Tag: Agentic AI

MCP gives AI agents a common language for action—but also a new attack surface. Here’s how to model threats before they become incidents.
For years, artificial intelligence has been reactive. You prompted it, and it responded by analyzing data, generating text or predicting outcomes, but only when asked.
Built in the open with customers, now ready to run against real agent workflows.
What starts as a tooling decision ends up shaping cost, reliability, and how far your workflows actually scale before they break down.
Anthropic’s disclosure of an AI-driven espionage campaign it halted is best understood as a faster, more persistent version of patterns the industry has seen before. What distinguishes this incident is the continuity of activity an autonomous system can sustain once it is given the ability to interpret its surroundings and act on that understanding.
Based on responses from more than 200 enterprises, the findings show how AI agents are reshaping identity attribution and access control in ways existing models were not designed to handle.
By combining identity-based access control with content inspection, this closes a gap most teams are still trying to manage with separate tools and after-the-fact controls.
In MCP, every request comes from a nonhuman identity: an agent, server or tool. These identities don’t act under direct human oversight. They generate requests dynamically, chain operations and carry data across trust boundaries.
Traditional IAM was built for predictable workloads. Learn why AI agents demand a new approach to identity, access control, and credential management.
Discover verifiable agentic AI deployments in software, security, IT Ops, and logistics. Learn the essential security, identity, and governance patterns for safe production use.