Cloud & AI in Banking: Navigating Residency, Risks, and Automation | Microsoft & Red Hat
🔑 Cloud, AI, and Automation: Navigating the New Regulatory Frontier | Open Source in Finance Podcast🚀 Explore insights from the #FINOS community – the center of open source innovation in financial services.🌐 More about FINOS: https://www.finos.org/ 🤖 Explore the FINOS AI Governance Framework: https://air-governance-framework.finos.org/ ☁️ Learn about Common Cloud Controls (CCC): https://ccc.finos.org/ 📧 Join our newsletter: https://www.finos.org/sign-up 📥 Download the State of Open Source in Financial Services report: https://www.finos.org/state-of-open-source-in-financial-services 🎙️ Listen to our Open Source in Finance Podcast: https://www.youtube.com/@FINOS/podcasts 🗣️ Attend the next Open Source in Finance Forum: https://hubs.ly/Q03z9D9D0 LinkedIn: https://www.linkedin.com/company/finosfoundationIn this episode, Grizz Griswold (FINOS) sits down with Allison Nachtigal (VP, Azure Chief Product Officer, Microsoft) and Aric Rosenbaum (Chief Technologist, Red Hat) to discuss the complexities of deploying cloud and AI in the most highly regulated industry in the world.🏛️ Why Cloud is Different for BanksThe panelists establish that for financial services, the cloud isn't just a technology shift—it's a massive compliance and "headline risk" challenge.Shared Responsibility: Moving to the cloud requires banks to demonstrate compliance for third-party infrastructure they do not directly control.Sovereignty & Residency: Meeting strict data residency requirements (e.g., keeping data within Swiss or EU borders) remains a primary enabler—and hurdle—for global Azure and Red Hat deployments.The "Toil" of Compliance: Implementation is often slowed by manual verification. Allison and Aric highlight the need for Common Cloud Controls (CCC) to standardize what "good" looks like across providers.🤖 The "Order of Magnitude" Complexity of AIWhile machine learning has existed for decades, LLMs introduce a fundamental conflict for auditors: Non-determinism.The Deterministic Gap: Banks require "same input, same output." LLMs, by nature, are unpredictable.Synthetic Data: Because private data cannot always leave specific regions for model training, banks are increasingly relying on synthetic data to bridge the gap.AI Governance: Aric and Allison discuss the FINOS AI Governance Framework (AIGF) as a way to crowdsource risks and automate mitigations, ensuring AI isn't just an "experiment" but a production-ready tool.⚙️ Automation as the Compliance EngineTo stay off the headlines, security must be reproducible 100% of the time.Ansible for Security: Aric emphasizes using tools like Ansible to automate security playbooks. This ensures that as Red Hat or Microsoft releases new versions, the controls remain intact across on-prem and multi-cloud environments.Crowdsourced Risks: By collaborating through FINOS, competitors like Microsoft and Red Hat work with banks to define a common set of controls, reducing the regulatory burden for everyone.🔮 Looking Ahead to 2026What does the next year hold for the industry?Efficiency Shift: Moving from "insane" energy-hungry models to smaller, high-efficiency models and autonomous agents.Quantum Uplift: A massive focus on post-quantum cryptography will likely "slow down" some migrations to ensure security foundations are rebuilt for the quantum era.Agentic Modernization: The convergence of digital assets, AI agents, and cloud will drive the next multi-year wave of modernization.The takeaway: Cloud and AI are no longer optional for financial services, but their success depends on "trust through automation." By leveraging open standards like the FINOS Common Cloud Controls and AI Governance Framework, institutions can move away from manual "toil" and build safe, non-deterministic systems that satisfy both auditors and customers.#FINOS #OpenSource #CloudSecurity #Azure #RedHat #AI #AIGovernance #FinTech #Ansible #DigitalSovereignty #QuantumComputing #Podcast
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