Enterprise AI Governance Moves From Policy To Runtime Controls

Enterprise AI governance is moving beyond written policies as companies deploy autonomous systems that can access data, invoke software tools and act across business workflows.

The shift reflects a practical limitation of predeployment reviews: A policy can define what an agent is permitted to do, but organizations also need visibility into its actions while it operates across cloud services, employee devices and enterprise systems.

Runtime governance must cover an agent’s behavior during execution. Singulr applies controls to local AI tools on employee endpoints, including whether they can access the internet or send email.

The approach extends across cloud environments, software-as-a-service platforms and business workflows. Companies can use such controls to limit available actions, restrict access to approved systems and keep sensitive workloads within designated environments.

Infrastructure boundaries are also becoming a focus. Sri Viswanath, founder and CEO of Sycamore Labs, said some chief information officers want agents and large language model calls to remain inside their virtual private clouds.

“They don’t trust sending it outside because then we don’t know where it goes,” Viswanath said.

Companies are seeking stronger oversight while keeping pace with advances in AI. That requires organizations to update controls as agent capabilities and the systems they can access change.

The governance challenge is developing as AWS expands infrastructure and sandboxing tools for AI agents. Its products are designed to manage longer-running tasks and evaluate sequences of agent actions rather than treating every operation in isolation.

Dell Technologies’ 2026 AI Leadership Symposium program includes agentic AI, AI-powered PCs, storage and server architecture. Those topics reflect the wider infrastructure needs of enterprises deploying systems that can run multistep processes with limited human input.