SAILLENT: Policy Enforcement for AI Agents

SAILLENT is a runtime policy enforcement layer for AI systems. The vendor describes a deployment that intercepts covered requests or actions before they reach a model, tool or external system. It is intended for teams that want a place to apply policies to actions taken by their agents.

What can a policy cover?

The provider lists model use, tools, access to data and user roles as policy inputs. Its home page also describes sensitive-data redaction before requests reach external models. For a team evaluating a deployment, the useful first question is which requests actually traverse the enforcement point. A policy only has an opportunity to act on traffic routed through the covered integration.

Where does it run?

The pricing page describes self-hosted enforcement and a SaaS console for standard options. Other deployments can be scoped for a customer-controlled environment, including a VPC or on-premises setup where supported. Its documentation describes a Linux runtime managed by systemd. Work through the supported setup for the particular environment and verify an allowed action and a blocked action before treating a policy as effective.

The provider also says it supports covered MCP interactions, subject to the integration and deployment. That is a scope statement: a specific MCP architecture still needs a technical review.

How does pricing measure use?

Pricing is based on protected-action capacity, protected deployment nodes and integration scope, among other requirements. A node refers to an enforcement deployment rather than an individual user, model or agent. The vendor also says SAILLENT is an additional control alongside identity, network protections and model safeguards. Use the scope of the planned integration to estimate capacity and check the controls that need to operate independently.