TenetGraph derives each agent's least-privilege policies from the agent itself, evaluates them against attacks and over-permission risks before deployment, and enforces them on every action.
Your auditor sees the decision and the policy it was evaluated against, not a log to reconstruct.
Your product acts on its own now: issuing refunds, updating accounts, moving money through agentic workflows. One injected input can reach everything those workflows touch. TenetGraph evaluates every action against the control policies it generated for the agent, keeping it within its intended purpose, before anything executes.
Employees are running agents with their own access to email, files, and systems of record, and no one has defined what those agents are authorized to do. TenetGraph derives and enforces least-privilege policies for each agent, and surfaces the agents no one registered.
The control policies come from the agent itself: its code, prompts, and tool definitions establish the least privilege it needs.
An adversarial agent attacks the boundary before deployment, injection, tool misuse, out-of-purpose actions. Findings harden the policies.
Every action is evaluated against the control policies at the decision point, before it executes. Deterministic, not probabilistic.
Every allow and deny is captured as a decision record citing the policy that produced it.
What is this agent authorized to do? Each layer below does real work, and none of them answers it.
Defines what a specific agent is authorized to do, denies what falls outside it, and proves every decision against the control policy that made it.
Agentic features act with production access, and model guardrails do not bound what they can do. Assume some injection attempts succeed: a manipulated agent will try to act outside its purpose. TenetGraph derives each agent's least-privilege policy from its own code and prompts, evaluates that boundary against attack before release, and denies out-of-policy actions in production.
Security review signs off on evidence: the control policies, the adversarial evaluation results, and a decision record for every action.
For product security teams →Employees run AI agents with their own access to email, files, and systems of record, and anything those agents read can carry an attacker's instructions. TenetGraph derives least-privilege policies for each agent from the agent itself, enforces them on every action, and surfaces the agents no one registered.
Every agent working with employee access gets a defined, evaluated, enforced boundary.
For security teams →Each action is evaluated against a specific policy, allowed or denied, and captured as a decision record. The audit answer is evidence, not a log to reconstruct.
The boundary comes from the agent's own definition and is re-derived when the agent changes. No one maintains policy sets by hand.
The boundary is evaluated against attack before deployment and enforced at the decision point when the agent acts. Deterministic policy evaluation, not a model's judgment in the moment.
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