JFrog x AWS MLGovOps 2026: Balancing AI Innovation with Automated Guardrails and Responsible Governance

From AI-Ready to AI-Governed.

AI-accelerated development is no longer experimental - it's operational. But for today's CISO, it introduces an entirely new class of Day Zero risk. 

From polymorphic malware generated by LLMs to poisoned open-source models quietly entering enterprise repositories, the attack surface is expanding faster than traditional security controls can keep up. When AI agents can write, test and deploy code autonomously, perimeter-based security is no longer enough. 

The industry is responding with MLGovOps - a unified operating model where security, governance and compliance are embedded directly into the software supply chain, not bolted on after the fact. 

Join a select group of peer security leaders for this strategic virtual roundtable as we explore how enterprises are operationalizing MLGovOps to ensure that every line of code - human or machine generated - is trusted, traceable and compliant by design.

What You'll Explore

In this interactive, boardroom-style discussion, participants will examine how security organizations are evolving governance models to keep pace with autonomous development and AI-driven innovation.

The Rise of the Autonomous Supply Chain :

• How do you secure the identity perimeter when AI agents - not humans - are writing, testing and deploying software?
• What does trust look like in an environment with minimal human intervention?

Curation vs Chaos in AI Model Intake :

• Strategies for sanitizing AI models before they enter your environment.
• Preventing model poisoning, prompt injection, and malicious dependencies.
• How curated approaches (such as trusted AI catalogs) reduce systemic risk.

DevGovOps & Regulatory Readiness :

• Preparing for the 2026 regulatory landscape, including NIST 800-218 and the EU AI Act.
• Using automated evidence collection and trust-by-design architectures to simplify compliance.

The MLGovOps Maturity Framework : 

•  A CISO-level rubric to assess where your organization stands today.
• Key milestones on the journey from AI experimentation to governed AI at scale.

Managing "Shadow AI"

• Real-world tactics for reducing unsanctioned AI usage.
• Providing developers with "Golden Models" and approved pathways that enable innovation without sacrificing control.

Board-Ready Metrics for AI Risk

• How to quantify and communicate AI risk reduction.
• Measuring MTTD, policy enforcement and "Governance Uptime" in a way executives understand. 

Reserve your seat below

Attendance is limited to ensure a high-value, interactive discussion. Register now to join your peers and shape your MLGovOps strategy for 2026 and beyond.