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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. AI governance refers to the practice of directing, managing and monitoring an organization’s AI activities.

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Automating Model Risk Compliance: Model Development

DataRobot Blog

Addressing the Key Mandates of a Modern Model Risk Management Framework (MRM) When Leveraging Machine Learning . The regulatory guidance presented in these documents laid the foundation for evaluating and managing model risk for financial institutions across the United States. To reference SR 11-7: .

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AI Governance: Break open the black box

IBM Big Data Hub

Today, AI presents an enormous opportunity to turn data into insights and actions, to amplify human capabilities, decrease risk and increase ROI by achieving break through innovations. Manual processes that introduce risk and make it hard to scale. Challenges around managing risk. It is an imperative.

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What is data governance? Best practices for managing data assets

CIO Business Intelligence

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. They must be accompanied by documentation to support compliance-based and operational auditing requirements.

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Integrating Data Governance and Enterprise Architecture

erwin

It documents your data assets from end to end for business understanding and clear data lineage with traceability. Data governance and EA also provide many of the same benefits of enterprise architecture or business process modeling projects: reducing risk, optimizing operations, and increasing the use of trusted data.

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What is BCBS 239 Compliance?

Octopai

BCBS 239 is a document published by that committee entitled, Principles for Effective Risk Data Aggregation and Risk Reporting. The document, first published in 2013, outlines best practices for global and domestic banks to identify, manage, and report risks, including credit, market, liquidity, and operational risks.

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Using Strategic Data Governance to Manage GDPR/CCPA Complexity

erwin

Modern, strategic data governance , which involves both IT and the business, enables organizations to plan and document how they will discover and understand their data within context, track its physical existence and lineage, and maximize its security, quality and value. Five Steps to GDPR/CCPA Compliance. Strengthen data security.