Enhancing the Effectiveness of AI Governance Committees

May 13, 2024

Organizations are increasingly deploying artificial intelligence (AI) systems to drive innovation and gain competitive advantages. Effective AI governance is crucial for ensuring these technologies are used ethically, comply with regulations, and align with organizational values and goals. However, as the use of AI and AI regulations become more pervasive, so does the complexity of managing these technologies responsibly.

Given this increased complexity, many organizations are setting up AI Governance Committees. These committees – often centralized – play a pivotal role in orchestrating the organization’s AI strategy, tasked with overseeing the deployment, risk management, and operation of AI systems. However, many committees face challenges due to lack of AI competencies and tools tailored to manage these specific responsibilities efficiently.

AI Governance Committees must be empowered to leverage software solutions like Trustible oversee all levels of AI Governance (not just governance of the models or AI systems themselves). These include:

This white paper discusses how Trustible can transform AI governance committees from a strategic oversight body to an efficient operational powerhouse. We will explore Trustible’s alignment with the needs of these committees, detail its benefits, and provide actionable strategies for successful implementation. These include:

Develop AI Policies – Establish AI usage standards & internal rules
Inventory AI Use Cases – Centralize all use cases, models, data, and vendors
Identify and Mitigate AI Risks & Harms – Continuously assess and mitigate risks & harms
Comply at Scale – Ensure adherence to regulations & standards

Download the Whitepaper

AI Governance Meets AI Insurance: How Trustible and Armilla Are Advancing AI Risk Management

As enterprises race to deploy AI across critical operations, especially in highly-regulated sectors like finance, healthcare, telecom, and manufacturing, they face a double-edged sword. AI promises unprecedented efficiency and insights, but it also introduces complex risks and uncertainties. Nearly 59% of large enterprises are already working with AI and planning to increase investment, yet only about 42% have actually deployed AI at scale. At the same time, incidents of AI failures and misuse are mounting; the Stanford AI Index noted a 26-fold increase in AI incidents since 2012, with over 140 AI-related lawsuits already pending in U.S. courts. These statistics underscore a growing reality: while AI’s presence in the enterprise is accelerating, so too are the risks and scrutiny around its use.

Why AI Governance is the Next Generation of Model Risk Management

For decades, Model Risk Management (MRM) has been a cornerstone of financial services risk practices. In banking and insurance, model risk frameworks were designed to control the risks of internally built, rule-based, or statistical models such as credit risk models, actuarial pricing models, or stress testing frameworks. These practices have served regulators and institutions well, providing structured processes for validation, monitoring, and documentation.