AI security review for financial services

Financial services AI operates under layers of regulatory scrutiny that other sectors don't face. The EU AI Act classifies several financial AI applications as high-risk (credit scoring, insurance risk assessment, employment decisions). DORA requires ICT risk management that covers AI systems. The FCA and ECB have both published expectations on AI governance and model risk management.

The common thread: regulators expect documented evidence that you assessed AI risks, implemented proportionate controls, and made a defensible decision before the system handled real customers or real money.

Financial services AI faces attack surfaces that general-purpose threat models miss:

• Prompt injection in customer-facing AI. A customer-facing chatbot with access to account data and transaction history is a high-value target for prompt injection attacks that attempt to exfiltrate account information or manipulate transactions.
• Model manipulation for credit decisions. LLM-assisted credit scoring or risk assessment can be manipulated through adversarial inputs designed to produce favourable decisions for fraudulent applicants.
• Data leakage through RAG over regulated data. A RAG system over customer financial records, compliance documents, or proprietary trading data requires strict retrieval authorization — chunking and embedding do not respect document-level access controls by default.
• Agentic AI with payment or transaction tool access. An agent that can initiate transfers, modify account settings, or access trading systems requires specific blast-radius controls and approval boundaries that standard code review misses.

Drel maps every assessment to the frameworks most relevant to financial services:

• EU AI Act Article 9 — risk management evidence for high-risk AI systems
• ISO/IEC 42001 — AI management system evidence for clauses 6 and 8
• OWASP LLM Top 10 — controls for LLM application risks
• OWASP Agentic Top 10 — controls for agentic AI systems with tool access
• NIST AI RMF — govern, map, measure and manage AI risk

Drel's clearance record is designed to integrate with existing model risk management (MRM) frameworks. The structured output — threat model, control plan, evidence gaps, disposition, and re-assessment triggers — maps to the validation and ongoing monitoring requirements of SR 11-7 and equivalent guidance.

The re-assessment trigger system is particularly important for regulated AI: when a model version changes, training data is updated, or the system is extended to a new use case, the original clearance record automatically flags for re-review. You know when your AI governance records are stale.