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Building the agentic control plane for responsible AI
While every enterprise is talking about AI, a much smaller group is building the infrastructure that will determine whether it succeeds.
As AI agents operate across enterprise systems, organizations need more than models and copilots. They need trusted data, governance that scales, and security designed for autonomous decision-making. They need an agentic control plane. Join us for an executive-level discussion on the technologies, architectures, and governance models defining enterprise AI infrastructure and what's next.
See across every AI application, model, and agent operating in your enterprise.
Establish policy across data, identities, and models as adoption grows.
Guardrail agents acting across enterprise systems and non-human identities.
Prepare, curate, and govern the data every AI initiative depends on.
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Agentic AI is redefining responsible AI, shifting it from static governance frameworks to real-time oversight of increasingly autonomous systems. As AI agents act across workflows, systems, and decisions, technology, security, and risk leaders must ensure they are explainable, accountable, and continuously observable in operation, not just in design.
As AI agents begin making decisions, accessing systems, and taking action across the enterprise, identity becomes more than authentication. This session explores how organizations can manage agent identities, delegated permissions, machine-to-machine trust, and least privilege in a world where software is becoming an active participant in the business.
Knowing whether an AI can access data is no longer enough. This discussion looks at how organizations are using context, purpose, risk, and business intent to govern AI behavior—ensuring agents act appropriately, not just permissibly.