Agentic AI security, defined.

A control AI builders add to their agents that verifies every action before it runs, and the agent has no influence over the decision.

The Agent Security Context Graph is a security context graph for AI agents: everything relevant to a single agent action, across six dimensions and 29 signals, assembled outside the agent before the action runs, so the agent cannot edit it.

The placement for agent security beside the agent, in the execution path: not inside the agent, and not at the gateway.

The set of responses Ackuity chooses from for each agent action, scaled to severity and confidence: Allow, Constrain, Human in the loop, Block and Terminate.

Detection that combines rules, behavioral baselines and small language models, correlated across steps, to judge an agent action in real time.

A deployment option in which Ackuity holds the agent’s tokens and runs each approved action on its behalf, so the agent never handles the credentials.

A control that stops an AI agent before it causes harm. Most kill switches stop the whole agent; a control switch acts on each action and keeps termination for catastrophic cases.

An isolated environment, such as a container or virtual machine, that limits what an agent process can reach. NVIDIA OpenShell is one example.

Ackuity’s open source component that runs in the agent’s pod and checks every action, with traffic rerouted by an init container and no change to agent code.