Compliance

TL;DR for operators Enterprise LLM safety is often discussed as if the main question is whether the model has been trained to “behave”. That is the comforting version of the story. It is also too small. IBM’s OneShield paper argues for a different operating model: treat safety as a separate, model-agnostic guardrail layer that sits around the LLM, runs multiple specialised detectors in parallel, and then applies explicit policy decisions through a separate policy manager.1 In plain business terms, OneShield is less like teaching the model good manners and more like installing a configurable safety-control plane around every AI interaction. Glamorous? Not especially. Operationally useful? Very much so. ...

TL;DR for operators Financial foundation models are not one product category. They are three partly overlapping tool families, and confusing them is how firms end up buying a chatbot and expecting a risk engine. The paper reviewed here offers a useful taxonomy of financial foundation models across language, time-series, and visual-language systems, covering architectures, training methods, datasets, applications, and deployment challenges through June 2025.1 Its practical value is not that it declares a winner. It does something more useful: it shows which parts of financial AI are mature enough for workflow adoption, which are still research-shaped, and where the real bottlenecks sit. ...