This working paper introduces Enterprise AI Risk Orchestration, a vendor-neutral reference architecture designed to improve cybersecurity and fraud prevention across financial institutions through AI-driven cross-domain risk correlation. The paper argues that one of the principal weaknesses of current financial security environments is the structural fragmentation of fraud prevention, identity management, cybersecurity, authentication, AML, and transaction monitoring systems. Although these domains individually generate valuable intelligence, they frequently operate in isolation, limiting organizations’ ability to identify sophisticated attacks that span multiple security domains. To address this challenge, the paper proposes an enterprise orchestration layer capable of continuously correlating heterogeneous risk signals, generating dynamic contextual intelligence, and supporting coordinated, real-time security decisions without replacing existing security platforms. The proposed architecture is aligned with recognized cybersecurity principles, including NIST SP 800-207 (Zero Trust Architecture), the NIST AI Risk Management Framework, and the NIST Cybersecurity Framework 2.0, while complementing existing fraud prevention, IAM, SIEM, EDR, and payment security ecosystems. Rather than presenting a commercial product, implementation guide, or proprietary framework, this publication is intended as a technical discussion paper that explores a possible architectural approach to strengthening operational resilience, reducing enterprise-wide security blind spots, and improving decision orchestration across complex financial environments. The paper is intended for cybersecurity architects, fraud prevention specialists, enterprise architects, financial institutions, technology providers, researchers, and policymakers interested in AI-driven cybersecurity, digital trust, and critical financial infrastructure protection.
Paulo Helou Netto (Sat,) studied this question.