replacement risks, particularly in white-collar administration, customer service, and certain areas of programming. At the same time, research from the World Economic Forum and major banks predicts that AI-related transformations will create new job categories. These include machine learning engineers, AI safety and ethics experts, and digital transformation consultants. Why Humans Retain the Advantage Currently, AI excels at standardized, predictable, and data-driven tasks. For work requiring empathy, complex communication, cross-domain judgment, and creative strategy, AI remains a tool for assistance rather than a total replacement. Many studies emphasize that "Human-Machine Collaboration" will become the mainstream model: Humans set the direction, make decisions, and bear responsibility, while AI handles calculation, generation, and analysis. How to Respond: Don't Fear Replacement, Learn to Utilize It The group facing the highest career risk is often not "people affected by AI," but "people who don't know how to use AI." Within the same job function, individuals who master AI tools will possess significantly higher productivity and competitiveness than their peers. Practical actions include: Learning to deconstruct work into automatable and non-automatable components. Mastering at least one AI tool (e.g., Generative AI, RPA, Data Analysis). Strengthening "non-programmable" capabilities, such as communication and problem definition. Personal Perspective: Reframe "Displaced" as "Restructured" Instead of asking, "Will Industry X be replaced by AI?" you should ask: "Which part of this industry's value chain is most susceptible to automation, and can I position myself on the side that designs and controls these systems?" Thinking this way offers far more actionable value than abstractly worrying about being "replaced."

Speed Without the Sprawl

Leveraging OutSystems' rapid development strengths, our team achieves true Agile development, focusing intensely on user requirements. However, requirements are never fixed; they take time to refine within the project's cycle. If a developer simply builds projects based on the initial requirements, it leads to significant rework when those requirements are inevitably revised. This creates serious technical debt that can derail a project's schedule. To combat this, we strictly follow the OutSystems Canvas Design architecture to define each module's usage and content. We generalize logic into foundational modules, optimizing reusability and providing high adaptability when requirements change. This approach allows us to eliminate complicated dependencies—avoiding the deployment nightmares that plague monolithic systems. The Real-World Challenge: "The Spaghetti Monolith" We’ve all seen it. A project starts fast. The "Idea-to-App" time is record-breaking. But as sprints pass and requirements evolve, the "interest rate" on technical debt spikes. Suddenly, changing a simple UI element breaks a core business process because the logic was trapped inside the screen. Deployment becomes a "big bang" event where everything must go live at once because of circular dependencies. In our team, we don't just "code fast"; we architect for resilience. Our Solution: The 4 Layer Canvas Strategy We treat the 4 Layer Canvas not just as a suggestion, but as our structural imperative. Here is how we use it to handle volatile requirements:  Isolating Volatility (End-User Layer): We keep our User Interfaces (UI) and interaction logic in the End-User Layer. This layer is highly volatile—it changes constantly based on user feedback. By isolating it, we can redesign a "Customer Portal" without risking regressions in our core business rules.Stabilizing Business Logic (Core Layer): We abstract our entities and business rules into the Core Layer. This is the backbone of our factory. Whether the data is accessed by a Mobile App, a Web Portal, or a Timer, the validation rules remain consistent. This promotes the "Don't Repeat Yourself" (DRY) principle.Enabling Independent Deployments: By using Service Actions (Weak Dependencies) in our Core layer, we decouple our modules. This allows different squads to deploy changes independently without forcing a factory-wide refresh—a critical enabler for our CI/CD pipelines.The Governor: AI-Driven Architecture How do we ensure we stick to these rules when moving at Agile speeds? We don't just rely on manual code reviews; we use the AI Mentor System. This tool acts as our automated architect. It scans our entire factory to detect architectural violations that humans might miss, such as: Upward References: Preventing foundational libraries from depending on business logic.Side References: Ensuring our End-User apps don't tightly couple with one another.Circular Dependencies: Identifying the "deadly embrace" between modules that locks deployments.The AI Mentor System quantifies this debt, allowing us to pay it down proactively before it hinders our release velocity. Join a Team That Values Architecture In our Taiwan office, we believe that low-code doesn't mean "low-architecture." We are building resilient, composable enterprise ecosystems that can scale. If you are a developer who cares about structural integrity, clean code, and mastering the art of OutSystems architecture, we want to hear from you.