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.