Security and Compliance
Banco do Brasil needed a trustworthy way to scale AI across critical banking functions while satisfying regulatory, ethical and public-accountability obligations around transparency, control, explainability and risk management.
Reported impact
Banco do Brasil improved oversight, agility and institutional trust in AI, giving the bank a scalable way to validate that models meet ethics, security and performance standards before and after deployment.
Banco do Brasil worked with EY and IBM to operationalize a unified AI-governance model built for a heavily regulated public financial institution. EY designed the governance frameworks for generative AI lifecycle management and continuous AI monitoring, while IBM implemented the operating layer with IBM watsonx.governance. The platform automates supervision across the AI journey, from model design and evaluation through deployment into critical banking environments, and adds real-time monitoring, customized metrics, proactive alerts, traceability and version control for AI assets. Banco do Brasil is testing the combined framework in generative AI use cases so performance, compliance and risk requirements can be checked continuously rather than through sporadic manual review. This gives the bank a technical control plane for responsible AI adoption, turning governance from policy documents into a repeatable operating system for analytics and model oversight.
AI models and assets move through EY-defined lifecycle and monitoring frameworks, while watsonx.governance captures evaluations, metrics, alerts, explainability and version history so compliance and risk teams can supervise models continuously from design through deployment.
"AI doesn't replace humans-it unleashes people's potential by automating tasks. But that's only possible with the right structure, culture and, most importantly, governance."
"IBM provided the technological foundation through the IBM watsonx.governance toolkit"
"The platform supports real-time monitoring, proactive alerts and customized metrics"
"Traceability and version control of AI assets, promoting visibility and accountability"
"validating every model deployed meets rigorous standards for ethics, security and performance"
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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