AI & LLM Security for Fintech & Financial Services

AI & LLM security for fintech protects the AI systems behind your products and operations from prompt injection, data leakage, model poisoning and excessive agency. CyberSigma red-teams these fintech AI flows and maps governance to PCI DSS, RBI and SEBI cyber frameworks, GDPR/DPDP, and model-risk-management expectations, plus OWASP LLM Top 10, NIST AI RMF, ISO/IEC 42001 and MITRE ATLAS. We are CERT-In empanelled and PCI QSA (CEMEA) authorised.

A real fintech AI attack: the manipulated banking assistant

Consider a banking support assistant that reads a customer's uploaded statement to answer questions. An attacker hides instructions inside that document (indirect prompt injection) telling the model to reveal another account's balance or initiate a transfer through a connected tool. No code was exploited — the model was talked into it. We red-team exactly these flows: injection through uploaded content, data leakage across customers, and excessive agency in payment or KYC actions.

What we test (OWASP Top 10 for LLMs + MITRE ATLAS)

We adversarially test your fintech LLM and GenAI applications the way a real attacker would:

• Prompt injection — direct and indirect (documents, web pages, tools).
• Sensitive information disclosure — PII, secrets and system-prompt leakage.
• Insecure output handling — XSS, SSRF and code execution from model output.
• Excessive agency — agents/plugins taking unauthorised or destructive actions.
• Training-data poisoning and model/data supply-chain risks.
• Jailbreaks, guardrail bypass, model extraction and denial-of-wallet.

AI governance & compliance for Fintech & Financial Services

We map AI controls to your sector's obligations and the global AI frameworks:

• PCI DSS for any AI touching cardholder data.
• RBI / SEBI cyber and model-governance expectations.
• Model risk management for credit, fraud and trading models.
• ISO/IEC 42001 + NIST AI RMF for AI governance.

Can AI security findings support our PCI DSS and RBI/SEBI obligations?

Yes. We map AI red-teaming and governance evidence to PCI DSS, RBI and SEBI expectations, including model governance and security of cardholder and customer data.

How do you test AI used for credit or fraud decisions?

We threat-model the model and its data, test for manipulation, bias-inducing poisoning and data leakage, and assess human-oversight and explainability controls expected by financial regulators.

How does AI red-teaming differ from normal penetration testing?

Traditional pen testing targets code and infrastructure; AI red-teaming additionally targets the model's behaviour via prompts, poisoned context and connected tools to make it leak data or act without authorisation. Mature programmes use both — we provide each and can combine them.