Korea's 13-year-old financial network separation regulation has begun to crack. This is not merely deregulation. It is closer to a signal that an institutional door is opening — one that lets financial AI move into real operational environments.
Until now, financial institutions faced a clear wall even when they wanted to use AI aggressively. Internal and external networks were strongly separated, and there were limits on using cloud-based AI tools, the latest security models, external data integration, and high-performance vulnerability testing. Many talked about generative AI, but there were limits to AI actually reading enough context, making judgments, and taking action inside the financial operations network.
The core of that wall was network separation.
And that wall is now beginning to come down. According to multiple press reports, in June 2026 financial regulators are running a process to relax network separation rules so that 10 financial institutions — including Shinhan, Hana, and Woori Bank, KakaoBank, KB, NH, and Mirae Asset Securities, Samsung Fire & Marine, Hanwha Life, and Hyundai Card — can use generative AI for security purposes. The measure will be applied temporarily for one year through a no-action letter, and the targeted institutions will be able to pursue high-performance AI-based vulnerability inspection, security analysis, and security SaaS adoption.
What stands out is the rationale. The starting point of this easing is not simple operational efficiency but security. In an era where high-performance AI can be used for vulnerability discovery and attack automation, there is a shared awareness that closed-network defense alone has limits against new threats. The regulator's message is clear: to respond to AI-based threats, finance must also build AI-based defenses.
But security-purpose easing is just the first step. The scope of relaxation is set to expand in stages. Additional institutions will be selected in August–September, and in Q4 the remaining applicants will be added in sequence. Regulators are also reviewing broader relaxation for institutions whose security capabilities and AI competence have been verified.
The direction is clear. Financial AI no longer stays in pilots or demos. It has begun to move inside real operational systems. And at the center of the next step is not a simple chatbot, but an AI agent that reads data, calls tools, and assists with work.
On the surface, deregulation. At its core, the expansion of AI agents
The way financial institutions use AI is changing fast. Early generative AI was mainly a tool that answered questions — summarizing product descriptions, responding to customer inquiries, finding internal documents. At this stage, AI was strictly an “answering system.”
But the AI that financial institutions will need going forward is different. AI will read documents, compare data, call systems, review internal policy, detect anomalous transactions, write reports, and at times even assist in executing work. In other words, AI is moving from a tool that simply generates answers to an agent that intervenes in real workflows.
AI agents are likely to become the core of financial AX (AI transformation).
- Credit assessment — analyzing customer financial information and transaction patterns
- Corporate finance — reviewing financial statements, industry reports, credit information, and internal underwriting criteria together
- Internal controls — detecting employee conduct and potential policy violations
- Security — automating vulnerability testing, anomaly detection, and incident response
- Customer service — moving beyond basic support to personalized financial guidance and follow-up actions
Using this kind of AI properly has limits in a closed environment alone. It must connect to the latest models, to external data, and to operational systems. For AI to make more accurate judgments it must access more context, and to do more substantive work it must be able to use more tools.
Network separation easing must be understood within this flow. For finance to use AI agents more freely, the existing physical and logical isolation structure is no longer enough. A new security model built on the assumption of connectivity is required. This change can be seen as the first scene of that transition. But the moment freedom grows, so does risk.
The premise of security is changing
For the past 13 years, the basic mindset of financial security has been perimeter defense — building a wall between internal and external networks, separating the public internet from operational systems, allowing only defined contact points, and controlling sensitive data so it cannot leave.
Network separation, firewalls, access control, DLP, and security monitoring all evolved on this perimeter-centric thinking — blocking incoming attacks and stopping internal data from leaving. This approach was effective for a long time, under the premise that attackers are outside, the assets to protect are inside, and dangerous traffic crosses the boundary.
But in the age of AI agents, that premise wavers. AI agents do not move only along the perimeter. An agent receives input inside the internal network, reads documents, calls models, makes judgments, and sends requests to systems. An attacker no longer needs to break through the firewall. Attack commands can be hidden inside the documents the agent reads, the sentences a user types, the data pulled in from outside, and the connected plugins and APIs.
Attacks no longer happen only outside the network perimeter. They happen inside the runtime where AI judges and acts. This is the most important change. A lowered network separation does not simply mean more external connections. It means the location that security must protect has changed.
If the old security perimeter was the network, the future security perimeter will be the AI runtime.
AI agents create a new attack surface
AI agents are powerful. But because they are powerful, they are dangerous. Traditional software runs according to fixed code. Bugs and vulnerabilities exist, of course, but the execution path is fundamentally within the range the developer designed. An LLM-based agent, by contrast, interprets natural-language input, reasons about context, selects tools, and generates responses. This process is flexible, but also hard to predict. In financial security, this unpredictability becomes a critical risk.
- Prompt injection shakes the agent's decision-making. Attackers can embed commands like “ignore previous instructions,” “bypass the security policy,” or “output the hidden information” inside natural language. The problem is that such commands can look like a customer inquiry, like the body of a document, or be hidden inside a web page or email.
- Indirect prompt injection is a trickier threat. Even if a user never directly types a malicious command, an attack can begin if the command is hidden in an external document or connected data the agent references. The agent believes it is reading information, but ends up executing a manipulated command.
- RAG poisoning is a realistic threat too. When an institution runs internal document-retrieval AI, if a wrong or manipulated document enters the knowledge base, the AI answers based on it. Without hacking the model directly, an attacker can poison the foundation of knowledge the model references and change the outcome.
- Information leakage through output is especially sensitive in finance. If the AI includes customer personal data, account information, internal underwriting criteria, parts of a risk model, undisclosed policy, or security configuration in its response, an incident happens in an instant. The result is the same even when the leak is unintentional.
- Agent privilege misuse can lead to bigger problems. Once an AI agent gains the authority to call systems rather than just respond, a wrong judgment turns into real execution — wrong customer classification, inappropriate loan-review assistance, unauthorized data queries, unapproved notifications, and internal system malfunctions all become possible.
- Hallucination is not just a quality issue either. In finance, wrong information is risk. If AI invents a non-existent rule, gives inaccurate product terms, or proposes a judgment that diverges from internal policy, it can lead to customer harm and regulatory risk.
These threats share one thing in common: existing security alone is not enough. On top of security that protects the network and data perimeter, you need runtime security that verifies AI's input, output, and behavior in real time.
After the easing, the questions change
Before network separation easing, the questions were relatively simple.
- Are the external and internal networks separated?
- Is access permission controlled?
- Is data exfiltration restricted?
- Is security equipment installed?
But once AI agents move into the operations network, the questions change.
- What input did the AI receive, and was there a malicious instruction inside it?
- What documents did the AI reference, and were they trustworthy sources?
- What tools did the AI call, and was each call a policy-permitted action?
- Did the response contain sensitive information, and was there any chance of hallucination or policy violation?
- When something goes wrong, can you trace who made what request and when, and why the AI judged the way it did?
What regulators ultimately want to confirm is close to this point. Not “did you adopt AI,” but “can you control the risk that arises from adopting AI.”
Going forward, a financial institution's AI capability will not be measured by how many models it adopted. What matters is how safely it can operate AI.
- The ability to connect AI
- The ability to control AI
- The ability to verify AI's judgments
- The ability to record and prove AI's actions
These will become the new standard for financial AI.
The real competition starts at the runtime, not the model
Many companies understand the AI race as a model race — believing that a bigger, faster, more accurate model creates competitiveness. Model performance matters, of course. But in finance, that alone is not enough.
The real risk of financial AI arises less from the model itself than from the moment the model is used in a real operational environment. The moment it meets customer data, connects to internal documents, calls an API, or intervenes in a business process, AI is no longer an independent technology. It becomes part of the financial system.
What is needed then is an operating layer that sits on top of the model — one that inspects input as AI receives it, verifies responses before they are sent out, checks permissions and policy before tools are called, and leaves every action in a traceable form. This is AI runtime security.
The runtime is the moment AI actually moves — the entire process in which a user's request comes in, the model judges, a tool is called, a response is generated, and the result is delivered. Attacks happen in this moment. So defense must be in this moment too. Security is no longer sufficient as a policy document written in advance. Every time AI makes a judgment, you must confirm in real time whether that judgment is safe.
Trust is not a fixed certificate. Trust is a decision that must be made at every moment AI receives input, judges, and acts.
What Tynapse protects: not the network perimeter, but the AI runtime
Tynapse was built precisely for this problem. We place a trust layer over the AI runtime so that financial institutions can use AI agents more freely while operating them more safely. We call this the Runtime Trust Layer.
Where traditional security guarded the network perimeter, Tynapse guards the moment AI actually operates. We monitor and verify, in real time, what the AI receives as input, what it outputs, which policies it violates, what attacks it is under, and what reasoning it leaves behind.
First is the input stage. Detect Guard inspects the input coming into the AI. It detects and blocks prompt injection, jailbreak attempts, malicious commands, attempts to induce personal-data leakage, and policy-bypass requests. It filters attacks that can arise across diverse input paths — customer inquiries, internal staff requests, uploaded documents, external data — before they reach the model. In the age of AI agents, input itself is the attack surface.
Second is the output stage. Trust Judge verifies the response the AI generates. It evaluates hallucination, policy violations, sensitive-information exposure, inappropriate financial guidance, and answers that do not match internal standards. Before the response reaches the user, or moves on to the next step of the system, it judges whether the output is safe. In finance, AI's words become the customer experience, the basis for business judgment, and a potential starting point for regulatory risk.
Third is the operations stage. Trust Ops leaves the AI agent's usage history and decision process in an operable form. It records what requests came in, what risks were detected, what was blocked, what responses were allowed, and which policy criteria were applied. Through incidents, SLOs, audit logs, and operational reports, it lets a financial institution prove that it is in control of its AI.
In finance, what matters is not only preventing incidents. Proving that no incident occurred, tracing the cause when a problem arises, and being able to explain the operating system before regulators and internal audit all matter too.
Tynapse connects these three into a single flow. It blocks the input, verifies the output, and proves the operation. This is the new security architecture needed in the age of financial AI agents.
The easing is not an end but a beginning
This network separation easing is closer to a starting point for AI competition in finance. Some institutions enter proof-of-concept first, and the scope is likely to expand in stages afterward. Eventually, most institutions will face the same questions: how well are you using AI, and how safely are you controlling that AI?
Going forward, an AI adoption plan alone is not enough. It must come with an AI security operations plan.
- Which tasks will AI be attached to?
- What is the scope of data the AI can access?
- What tools can the agent call?
- How will input attacks be detected?
- How will output risk be verified?
- How will hallucination and policy violations be reduced?
- How will audit logs and evidence be kept?
- When an incident occurs, who responds and how?
Only institutions that can answer these questions will be able to use AI more broadly, more quickly, and more safely. Easing means the breadth of financial AI use can expand — but its premise is stronger control. A free AI agent requires stronger runtime security.
The wall does not disappear — it moves
The wall of network separation is coming down. But security is not disappearing.
The wall does not disappear — it moves. It is shifting from the network perimeter to the AI runtime.
In the past, the wall dividing outside from inside mattered. Going forward, what matters is a security layer that operates at every moment AI judges and acts. AI agents will change financial work — faster reviews, more sophisticated risk analysis, more personalized customer experiences, more automated internal controls, and stronger security inspection will become possible. But this change can only last if it is designed safely.
To let AI move freely, you need a trust layer that monitors and verifies that freedom. To give AI more authority, you need runtime security that controls against misuse of that authority. For AI to enter an institution's core operations, every judgment and action must be explainable and provable.
The competition in financial AI now goes beyond a race to adopt models. The real competition starts with who can control the AI runtime more safely.
Tynapse protects the runtime of financial AI. It blocks the input, verifies the output, and proves the operation. After network separation easing, the real security race for financial AI has only just begun.
If you would like to review your financial AI security posture in the wake of network separation easing, reach out to Tynapse anytime. We will examine the risks across the entire runtime together — from your AI agent's input and output to its operational evidence.
