Applied Generative Ai Governance: A Viable Model Through Control Automation

Generative AI has the potential to revolutionize the banking industry with hyper-personalization and advanced chatbots. However, the technology also poses risks to trust, accuracy, fairness, intellectual property, and confidentiality that all need to be mitigated to ensure that the benefits of Generative AI are realized. In this article, we explore practical considerations to help mitigate these risks through the construction of a governance framework that has a focus on AI explainability, intellectual property protection, and minimizing model hallucination. We then derive a control framework against these key outcomes and present technology solutions we built around automating some of the key controls towards making our governance model viable. Finally, we explore what other institutions are doing in the field of generative AI governance and discuss new emerging roles needed to execute against the governance model. In terms of practical application, we recommend that financial institutions start small when it comes to generative AI governance and focus on defining a “minimum governance model” on a use case by use case basis to minimize the time and cost footprint of governance. We also recommend that governance is implemented very early in the solution lifecycle so that it is baked in at root-level; hence, reducing churn and rework of the solution when industrializing the use case within the financial institution.