As developer teams increasingly adopt agentic AI workflows using AWS Bedrock, Google Vertex, or custom-built LLM-powered systems, these first-party agents now interact directly with cloud resources, data, and applications, often without a human in the loop.
Most agents run with standing permissions, often far broader than any human user would ever receive. Traditional IAM and PAM weren’t built to govern entities that think and act independently.
In this on-demand webinar, Lalit Choda (Mr. NHI), Founder of Non-Human Identities Management Group, and Neha Duggal, Head of Product of P0 Security unpack how access policies must evolve to govern the implicit trust assumed by autonomous systems.
They’ll discuss how to best keep agents productive and contained before they access your sensitive resources.
Frequently asked questions
How should teams design access control for autonomous AI systems?
Teams design access control for autonomous AI systems by applying least-privilege, just-in-time authorization to every action instead of granting broad, standing permissions upfront.
What access controls do autonomous AI agents and systems require?
Autonomous AI agents and systems require access controls that scope every action to the specific task, since standing permissions create outsized risk once compromised.
How does secondary authorization scope autonomous system access to humans?
Secondary authorization scopes autonomous system access to humans by checking every agent action against the permissions of the accountable human user.
How does P0 Security build a control plane for autonomous, agentic access?
P0 Security builds a control plane for autonomous, agentic access that enforces runtime authorization across the entire agent action chain.