Security Testing for Global Regulated Financial Services

Global financial services operate under some of the most demanding regulatory environments on earth. PCI DSS v4.0, DORA, GDPR, and SWIFT CSCF all reference technology risk — and all are moving toward requiring documented AI-specific security controls. Most fintech security programs haven’t caught up.

PCI DSS v4.0: A Forcing Function for AI Security Testing

PCI DSS v4.0 Requirement 6.3 mandates that security vulnerabilities be identified and managed using an industry-recognized vulnerability identification process. Requirement 6.3.2 specifically requires that all bespoke and custom software is reviewed for vulnerabilities. As AI-powered features become embedded in payment applications — fraud scoring, transaction routing, customer-facing chatbots with account access — they fall squarely within PCI scope.

The challenge is that traditional application penetration testing methodology does not cover prompt injection, tool poisoning, insecure output handling, or excessive agency — the vulnerability classes that matter for AI components. An annual web application pentest that ignores the LLM layer is not satisfying PCI DSS v4.0’s intent for AI-powered cardholder data environments.

pentest.qa’s LLM Penetration Testing service is structured to address PCI DSS v4.0 requirements for AI components, delivering findings reports that map to the PCI DSS control framework your QSA expects.

DORA: Operational Resilience Testing for EU Financial Entities

The EU Digital Operational Resilience Act came into force in January 2025, imposing new ICT risk management and testing obligations on EU financial entities and their critical ICT third-party providers. DORA’s threat-led penetration testing requirements (TLPT) extend to the full ICT environment — including AI systems used for credit risk assessment, AML compliance, algorithmic trading, and customer service automation.

DORA requires financial entities to test the resilience of their ICT systems under realistic adversarial conditions. An AI-powered compliance tool that has never been subjected to adversarial input testing is not DORA-compliant — regardless of the traditional security controls surrounding it.

For EU-regulated financial entities and their global counterparts, pentest.qa’s Agentic Red Team Exercise provides the structured adversarial testing methodology that DORA threat-led penetration testing requirements call for, with findings documentation formatted for DORA regulatory evidence packages.

AI Fraud Detection: The Integrity Risk Nobody Tests

AI fraud detection and AML screening are now core infrastructure for most financial institutions. These systems make binary decisions — flag or pass — based on model inference. The security question that almost never gets asked is: can an adversary manipulate the model’s decisions?

Two attack vectors are particularly relevant:

Training data poisoning — an adversary who can influence the data used to train or fine-tune a fraud detection model can embed systematic blind spots. Specific transaction patterns, merchant categories, or behavioral signatures can be made invisible to the model. The result is a fraud detection system that passes fraudulent transactions while appearing to function normally.

Adversarial input manipulation — without modifying the model itself, an adversary can craft inputs — transaction metadata, behavioral signals, account attributes — specifically designed to fall outside the model’s decision boundary and avoid a fraud flag. AI fraud models trained on historical fraud patterns can often be defeated by adversarial inputs crafted with knowledge of the training distribution.

pentest.qa’s AI Security Assessment includes integrity testing for AI decision systems — a capability that no traditional penetration testing firm offers.

Open Banking APIs and AI-Assisted Transaction Routing

Open banking mandates — PSD2 in Europe, equivalent frameworks globally — have created a rich ecosystem of APIs exposing account data, payment initiation, and transaction history. Many fintech platforms now layer AI onto these APIs: LLM-powered financial advisors, AI transaction categorization, AI-assisted spending analysis.

This combination creates new attack vectors:

BOLA (Broken Object Level Authorization) in open banking APIs allows adversaries to access other customers’ account data. When an LLM assistant is connected to these APIs with tool access, a successful BOLA exploit through the AI layer can escalate from unauthorized API access to automated data exfiltration across multiple accounts.

Prompt injection via financial data — when an LLM reads transaction descriptions, merchant names, or payment references as part of its context, adversaries can embed prompt injection payloads in those fields. A compromised merchant can inject instructions into the LLM processing their customers’ transaction data.

pentest.qa’s API Security Testing combines traditional API security assessment with LLM-specific attack scenarios — testing the full stack from REST API authorization to AI-layer injection.

The Enterprise Security Questionnaire Problem

B2B fintech companies — payment processors, embedded finance platforms, open banking providers — are increasingly receiving security questionnaires from enterprise clients with AI-specific sections. These questionnaires ask about:

• AI security testing methodology and coverage
• Penetration testing scope for AI components
• AI model integrity and supply chain security
• Agent privilege boundaries and data access controls

Without documented AI security testing, your sales team cannot answer these questions with evidence. A security questionnaire response that says “AI features are included in our annual penetration test” without specifics about methodology will fail enterprise security review.

pentest.qa’s Guardian Security Retainer provides ongoing AI security testing coverage and the continuous evidence stream that enterprise procurement teams require — turning security questionnaire completion from a bottleneck into a competitive advantage.