Financial services sits under more overlapping regulatory regimes than almost any other vertical — DORA, MiFID II, SR 11-7, FCRA, and ECOA can all apply to a single AI system at once — while also carrying some of the highest-stakes adversarial risk anywhere in enterprise AI, since a successful attack on a fraud or trading model translates directly into financial loss, not just data exposure.
AI in financial services isn't one problem, it's five distinct ones running in parallel: cross-border compliance across multiple regulatory regimes, adversarial evasion of fraud detection, model risk governance for autonomous trading, vendor and operational resilience obligations across the AI supply chain, and fair lending compliance for underwriting. Each has its own regulator, its own failure mode, and its own technical control set — and most financial institutions are running AI systems that touch more than one of these areas simultaneously.
DORA, MiFID II, FCA, and SEC requirements for LLM systems — what each regime actually demands and where they overlap imperfectly for institutions operating across jurisdictions.
Read the full guide →Adversarial attacks and model evasion techniques specifically targeting fraud detection systems, and the defenses that hold up against an attacker actively trying to evade detection.
Read the full guide →SR 11-7 model risk management requirements and the autonomous execution controls that keep AI-driven trading systems inside acceptable risk boundaries.
Read the full guide →DORA's operational resilience and third-party risk obligations applied to the AI vendor stack — model providers, inference APIs, and orchestration platforms included.
Read the full guide →FCRA, ECOA, and insurance AI regulations, plus the model fairness and explainability requirements that determine whether an underwriting AI system is actually defensible.
Read the full guide →The HexTyx AI Security Assessment covers adversarial resilience, compliance readiness, and model governance in one scored report.