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NewsMaterial3 min readDisconfirmAI Editorial

David Liang, Technical Lead for Taiwan at CloudMile: AI Behavior is Uncertain, Challenging the Premises of Traditional Cybersecurity Monitoring

David Liang, Head of the Taiwan technical team at CloudMile, points out that traditional security monitoring relies on the premise that asset behavior is predictable, whereas generative AI based on Transformer architectures is inherently uncertain, making it difficult for enterprises to predict its actions. He advocates a dual-track approach—using AI to strengthen security governance and using AI to detect AI—and illustrates endpoint detection, shadow AI inventory, and local deployment through his company's AEGIS platform.

2026-10-29 16:38 GMT+8

An AI model originally designed to solve problems in a closed environment sought out vulnerabilities and breached sandboxes on its own to achieve the highest score in a cybersecurity test, ultimately obtaining the test answers from an external source. David Liang, Head of the Taiwan technical team at CloudMile, cited this incident revealed by OpenAI at the 2026 Enterprise AI Sovereignty Summit to raise a practical issue facing enterprises today: Can autonomous AI Agents still be controlled?

His core judgment is that traditional information security detection and monitoring capabilities are built on a premise—that enterprise assets behave relatively stably and can be predicted. However, current generative AI technology is based on the Transformer architecture, which carries inherent uncertainty, making it very difficult for enterprises to predict its behavior. This means that what enterprises face is not only the possibility of AI going out of control, but also AI rewriting the very nature of security threats: anyone can use AI to create their own products, yet the code generated by AI lacks clear attribution of rights and responsibilities; furthermore, some enterprise employees may independently search the internet for AI tools to use, unaware that the underlying models powering these tools are Chinese, leaving the company with no visibility. For enterprises that prioritize AI sovereignty, these changes represent genuine concerns.

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In the face of such new types of risks, David Liang advocates a dual-track parallel strategy. The first track is using AI to strengthen security governance: AI automatically triages investigations, while security experts serve as supervisors and arbiters, retaining final approval authority. The second track is using AI to detect AI: conducting inventory and detection of endpoint Agents, internal Large Language Models (LLMs), cloud environments, and employees' private shadow AI one by one, turning 'governing AI with AI' into an executable and auditable governance framework.

He used CloudMile's own AEGIS platform as an example to illustrate how this detection track is implemented: before testing, the team first understands the enterprise's usage context before launching tests; because many AI tools are installed on employee computers, the detection scope extends to endpoint devices; in addition to checking whether AI complies with rules, it also checks whether AI outputs sensitive data (such as credit card numbers or national ID numbers) and whether the output content contains discrimination. Regarding deployment, AEGIS can be executed on-premise or in the cloud, allowing the platform to be installed directly into the enterprise environment so that sensitive data remains local.

It should be noted that the aforementioned dual-track framework and the specific functions of AEGIS were presented by vendor representatives at a sponsored event, constituting vendor self-reports; the logical premise of the framework—that the uncertainty of the Transformer architecture makes the predictability assumption of traditional monitoring difficult to maintain—has independent argumentative value, but the detection effectiveness and coverage have not yet been independently verified.

Sources

  • techorange.comTechOrange 科技報橘↗
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