Why enterprise AI agents need cryptographic workload identity, delegated authorization, scope attenuation, and proof-of-possession instead of generic service accounts.
AI agents are getting increasingly autonomous.
They can read data, call APIs, invoke MCP tools, create infrastructure, execute workflows, interact with enterprise applications, and even delegate tasks to other agents.
But there is a fundamental question that many enterprise architectures still struggle to answer:
Who is the agent?
Not what model is it running?
AI agents can access data, trigger workflows, deploy code, and interact with critical business systems, often with little…
We must treat agent identity as a first-class infrastructure requirement.
The identity problem behind AI agents An application that calls an API for a user normally...
Enterprise identity solved many of the problems AI agents are creating today. Here are five lessons worth borrowing.
Snowflake's Data-Model-Agent framework secures the agentic enterprise with prompt injection defense, zero-copy governance and…
AI agents can access data, trigger workflows, and take action across enterprise systems. Token Security explains why governing…
