This chapter describes a five-step control loop technology model that can be used to align technology adoption with a firm’s ethical culture and regulatory environment. The chapter applies the model to the adoption of Artificial Intelligence and Machine Learning, Regulatory Technology and Supervisory Technology, Distributed Ledger Technology and Tokenization, Open Banking with Application Programming Interfaces, and Cloud Computing with Software-as-a-Service platforms. For each domain, the chapter maps typical hazards, including cognitive, behavioral, and psychological biases, model drift, alert fatigue, smart contract defects, data misuse, concentration risk, and resilience gaps to concrete controls, continuous monitoring, auditable proof, and the adoption process. A risk–control–evidence matrix and a six-step technology adoption checklist are presented to help firms incorporate model registers and validation packs, consent lifecycle management for data sharing, impact tolerances and exercises, third-party registers, and tokenization safeguards into daily practice. The approach described in this chapter is benchmarked to the Guidance on Model Risk Management by the Board of Governors of the Federal Reserve System (SR 11-7), the National Institute of Standards and Technology Artificial Intelligence Risk Management Framework, the European Union Artificial Intelligence Act, the European Union Digital Operational Resilience Act, the United Kingdom Consumer Duty, and the Business Continuity Management Booklet of the Federal Financial Institutions Examination Council.