Company’s Recent 3.2 Release of Its AI Governance Platform Empowers Leaders to Safeguard Large Language Models (LLMs) and Generative AI without Stifling Innovation
(CHICAGO, August 30, 2023) — ModelOp, the world’s leading AI Governance platform, is pleased to announce its annual AI Governance Leadership Summit. The virtual summit will be held on Tuesday, September 26; registrants are advised to reserve admission early.
This event, convening a community of AI, governance, risk, compliance, data, and security leaders from Fortune 500 companies and leading universities, will explore trends, strategies, and best practices on how to approach and implement AI Governance while navigating the challenges and opportunities related to generative AI. ModelOp’s virtual summit includes a keynote, fireside chat, case study panel, and masterclass featuring the leading thinkers and innovators in AI Governance, including Kimberly Weiland, Vice President of Enterprise Operations at the Financial Industry Regulatory Authority (FINRA), and Agus Sudjianto, Executive Vice President, Head of Corporate Model Risk at Wells Fargo (NYSE: WFC). Speaker information is available on the summit website, with more to be revealed by ModelOp in the lead–up to the event.
“AI – especially generative AI – and its opportunities, use cases, potential regulations, decision-making capabilities, and overall impact to the bottom line is a board-level conversation,” said Pete Foley, co-founder and CEO of ModelOp. “In our annual AI Governance Report, enterprise AI leaders revealed that just 5% of them have full visibility into production models across the enterprise, and 52% reported most models are operated in silos with limited oversight or accountability. While generative AI and Large Language Models (LLMs) offer enterprises transformational opportunities, they also need to be carefully tracked from a financial, data science, legal, security, and IT perspective, as there are many moving pieces that can introduce risk to the organization. This is where ModelOp plays a crucial role, delivering comprehensive, real-time insights into all aspects of all models, including LLMs. This provides a true “portfolio view” of an enterprise’s AI investments. We’re the only commercially available software that helps global enterprises govern and scale their AI initiatives.”
The AI Governance Leadership Summit comes on the heels of the company’s ModelOp Center version 3.2 release, the first commercially available software that enables enterprises to safeguard LLMs and generative AI without stifling innovation. ModelOp’s 3.2 version builds on its existing governance support for all models, including regressions, Excel, and vendor models, and adds new capabilities that manage LLM ensembles, track value, chart and visualize risks, provide universal monitoring and enforce governance controls through model life-cycle automation.
Generative AI increases the scrutiny of all analytics models from corporate boards to team leads. Academic institutions, governments, technology vendors, and concerned citizens and workers continue unprecedented support for regulations that establish guardrails for the safe and humane use of AI, including the EU Artificial Intelligence Act, US NIST AI-Risk Management Framework and Canadian OSFI E-23 Extensions for AI.
ModelOp’s 3.2 release helps enforce AI Governance and provide verification of adherence to AI guidelines and regulations via a single source of truth and transparency, providing guardrails for all models, regardless of the type or methodology. Furthermore, the 3.2 release provides enterprises with the capabilities to inventory, manage assets, and test and monitor generative AI, including support for:
- Classifying use cases, models, and ensembles as LLMs
- Tracking prompt templates, guardrails, etc.
- Automating testing, monitoring, and documentation generation
These capabilities empower organizations to be proactive; to inventory, control and report on LLMs across business units; and ultimately to help organizations innovate and scale with generative AI while mitigating financial, legal, and brand risks associated with the rapidly evolving technology and regulatory landscape.
