AI and LLM Security for Fintech

Every fintech we talk to this year has the same story. Product wants an AI-powered feature shipped this quarter, whether it's a support copilot that reads customer account data, an underwriting assistant that summarizes financial documents, or an internal agent that can move data between systems. Security finds out after the roadmap is already public. That sequence is how breaches happen, and in fintech the blast radius is bigger than in almost any other sector.

Why fintech is a different risk category

Large language models fail in ways traditional software doesn't. A web app with a SQL injection bug leaks rows from a database. An LLM with a prompt injection vulnerability can be talked into leaking a customer's full transaction history, approving a fraudulent request, or handing a malicious user the exact language needed to bypass your KYC controls, all through a normal-looking chat message. The model doesn't distinguish between "instructions from the developer" and "text pasted in by a user" as cleanly as most teams assume.

For a fintech, that gap intersects directly with regulated obligations: PCI DSS, SOC 2, PIPEDA or GLBA depending on your footprint, and increasingly explicit expectations from banking partners and payment networks that AI features won't become a new fraud vector. An LLM feature that leaks account balances or lets an attacker manipulate a lending decision isn't just a security incident, it's a regulatory and partner-trust problem that can stall a deal or trigger a disclosure obligation.

The other reason fintech is different: your enterprise customers and banking partners are starting to ask about this directly. "How do you secure your AI features" is showing up in vendor security questionnaires and due diligence right alongside SOC 2 status. Teams that can't answer with specifics lose time in procurement, sometimes lose the deal.

Where the actual vulnerabilities live

Most fintech AI risk clusters into a handful of patterns we see repeatedly:

• Prompt injection. Attacker-controlled text, whether typed into a chat box, embedded in an uploaded document, or hidden in a webpage the agent fetches, overrides the system's intended instructions and gets the model to do something it shouldn't.
• RAG leakage. Retrieval-augmented generation systems that pull from a shared document or account store can be tricked into surfacing content the requesting user was never authorized to see, especially when access control is enforced at the application layer but not at the retrieval layer.
• Agent tool abuse. Once an LLM is given tools, the ability to send a payment, query an account, update a record, the model's decision-making becomes part of your attack surface. A model that can be manipulated into calling a tool with attacker-chosen parameters is functionally equivalent to a broken authorization check.
• Excessive agency and insufficient output validation. Systems that let a model act on its own conclusions without a human checkpoint or a hard business-logic guardrail, particularly anywhere near money movement or account changes.

These map closely to the OWASP LLM Top 10, which is the framework we test against because it's the closest thing the industry has to a shared standard, and because it's what your auditors and enterprise customers are starting to reference too.

How traztech scopes it

We treat AI security testing the same way we'd treat a penetration test: scoped to what you've actually built, not a generic checklist run against a demo environment. For a typical fintech engagement that means mapping every LLM-touching feature, chatbots, copilots, document summarizers, internal agents, and testing each one against the failure modes that matter for your architecture. If you're running RAG over customer financial data, we test whether retrieval respects account-level authorization. If you've given an agent tool access, we test whether it can be manipulated into calling tools outside its intended scope. If you're using a third-party model API, we test the boundary between what your system prompt controls and what an attacker can override.

The output isn't a generic report. It's a prioritized list of exploitable issues, mapped to OWASP LLM Top 10 categories, with plain-language explanations your engineering team can act on without needing a security background to parse them. Where an issue ties to a compliance obligation, whether it's a SOC 2 control gap or something a banking partner will ask about, we flag that connection explicitly so it doesn't get lost between the security team and whoever owns your audit.

Full detail on how we run these assessments, what's in scope, and how engagements are structured is on our AI security assessments page.

Where this fits alongside the rest of your security program

AI security testing isn't a replacement for the rest of your program, it's an addition to it. If you're a fintech working toward SOC 2 or already maintaining one, an AI security assessment slots in as evidence that you're managing a risk area auditors and enterprise buyers are increasingly asking about by name. If you haven't started that broader compliance work yet, it's worth looking at alongside our fintech-specific security and compliance work so the two efforts reinforce each other instead of running as separate, disconnected projects.

The fintechs that get ahead of this aren't the ones with the biggest AI feature set, they're the ones who can show a customer, a bank partner, or an auditor exactly how their AI systems were tested and what was found. That's a much stronger position than hoping nobody asks.

Let's scope your assessment

If you're shipping or planning LLM-powered features and want a clear picture of your actual exposure, before an incident or a due diligence request forces the question, get in touch. Contact traztech to talk through your architecture and what a scoped AI security assessment would look like for your team.

The control that matters most sits outside the model

The single most useful design principle we push in fintech AI work is that the model must never be the thing that decides whether an action is permitted. Teams get this backwards constantly. They write an elaborate system prompt telling the assistant to only discuss the authenticated user's own accounts, then wire the retrieval layer and the tool layer to a service account that can read every account in the tenant. The prompt becomes the access control, and prompts are advisory text that a determined user can argue with.

The correct pattern is that every tool call and every retrieval query executes under the end user's own identity, with the same authorization checks your REST API applies. If a user cannot fetch account 4471 through your normal API, the retrieval index must return nothing for that account no matter what the model was persuaded to ask for. Filter at query time using the caller's entitlements rather than post-filtering results after retrieval, because post-filtering leaks through summarization: the model sees the restricted document, and even if you strip it from the response, the assistant's paraphrase of it survives.

Apply the same reasoning to money movement. A model may draft a payment. A deterministic service outside the model must validate the payee against the user's saved beneficiaries, enforce per-transaction and daily limits, and require an out-of-band confirmation above a threshold you set in code. When we test a fintech agent, the finding that ends up at the top of the report is almost always a tool whose parameters the model controls end to end with no server-side validation behind it.

Logging an LLM feature collides with your other obligations

Fintech teams instrument LLM features heavily, because debugging model behavior without full prompt and completion capture is miserable. That instinct creates a compliance problem nobody flags until an assessment.

Prompts contain whatever the user typed. In a support copilot, users paste card numbers, full account numbers, government identifiers, and occasionally screenshots of statements. If you log raw prompts to a general observability platform, you have just extended your cardholder data environment into a SaaS log tool that was never assessed, and you have created a retention problem under PIPEDA and under any data minimization commitment in your customer contracts. We have found primary account numbers sitting in prompt logs at companies whose PCI DSS scope diagram showed the card path terminating at the payment gateway.

Fix it at ingestion rather than at review. Run detection and redaction on prompts and completions before they reach storage, set retention on the trace store to a defined and short window, and put the trace store inside the same access model as your production database rather than in whatever tool the engineering team signed up for. Then write down where prompts go, because the question is now appearing in bank partner diligence packets in the specific form of asking which third parties receive customer data as part of your AI features.

What your model provider's terms actually commit to

Ask any fintech which model API they use and you get an immediate answer. Ask what the contract says about data retention and training and the answer takes a week. This matters because your enterprise customers and your sponsor bank will ask, and because the default terms on a self-serve API key are frequently not the terms you told your customers you had.

Check four things and record them where your compliance evidence lives. Whether inputs and outputs are used for model training under your plan. What the retention window is for abuse monitoring and whether a zero retention arrangement is available to you. Which region processes the request and whether that is contractually pinned or best effort. And whether you signed the enterprise agreement or are running production traffic on an individual account someone created with a personal card, which is more common than anyone likes to admit and is the kind of finding that stops an audit dead.

Model evaluation is not security testing

Fintech teams often tell us their AI feature has been tested, and what they mean is that they built an evaluation harness measuring accuracy, refusal rate, and hallucination on a held-out set. That work is valuable and it answers a different question. Evaluations tell you how the system behaves for a cooperative user on representative inputs. Security testing tells you how it behaves for a hostile user on inputs designed to break it.

The distinction shows up sharply in document handling. An underwriting assistant that summarizes uploaded financial statements will score well on an evaluation set of real statements. Upload a PDF with instructions in white eight-point text in the footer, or in an image the OCR layer reads but a human reviewer never sees, and the same system will cheerfully follow those instructions. We test this directly, because uploaded documents are the highest-value injection channel in lending and onboarding workflows, and because the attacker in that scenario is the applicant, who has every incentive and full control of the input.

The questions bank partners and enterprise buyers are asking

The AI section of vendor diligence has settled into a recognizable set over the past year. Expect to be asked which features use a large language model and whether that is disclosed to end users. Which provider and model, hosted where, under what data terms. Whether the model can initiate any action with financial consequence, and what human review exists on that path. How you prevent one customer's data reaching another customer's session. Whether you have had an independent adversarial test of the AI features, by whom, and when. What your rollback plan is if the model provider changes behavior or deprecates a version.

That last question catches teams out. Model versions get deprecated on the provider's schedule, not yours, and a prompt that was carefully tuned against one version can behave differently on the next. Pin versions explicitly, keep the evaluation and the adversarial test cases in CI so a version bump reruns them, and be able to answer what your fallback is if the endpoint is unavailable. For a payments-adjacent feature, "the assistant returns an error and the user completes the task manually" is a perfectly good answer. Having no answer is not.

The AI you did not build is still in your scope

Most of the AI risk conversation focuses on features your engineers shipped. In practice, a growing share of fintech exposure comes from AI functionality switched on inside tools you already bought. Your support desk vendor adds a suggested-reply feature that reads full ticket history including whatever the customer pasted. Your CRM adds a summarization feature over deal notes. Your code assistant indexes a repository that contains production configuration.

None of that went through your architecture review, because from the vendor's perspective it was a product update. Treat it as a vendor change with a security review trigger: add a question to your annual vendor review asking what AI processing the vendor performs on your data and under whose model terms, and require notification of new AI subprocessors in your renewals. This is unglamorous vendor management work rather than adversarial testing, and it catches real exposure that no amount of prompt hardening on your own product will address.

Responding to an incident in an LLM feature

Plan this before you need it, because LLM incidents have a property that makes standard runbooks awkward. The system is non-deterministic, so "reproduce the issue" may fail even when the report is accurate, and your team can waste a day debating whether anything happened.

Three preparations make the difference. Keep enough trace data, redacted, to reconstruct what context was assembled for a given request, including which documents the retrieval layer returned. Without that you cannot determine whether data was actually disclosed, and under a notification obligation you will be forced to assume the worst. Have a kill switch that disables the feature or drops it to a restricted mode without a full deploy, owned by on-call and tested. And decide in advance who judges whether a model output that included another customer's data constitutes a reportable breach, because that call involves privacy counsel and it should not be made for the first time at 11pm.

What moves the price of an assessment

Scope on AI work is driven by the number of distinct trust boundaries rather than by the number of features. A single copilot with one retrieval source and no tools is a small piece of work. The same copilot with six tools, two of which write to systems of record, plus a document upload path and a multi-tenant index, is several times the effort because each tool needs its own abuse testing and each boundary needs its own authorization verification.

The other real driver is environment readiness. If we get accounts at two different privilege levels in a populated staging environment on day one, testing starts immediately. If we spend the first week waiting for access, or working against an environment with three synthetic records where cross-tenant leakage is impossible to demonstrate, the calendar stretches and the findings get weaker. Our fixed-scope pricing assumes the first case, and the fastest way to keep an engagement small is to have the environment ready before kickoff.

When you should not buy this yet

If your only AI feature is a marketing site chatbot answering from public documentation, with no authentication, no customer data, and no tools, you do not need an adversarial assessment. Rate-limit it, cap spend, keep it off any authenticated surface, and put the budget into your application security testing instead. The worst realistic outcome is an embarrassing screenshot.

If your AI features are still in design, book an architecture review rather than a test. There is nothing to attack yet, and an hour spent deciding that tool calls will run under user identity saves a month of remediation later. That conversation is cheap and frequently ends with us telling a team their planned design is fine and to call us after launch.

And if you have known unresolved problems in the fundamentals, fix those first. A fintech with no MFA on production access, no logging on privileged actions, or an unpatched external attack surface will get more risk reduction from a conventional penetration test starting at $1,000 than from LLM red teaming. The AI assessment is the right purchase when the boring controls are in place and the model has been handed real data or real authority. When it is not the right purchase, we will say so on the scoping call, because selling a fintech an AI assessment while their actual exposure sits somewhere else helps nobody. If ongoing coverage is what you need as features ship, that belongs in a retainer rather than a one-off test.