零日攻擊:未知的威脅及資訊安全的挑戰

在網路安全的領域中,零日攻擊(又名零差別攻擊,Zero-day attack)是一種難以防範的威脅。零日攻擊指的是攻擊者利用尚未被軟件開發者發現或修復的安全漏洞來進行攻擊,這種安全漏洞在被發現和利用之前,甚至連安全專家也不知道其存在。

零日漏洞是軟件或系統中的安全缺陷,這些缺陷未被開發者或安全專家在產品發佈前發現,從而使攻擊者有機會透過惡意軟件或代碼實施攻擊,控制目標系統,盜取數據或造成其他破壞。 

2017年的WannaCry勒索軟件事件是一個經典的零日攻擊案例。在該事件中,攻擊者利用Windows系統中的網路共享漏洞,迅速將攻擊在全球範圍內傳播,影響了包括醫院、銀行和政府機構在內的數萬個系統。該次攻擊不僅造成了巨大的經濟損失,還暴露了全球資訊安全體系中的重大漏洞。 

零日攻擊的影響範圍非常廣泛,可以針對個人用戶的系統,企業的服務器甚至是國家級的基礎設施。而由於其未知性及複雜性,此攻擊往往能夠繞過一些傳統的安全防御措施,例如防火牆和殺毒軟件等等。而一旦被成功攻擊,不僅會有可能造成龐大的經濟損失,還可能會損害企業或者機構的聲譽,嚴重時甚至可以影響到國家安全。 

雖然零日攻擊難以完全防範,但可以採取以下策略來減少被攻擊的風險:首先,軟件供應商應在發現安全漏洞後盡快推出補丁。因此,及時更新軟件和系統,保持其處於最新版本是防範攻擊的重要一步。其次,實施網路隔離和最小權限原則可以限制攻擊者在系統內的活動範圍,並確保即使系統被侵入,攻擊者也難以輕易獲取敏感資訊。再者,定期進行安全評估和滲透測試,通過模擬攻擊來檢測系統潛在的安全漏洞,以便提前發現並修復可能被零日攻擊利用的缺陷。最後,加強員工的安全意識,訓練他們識別釣魚郵件、惡意附件等常見的網路攻擊手法,可有效減少漏洞被利用的機會。

面對零日攻擊的威脅,無論是個人還是企業,都應采取多層次、多角度的防禦措施。透過持續的技術更新、系統的安全檢測和員工培訓,可以有效提高對抗零日攻擊的能力,盡可能保護重要的敏感資訊。防範零日攻擊不是一次性的任務,而是一個持續的過程。隨著技術的發展及攻擊手法的不斷進化,防範策略也應不斷調整和更新。只有通過持續的努力和投入,才能在不斷變化的網路安全威脅面前保持堅強的防線。 

尹展軒 

Senior IT Consultant

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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.