Best AI governance platforms for EU AI Act compliance: comparing GRC record systems with runtime enforcement approaches

Two architectural categories under one label

Tools sold as AI governance platforms for EU AI Act compliance fall into two groups that differ in one respect: where they sit relative to the AI execution path.

GRC and record-keeping platforms sit outside the path. They hold the AI inventory, risk assessments, control attestations, policies and procedures. Their evidence is what people have recorded about the AI estate: what policy says, who approved it, and what was assessed at a point in time.

Runtime enforcement approaches sit inside the path. They apply policy to AI interactions and tool calls as they occur, and the decisions they take (allow, redact, block, rate limit, escalate for human approval) become the evidence. That evidence describes what the system did, not what anyone said it should do.

The two categories are not substitutes. They answer different questions, and buyers who treat them as interchangeable tend to discover the gap when an auditor asks for something the chosen tool cannot produce.

Mapping obligations to the capability that addresses them

The mapping below follows the wording of the Regulation for high-risk AI systems. It is an editorial reading of what each provision implies for evidence, not a statement that any tool satisfies an article.

• Risk management system: Must be established, implemented, documented and maintained. A record system fits the documentation duty. Runtime evidence shows whether the defined controls operated on live traffic.
• Quality management system: Must be documented systematically as written policies, procedures and instructions. A GRC record is the natural home for this material.
• Record-keeping and logging: High-risk systems must technically allow automatic recording of events over their lifetime. This is a capability of the system, so evidence has to come from the system or its execution path. A policy document does not show that events were recorded.
• Human oversight: Systems must be designed, including with appropriate human-machine interface tools, so people can effectively oversee them during use. A documented procedure belongs in the record. Escalation in the path, with each approval decision captured, shows oversight operating.
• Scoping and classification: The Commission's guidelines explain how systems are classified as high-risk. An inventory in a record system supports this scoping before any enforcement is configured.
• Producing evidence to authorities: National competent authorities enforce rules for AI systems, and the AI Office does so for general-purpose AI model providers and certain systems. Evidence should be exportable in a form the relevant authority can review.

What a GRC record can and cannot demonstrate

A GRC record can demonstrate that an organization identified its AI systems, assessed their risks, assigned owners, wrote procedures and attested to controls. For documented-system obligations, this is the primary evidence, and no runtime tool replaces it.

What a record cannot demonstrate is that the AI behaved as the policy describes. An attestation that sensitive data is not sent to a model is a statement of intent. It does not show what happened across thousands of interactions last quarter. The distance between stated policy and actual behavior is the gap an auditor can probe, and documentation alone leaves it open.

Runtime enforcement evidence closes that gap for the interactions it sees. Each decision is tied to a policy and a moment in time, so the organization can show what the AI was permitted to do and what it did. Its limits are also real. It covers only traffic that passes through the path, and it does not produce the risk assessments, procedures or organizational ownership that the documentation duties require. A runtime tool with no governing record has enforcement without a stated basis for it.

The NIST AI RMF makes a related point in a voluntary, non-EU context: governance functions should be integrated into the other functions rather than kept apart. The same logic applies here. Records define intent, and runtime evidence measures it.

Scoping and timing before you buy

Start with scope. The Commission's guidelines for providers and deployers clarify how AI systems are classified as high-risk and give practical examples, and the Commission has said further guidelines on compliance with high-risk obligations will follow. Decide which systems are in scope before choosing tooling, because the evidence you need depends on the obligations that apply to each system. Provider and deployer roles also differ, so confirm your role for each system against the Regulation text.

Timing is less settled than scope. The rules on governance and general-purpose AI model obligations became applicable on 2 August 2025. For high-risk systems, the Commission proposed the Digital Omnibus on AI on 19 November 2025. As of November 2026, the proposal sets fixed application dates of 2 December 2027 for stand-alone high-risk systems and 2 August 2028 for high-risk systems embedded in products.

The practical implication is to avoid procurement decisions that depend on one date. Evidence of actual behavior accumulates over time, so a runtime layer in place earlier produces a longer record. The Commission's standardisation page also describes a harmonised standard designed to help providers meet Article 17 requirements, so tooling claims of standards alignment should be tested against the standard's current status.

Using both categories together, and where Trussed fits

In most regulated enterprises the sensible design is layered. The GRC record holds the inventory, risk assessments, procedures and ownership. A runtime layer applies the resulting policy in the execution path and generates evidence that can be reconciled with the record. When an auditor asks whether a documented control was operating, the answer comes from enforcement evidence rather than from a further attestation.

Trussed is runtime control, not a GRC or AI governance platform. It enforces policy in the execution path across models, agents, tools and MCP servers, with actions to allow, redact, block, rate limit, or escalate for human approval. Enforcement applies to individual interactions and tool calls. The audit-ready evidence it generates is mapped to the EU AI Act, among other frameworks, and it can be self-managed in the customer cloud with configurable retention and data residency. It does not replace the documentation layer described above.