AI Security for Fintech

AI security for fintech means securing and governing the AI and LLM features fintechs and financial-services firms ship — credit scoring, fraud detection, KYC, and AI assistants for customers and staff — against the OWASP Top 10 for LLM Applications and the AI governance standards (EU AI Act, NIST AI RMF, ISO/IEC 42001). CyberSigma threat-models your AI stack, tests guardrails and data isolation, reviews governance, and hands you a prioritised, board-ready report. We are CERT-In empanelled and PCI QSA authorised (CEMEA, Asia Pacific and the USA).

The AI security risks that matter most in fintech

In fintech, AI increasingly drives decisions about people — credit, fraud, onboarding — which brings explainability, bias and regulatory scrutiny on top of the usual model-security risks. An assessment is only useful if it targets the risks your AI use cases actually create:

• Bias and lack of explainability in credit or risk models that decide outcomes for customers, drawing regulator and reputational risk.
• Prompt injection and excessive agency in AI assistants that can move money, change limits or read account data.
• Sensitive financial-data leakage through prompts, fine-tuning sets or model responses.
• Model and training-data poisoning targeting fraud or AML detection.
• Third-party and foundation-model supply-chain risk in a regulated environment.

Which AI standards should fintechs and financial-services firms map to?

Beyond the sector-specific risks above, these are the cross-cutting AI standards we assess against:

• EU AI Act — the risk-tiered AI regulation that applies extraterritorially to anyone placing AI systems on the EU market or whose AI output is used in the EU; high-risk and general-purpose AI carry specific obligations.
• NIST AI Risk Management Framework (AI RMF 1.0) — the leading voluntary framework for governing, mapping, measuring and managing AI risk.
• ISO/IEC 42001:2023 — the certifiable AI Management System standard, with ISO/IEC 23894 for AI risk management.
• OWASP Top 10 for LLM Applications — the de-facto checklist for securing LLM and generative-AI features.
• Financial regulation and data-protection law — AI decisions in finance attract regulator scrutiny on fairness, explainability and the personal data involved.

What a CyberSigma AI security review covers for fintech

We assess how your AI systems behave under attack and how they handle data, not just your model card. In a typical engagement we:

• Threat-model the AI/LLM stack against the OWASP Top 10 for LLM Applications — prompt injection, insecure output handling, sensitive-information disclosure, excessive agency and the rest.
• Test guardrails for real: jailbreak and prompt-injection resistance, input/output filtering, and what the model does with untrusted content from tools, RAG sources and users.
• Review data governance for AI: what personal or sensitive data feeds training, fine-tuning and prompts, the lawful basis for it, and whether customer data leaks across tenants or into a foundation model.
• Secure the model pipeline: access to model registries, secrets and MLOps tooling, supply-chain risk in third-party and foundation models, and protection against model and data exfiltration.
• Assess governance and oversight against ISO/IEC 42001 and the NIST AI RMF: accountability, human oversight, documentation, and evaluation of high-risk use cases.
• Check monitoring and incident response for AI-specific failure modes — abuse, drift, harmful output — not just classic infrastructure alerts.

Representative engagement: a lender’s AI underwriting assistant

A useful way to picture the work: a lender’s AI assistant helped underwriters and could pull customer data. We red-teamed it against the OWASP LLM Top 10, reviewed explainability and data governance, and delivered a remediation plan that satisfied both the security team and the risk function. This example is representative of how we structure these reviews; named client references are available under NDA on request.

How long does an AI security review take, and what does it cost?

Most AI security reviews run a few weeks, depending on how many models, applications and data flows are in scope. Cost follows that scope rather than a fixed list price, so we run a short, free discovery call, agree the scope in writing, and give you a fixed quote before any work starts. If you are working to a launch or customer deadline, tell us the date and we will tell you honestly whether it is achievable.

How do you handle AI fairness and explainability for credit decisions?

We assess whether high-impact AI decisions have documented rationale, human oversight and the explainability your regulator expects, alongside the security testing — using the NIST AI RMF and EU AI Act high-risk lens.

Can an AI assistant with account access be hijacked?

If it has excessive agency and weak guardrails, yes. We test prompt-injection and authorisation paths and show you how to constrain what the assistant can do with money and data.

Does the EU AI Act treat our use cases as high-risk?

Several financial use cases — particularly creditworthiness assessment — can fall into the EU AI Act’s high-risk tier. We help you work out where your use cases land and what that means.

How often should we review our AI systems?

At least annually, and again whenever a model that drives decisions changes materially or a new AI assistant gains access to sensitive systems.