Taiwan Opens Consultation on AI Agent Security Before Financial Systems Go Live

Thirty days to debate safeguards for AI that can act

Taiwan's Financial Supervisory Commission (FSC) opened a 30 day public consultation on October 13, 2026, proposing security safeguards for artificial intelligence agents that can connect to, operate or trigger financial institutions' operational systems. The proposal sets out four risk levels, L1 through L4, and calls for the highest risk actions to require necessary authorization, review or mechanisms to stop execution.

The document, titled Consultation Document on the Safe Use of Agentic AI in the Financial Sector, comes before widespread deployment. Lin Yu-tai, director of the commission's Department of Information Management, said most financial institutions were still experimenting or conducting small trials in controlled environments. He said he had not heard of institutions moving these agents into live business operations.

The commission's central concern is the transition from AI that produces information to AI that takes action. An assistant that summarizes customer complaints presents a different security problem from one that can retrieve confidential records, change information in a system or send a reply directly to a customer.

The consultation therefore focuses on permissions and accountability, rather than model accuracy alone. Institutions would need to determine what information an agent can access, which tools it can use, what actions it can perform and how staff can stop it or take over when something goes wrong.

The document is not a new regulation or a formal guideline. It does not replace existing laws, supervisory requirements, outsourcing rules or internal controls. The commission hopes the consultation will support later guidance, but has set no timetable for that step.

Why operational access changes the risk

Agentic AI can plan steps, call software tools and retrieve information across systems to pursue a task. That distinguishes it from a conversational application whose main function is generating an answer for a person to assess. The security issue becomes more urgent when an incorrect answer can turn directly into an operational action.

Lin identified four main risks: an agent departing from its intended objective, misusing tools or exceeding its permissions, exposing sensitive information, and allowing errors to spread through a sequence of actions. These risks can combine. An agent might retrieve information it should not use, include it in a message and then send that message outside the institution.

AI hallucinations, meaning plausible but incorrect output, remain a concern. Lin described a scenario in which an agent places personal information in an external email where it does not belong. This was an illustration of a possible failure, not a disclosed incident at a financial institution.

Speed also changes the potential scale. Lin contrasted a person handling perhaps one case an hour with an agent potentially handling 100. That example suggests a hundredfold difference in processing volume, not a measured productivity result. His point was that automated mistakes could spread across many cases before staff intervened.

Manage the task, not just the technology

The consultation organizes its proposals around three areas: security governance, risk classification and security controls. Its preferred unit of management is the application scenario, meaning the particular task an institution wants an agent to perform, rather than an AI product considered in isolation.

For each scenario, the institution should establish the business purpose, responsible personnel, permitted data, available tools and allowed actions. It should also identify where outputs may be sent or written and what happens if the agent behaves abnormally.

This approach makes the same software subject to different controls depending on its role. An agent analyzing security information without taking action is different from one with permission to modify an operational system. A change in permissions can therefore matter as much as a change in the underlying model.

Governance would extend beyond an initial approval. The proposals call for review, monitoring, inspection, reduced capability, suspension and recovery mechanisms matched to the risk. Classification should inform approval thresholds, trial conditions, deployment decisions and later supervision, with reassessment as the application changes.

Four risk levels, assessed on three axes

The proposed L1 to L4 classification uses three axes: data sensitivity, the impact of actions, and capability boundaries or the potential for effects to spread. The assessment follows the higher risk result rather than allowing a lower risk feature to offset a more dangerous one.

Data sensitivity concerns what the agent can see. Access to public information is different from access to personal records or business secrets. Action impact concerns what it can do with that information, from reading it to changing records or communicating outside the institution.

The third axis considers the scope and reach of its activity. An agent restricted to a single task differs from one that can process batches, continue operating around the clock or advance through several connected steps. This is where the possibility of a small error becoming a larger operational problem enters the assessment.

For L4 applications, the proposal would prevent high risk actions from executing automatically without necessary authorization, review or blocking mechanisms. Lower risk applications would retain room for trials. The commission says its approach is based on risk, proportionality and neutrality toward particular technologies, with reference to international security frameworks.

Customer service shows how permissions matter

Lin's customer service example provides a practical comparison. An agent that collects complaints and prepares summaries sits in a lower to medium risk range. If it can also send replies to customers, he places it in a medium to higher risk range because it can act outside the institution.

The distinction is not simply whether both applications use AI. In the first case, an employee can review the summary before it affects a customer. In the second, the agent can turn its interpretation into an external communication. Permission to send the message changes the consequences of an error.

Permission to write information creates another boundary. An agent limited to reading cannot, through that permission, alter the original records. Once writing is allowed, mistakes may change or delete information. Controls must therefore address both the quality of the output and the integrity of the systems it touches.

The example also explains why a single label for an institution's entire AI program would be inadequate. Two uses of the same agent may need different approval conditions, monitoring and recovery arrangements because their data access and permitted actions differ.

A digital employee still needs a human supervisor

Lin described an AI agent as a digital employee because it can actively advance a task. The analogy covers more than assigning a role: the institution must decide what it may do, test it before deployment, supervise its activity and have a procedure for stopping its work.

Speaking about responsibility for deployment, Lin made clear that handing a task to an agent does not remove human accountability. In English translation, he said:

AI can only be an assistant; humans are the ultimate supervisors.

That principle puts responsibility on people to define the agent's authority and respond to failures. Existing security controls cannot simply be bypassed because an AI agent is involved. The institution needs both a responsible human decision maker and technical means to enforce the limits that person approves.

The commission's focus is narrower than all employee use of AI. Personal assistants operating within a person's existing authority can be managed through existing internal controls and security arrangements. Connecting an agent to a financial institution's operational systems requires a fresh assessment of risk and information security.

Identity and access controls extend to the agent

The security proposals treat an agent as a nonhuman identity and a software workload. In practical terms, the institution needs to know which agent is acting and what permissions apply to it. Its activity should not become indistinguishable from unrelated users or other automated processes.

The approach draws on zero trust principles: access is not assumed to be safe simply because a system is inside the organization. Identity and permissions must be verified, access limited to what the task requires, and actions made traceable.

Controls cover identity, devices and execution environments, networks, applications and workloads, and data protection. The proposals include both basic and advanced security requirements and control objectives, with behavior controls, visibility and analysis also forming part of the approach.

Lin outlined several concrete measures. Agents should run in appropriately isolated environments, network connections should be restricted according to the task, and tools should be limited to an approved list. Data access should follow the minimum necessary principle. These measures address different routes through which an agent could exceed its intended role, rather than relying only on instructions given to the model.

Testing must cover failures across a whole workflow

For applications that can advance through a workflow, the consultation calls for testing before deployment against prompt injection, tool misuse, inherited permissions and memory contamination. These tests examine how an agent behaves when the information, tools or authority it encounters lead it away from the approved task.

Prompt injection occurs when instructions hidden in material an agent reads attempt to redirect its behavior. Tool misuse concerns inappropriate use of connected software. Inherited permissions can give an agent access carried over from another account or process, while memory contamination involves stored information influencing later activity in an unintended way.

The operational goals extend beyond preventing failures. Systems should be observable, capable of being restricted or stopped, recoverable and traceable. Staff need to see what an agent is doing, interrupt harmful activity and restore affected operations where possible.

Records of the agent's activity should support investigation and audit. Stopping an agent alone may not resolve a failure if it has already written incorrect information or triggered further actions. The proposals therefore connect interruption with recovery and a record of what happened.

A consultation, not a final deployment rule

The commission published the document in the public discussion section of Taiwan's Public Policy Network Participation Platform. It says consultation should bring practical experience into the framework and provide a basis for policy work, possible guidance and supervisory discussion.

Lin said rapid technological change was one reason not to issue a fixed guideline immediately. The commission also cannot assume it has already identified every relevant risk. Its intention to work toward guidance is therefore conditional on feedback and further developments, rather than a promise of a final document on a set date.

The sequence described by the commission is:

• July 2026: The FSC proposed strategies for responding to AI attacks, which Lin described as a precursor to the current work.
• October 13, 2026: The FSC released the agentic AI security consultation document.
• Following publication: A 30 day public comment period allows feedback on the proposed approach.
• After consultation: Possible formal guidance will depend on feedback and technological developments; no publication timetable has been announced.

Current trials help explain the timing. Lin said many experiments were in cybersecurity, such as analyzing security intelligence without directly taking operational action. The commission wants safeguards ready before agents move further into administrative and business processes. The next concrete step is the consultation, while the eventual wording and status of any guidance remain undecided.

Key Points

• Taiwan's FSC opened a 30 day consultation on October 13, 2026, on safe use of agentic AI in finance.
• The focus is agents that can connect to, operate or trigger financial institutions' operational systems.
• The proposal uses four risk levels assessed through data sensitivity, action impact and capability boundaries.
• The highest risk actions require necessary authorization, review or mechanisms to block execution.
• Institutions should limit permissions, retain activity records, and be able to stop agents and recover from failures.
• Human responsibility remains in place even when an AI agent performs the task.
• Most uses remain experimental. The document is not a formal guideline, and no timetable for one has been set.