Two Layers of AI Governance
Enterprise teams evaluating AI governance platforms often start by comparing tools that appear to solve the same problem but actually instrument different parts of the AI system lifecycle. Hyperproof belongs to a category of control automation platforms built around GRC workflows: mapping policies to controls, scheduling evidence collection, and producing audit-ready documentation. Trussed operates as a runtime enforcement layer, applying policy directly to AI agent actions and tool calls as they execute in production. Understanding which layer each platform actually instruments, rather than assuming one displaces the other, is the first step in building an accurate AI governance program.
Control Automation: Policy, Evidence, Audit Readiness
Control automation platforms like Hyperproof center on a documentation and attestation cycle. They track which policies exist, assign control ownership, schedule evidence collection from connected systems, and generate reports that auditors can review. This model works well for demonstrating that governance processes exist and are followed on a defined cadence. It does not, by design, observe what an AI agent does at the moment it calls a tool or an external API. The evidence produced describes intended state: what policy says should happen, who is accountable for it, and whether periodic checks were completed on schedule. That evidence is necessary for audit readiness, but it does not confirm that the documented policy was actually enforced against live agent behavior between review cycles.
Runtime Enforcement: Governing Agent Actions in Production
Runtime enforcement, the model Trussed AI is built around, sits in the execution path of an AI agent rather than in a periodic review cycle. It evaluates individual tool calls, API actions, and agent-to-agent interactions against policy before allowing, blocking, or modifying them. In practice this involves governing agent identity and permissions, enforcing least privilege on what an agent is authorized to touch, requiring approval workflows for sensitive tool use, and logging actions as they occur so there is an audit trail of what actually happened rather than what was intended. Where control automation asks whether a policy exists, runtime enforcement asks whether a specific action was permitted at the moment it took place.
Control Automation vs Runtime Enforcement
The two layers can be summarized by the state each one tracks and the point in time at which it evaluates that state:
| Dimension | Control Automation (Hyperproof) | Runtime Enforcement (Trussed) |
|---|---|---|
| State tracked | Control state: what policy documents say should happen, tracked through attestations and periodic review. | Execution state: what an AI agent actually does when it calls a tool, API, or another agent in production. |
| Evaluation timing | Documentation cycle: policy and evidence are evaluated on a scheduled cadence. | Execution path: individual agent actions are evaluated at the moment they occur. |
Where the Gap Appears in Practice
The distinction matters because autonomous AI agents can chain multiple tool calls and complete a sequence of actions between scheduled review cycles, with no human in the loop. A control automation platform can show that a policy restricting a certain type of data access exists and was last reviewed on schedule, without confirming that no agent bypassed it last week. This creates a structural gap between control state, what policy documents say should happen, and execution state, what agents actually did. Neither category is wrong for its intended purpose. The risk appears when an organization treats evidence from one layer as an answer to a question that only the other layer can address, for example assuming a clean audit report proves no unauthorized agent action occurred in production.