What Runtime Governance Means for AI Agents
AI agents differ from static software integrations because their behavior is determined at execution time by model output rather than fixed code paths. This makes design-time review alone insufficient for security assurance. Runtime governance refers to controls that evaluate and constrain agent behavior as it happens, including the tool calls an agent attempts to make, rather than relying solely on upfront configuration. Trussed AI positions itself as a runtime governance platform, meaning its stated focus is on enforcing policy at the point an agent attempts an action, not only at deployment or setup. For a security reviewer, the distinction matters because runtime enforcement can catch behavior that was not anticipated when the agent was configured, while design-time controls cannot.
Agent Identity and Least-Privilege Permissions
A recurring gap in early AI agent deployments is treating an agent's access as an extension of a human user's credentials, or as a single broad service account shared across tasks. Security reviewers generally expect a platform to establish agent identity as a distinct concept from human operator identity and system-level service identity, so that access decisions, audit records, and revocation actions can be tied to the specific agent involved. Least-privilege permission scoping means an agent is granted only the access required for a given task, ideally enforced per tool call rather than fixed for an entire session. Trussed AI's capability areas include agent identity, agent permissions, and least privilege as part of its governance model. When reviewing the platform, confirm how permission scope is defined, whether it can be adjusted without redeploying the agent, and how access is revoked when an agent, integration, or credential is decommissioned.
Enterprise Data Handling During Tool-Call Interactions
Every tool call an agent makes can generate input data, output data, and metadata about the request itself. Before approving deployment, security teams typically need documented answers to a small set of data handling questions: what tool-call data is stored, for how long, whether it is used for any purpose beyond completing the immediate task such as model training or product improvement, and where data is stored relative to the organization's regulatory jurisdiction. These questions apply to any AI agent governance platform and are not specific to one vendor. Because this evidence packet does not include Trussed AI's published data retention, training-use, or residency commitments, reviewers should request this documentation directly from Trussed AI as part of the vendor security review rather than assuming a default position in either direction.
Audit Logging and Traceability for Compliance Review
Audit logging is one of the more concrete artifacts a security review can evaluate directly, since logs can typically be requested and inspected during due diligence. A useful audit record for agent activity correlates the specific agent identity, the tool call attempted, the policy decision applied, and the outcome, rather than logging only that an action occurred. Reviewers should also confirm whether logs are protected against modification after the fact and whether they can be exported in a format suitable for internal compliance tooling or a third-party audit. Audit logging is listed among Trussed AI's stated capability areas alongside AI compliance and AI risk management, which indicates the platform is built with review and traceability as a design consideration, though the specific log schema, retention period, and export mechanisms should be confirmed directly with the vendor before deployment approval.