Does Credo AI enforce policy on AI traffic?

Credo AI governs at the program level: inventory, policy mapping, risk assessment, and documentation. It does not sit on the request path and cannot block or allow an individual model call. Runtime enforcement is a separate layer that produces the evidence a governance registry references.

Can Credo AI produce EU AI Act Article 12 logs?

Credo AI records that a logging control is claimed for a given system. The actual per-decision logs required for traceability are generated on the request path by whatever component sees the traffic. A governance platform assembles and presents evidence; it does not create the underlying decision records.

Do I need both a governance platform and an enforcement layer?

For most regulated programs, yes. The governance platform maintains the inventory and the documentation. The enforcement layer runs the control and produces the per-decision evidence. They occupy different layers and reference each other rather than competing.

Is DeepInspect a governance platform?

DeepInspect is not a governance platform. It enforces identity-bound policy on live AI traffic and writes audit records, rather than maintaining a program-wide AI registry or mapping systems to frameworks. It supplies evidence into a governance platform such as Credo AI rather than replacing it.

How do the two connect in practice?

A governance registry entry for a high-risk system cites its runtime controls and points to the evidence. DeepInspect is the runtime control and the evidence source: it enforces the policy and exports the per-decision records the registry entry depends on.

DeepInspect vs Credo AI: Governance Documentation and Runtime Enforcement

Credo AI operates at the program level. It holds an inventory of the AI systems an organization runs, maps each one to policy packs for the EU AI Act, NIST AI RMF, ISO 42001, and SOC 2, tracks risk assessments, and produces the documentation a governance team hands to auditors and executives. Founded in 2020, it was named a Leader in Forrester's AI governance Wave, and its home ground is helping a cross-functional team prove a program is under control.

That is governance work, and it is real work. Someone has to know which models are in production, which are high-risk under Annex III, and whether each has an owner, a risk rating, and a control set on file.

Teams comparing Credo AI to DeepInspect are usually deciding whether a governance platform covers their runtime obligations too. It does not, and the reason is structural. I want to walk what each one owns, then show where the handoff has to happen.

TL;DR

Credo AI is a governance system of record: AI inventory, policy mapping, risk assessment, and audit-ready documentation across frameworks like the EU AI Act and NIST AI RMF. DeepInspect is a runtime enforcement layer: it decides whether each model call proceeds based on identity and data, and writes a per-decision audit record. One documents that controls exist. The other runs a control and produces its evidence.

Credo AI: registry, policy packs, and documentation

Credo AI's core is the registry. Every AI system gets an entry: owner, purpose, risk classification, applicable regulations, and the controls claimed against each requirement. On top of that sit policy packs, pre-built mappings that turn a framework like the EU AI Act into a checklist a governance team can work through, with automated workflows to collect attestations and evidence from system owners.

The output is documentation a governance team can defend. When a board asks whether the organization is ready for the August 2, 2026 high-risk obligations, or a regulator asks for the technical file, Credo AI is where the answer is assembled. It gives a Chief Risk Officer a defensible, current picture of the program.

A governance registry records that a control is in place. The entry for a high-risk system will show a green marker against the logging requirement. What the registry holds is the claim that logging exists, collected from the system's owner.

The claim in the registry versus the control on the wire

DeepInspect is a stateless proxy sitting between authenticated users or agents and any LLM endpoint. It runs a control rather than describing one.

The gap between these two layers is where EU AI Act programs get caught. A registry entry that says a control exists is a different thing from the control running on live traffic, and treating the two as interchangeable is the most common mistake I would warn a compliance owner about. When an auditor points at the green marker for Article 12 logging and asks to see the actual per-decision records behind it, the registry cannot produce them. Something on the request path has to have generated them. The requirement itself is laid out in EU AI Act Article 19 logging.

DeepInspect is that component. It evaluates identity, role, data classification, and policy on every request, and it commits a structured record of each decision to storage the application cannot alter. The registry references that evidence rather than creating it. The distinction between a governance claim and independent evidence is the whole argument in signed audit logs for AI requests.

Feature comparison

| Capability | Credo AI | DeepInspect | |---|---|---| | AI system registry and inventory | Yes, core | No | | Policy packs for EU AI Act, NIST, ISO 42001 | Yes, core | No | | Risk assessment workflows | Yes | No | | Audit-ready documentation assembly | Yes | Evidence source only | | Inline decision on a live model call | No | Core | | Identity-bound per-request policy | No | Core | | Per-decision audit record | No | Core, tamper-evident | | Blocks a non-compliant request in real time | No | Yes | | Model-agnostic across HTTP LLM endpoints | Not applicable | Yes | | Primary buyer | Governance, risk, compliance | Security and compliance |

The table shows almost no overlap, and that is the point. Credo AI governs the program. DeepInspect enforces at the request and produces the evidence the program's documentation points to.

Where Credo AI is the better answer

If the problem is that nobody can say how many AI systems the organization runs, which are high-risk, or whether each has an owner and a risk rating, a governance platform is the answer and a proxy is not. Program-level governance, cross-functional attestation collection, and framework mapping are Credo AI's job, and DeepInspect has nothing to say about any of them. The two ideas of governance and enforcement are separated in AI governance and risk management.

Any organization above a handful of AI systems needs a registry. Credo AI is a mature option for that layer.

Where the gap opens

Governance documents what should be true. Enforcement makes it true on live traffic, and produces the record that proves it.

An EU AI Act technical file will claim that access to high-risk systems is controlled by role and that decisions are logged for traceability. Under audit, both claims need evidence from the request path. A registry entry is an assertion. A per-decision record, committed independently of the application, is proof. The reason applications cannot be trusted to prove their own compliance is developed in AI vendor liability.

Runtime prevention is the second half. A registry cannot stop an analyst from sending regulated data to an unapproved model this afternoon. An inline policy layer can, before the request reaches the provider.

Pick Credo AI if

You need an AI inventory and a system of record for your governance program.
Your priority is mapping systems to the EU AI Act, NIST AI RMF, or ISO 42001.
You are assembling technical files and executive-level risk reporting.
Cross-functional attestation and workflow tracking is the immediate need.
Runtime enforcement is already handled elsewhere in your stack.

Pick DeepInspect if

You need model calls actually blocked when policy says no.
An auditor wants the per-decision records behind a governance claim.
Policy must be enforced by identity and role at request time.
You need audit evidence on storage the application cannot alter.
You operate under the EU AI Act, DORA, HIPAA, or a comparable mandate.

DeepInspect

This is the gap DeepInspect closes. DeepInspect sits at the AI request boundary as an external enforcement layer: deterministic, identity-aware, and independent of model behavior. Every request is evaluated against who is asking, what role they hold, and what data is involved, and enforcement happens inline and can fail closed.

The evidence is what makes it fit a governance program. Every decision writes a structured entry: the authenticated identity, the resolved role, the data classes in the prompt, the policy version in force, and the outcome. That entry commits before the response returns, on storage the application never had write access to, which is what turns a registry's claim into an audit's proof. A governance platform and an enforcement layer belong in the same program, at different layers.

If you are facing the August deadline, let's talk.