This webinar addressed the critical need for governance frameworks as enterprises deploy autonomous AI agents capable of taking actions across connected systems. With real-world vulnerabilities already emerging (like Asana's recent MCP server breach), organizations must implement proper controls before widespread adoption to avoid costly security incidents and operational disruptions.

👉 Download the full slide deck to revisit key frameworks, controls, and live demo takeaways.

As enterprises begin deploying autonomous AI agents, new governance challenges are emerging fast. From agent-to-agent communication risks to unclear accountability, the cost of poor oversight is only getting higher.

In this exclusive webinar replay, ModelOp CTO Jim Olsen explores how to bring safe, scalable governance to agentic AI—sharing practical strategies and live tooling examples, including:

• Why agent-to-agent interactions are a hidden vector for data leakage and risk
• The autonomy dilemma: how much control is too much—or not enough
• How enterprises are using Agent Service and MCP protocols to enforce guardrails

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Want more expert insights like this? Register for the Good Decisions webinar series to stay ahead of the curve with 30-minute sessions designed for enterprise AI leaders.

Agentic AI is here. The risks are real—and avoidable.

Organizations are experiencing a massive imbalance between AI ambition and execution. While there's an overwhelming number of proposed AI use cases and initiatives, very few actually make it to production. Most remain stuck in the proposal phase, creating a significant bottleneck that prevents companies from realizing AI's value at scale.

What’s holding enterprise AI back? The data tells a clear story.

The 2025 AI Governance Benchmark Report revealed a growing commitment to responsible AI—with 36% of enterprises budgeting over $1M for governance software. But investment alone isn’t enough. In this exclusive webinar replay, ModelOp’s experts dig into the report’s most surprising—and actionable—findings, including:

• Why fragmented systems and manual processes remain the #1 blockers to governance adoption
• The critical training gap that’s slowing AI scale across industries
• How top-performing organizations are aligning governance with business value

Download the full slide deck to review the stats, charts, and takeaways shared during the session.

Want more expert insights like this? Register for the Good Decisions webinar series to stay ahead of the curve with monthly 30-minute sessions designed for enterprise AI leaders.

Download the slide deck.

Model cards are a breakthrough for AI transparency—offering critical insights into how models were trained, their intended use, and their limitations. But transparency alone isn’t enough. To drive real-world results in healthcare, organizations need to move beyond reading model cards to actively using them for governance, compliance, and risk management.

Is your organization ready to turn model card transparency into operational advantage?

Join Dave Trier, VP of Product at ModelOp, and Amanpreet Kaur, Ph.D., Implementation Engineer, for a 30-minute session focused on bridging the gap between documentation and action. You’ll learn how leading healthcare organizations are importing model cards, integrating them into compliance workflows, and automating governance processes to scale AI safely and efficiently. See how to turn transparency into a practical, repeatable process that accelerates responsible AI deployment.

🔍 What You’ll Learn:

• How to import and evaluate model cards—including those from CHAI and other healthcare standards bodies
• Why transparency isn’t enough—and how to use model cards in real governance workflows
• How to centralize model tracking for compliance, audits, and risk reporting
• Ways to automate governance processes without slowing down AI adoption
• What leading healthcare teams are doing to scale AI responsibly and efficiently

Register for the series.

Just like nutrition labels transformed food transparency by providing standardized information about ingredients and nutrients, model cards serve as "nutrition labels" for AI systems. They provide essential transparency into what AI models contain, how they work, their intended use cases, limitations, and potential risks - enabling stakeholders to make informed decisions about AI deployment.