Does this platform enforce policy on individual model calls?

The platform governs at the program level and supervises agent behavior through its Guardian Agents, which can intervene when a risk threshold is crossed. It does not sit on each HTTP request deciding whether a single call, by a specific identity, with specific data, is permitted. That request-level decision is a separate enforcement layer.

Can a governance platform produce EU AI Act Article 12 logs?

It records that a logging control is claimed for a system and assembles evidence for reporting. The actual per-decision logs required for traceability are generated on the request path by whatever component sees the traffic. A governance platform 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 discovery, assessment, and framework mapping and supervises agent behavior. The enforcement layer runs the per-request control and produces the per-decision evidence. They occupy different layers and reference each other.

Is DeepInspect a governance platform?

No. DeepInspect enforces identity-bound policy on live AI traffic and writes audit records, rather than maintaining a program-wide AI registry, testing models for bias, or mapping systems to frameworks. It supplies evidence into a governance program rather than replacing one.

DeepInspect vs Holistic AI: Governance Supervision and Runtime Enforcement

The governance platform in this comparison operates at the program level. It discovers the AI systems an organization runs, assesses each for risk, tests for bias and safety, and maps controls to the EU AI Act, NIST AI RMF, ISO 42001, and NYC Local Law 144. On top of that sits a supervisory layer, Guardian Agents, split into Sentinel Agents that watch each agent action against policy and Operative Agents that intervene, enforce, or remediate when a risk threshold is crossed, with deployment gates, approval workflows, and kill switches around the program.

That is governance work with a supervisory layer, and it is real work. Someone has to know which models are in production, which are high-risk, and whether an agent stayed inside the guardrails set for it.

Teams comparing this vendor to DeepInspect are usually deciding whether a governance-and-supervision platform covers their runtime obligations too. It covers a different layer, and the reason is structural. I want to walk what each one owns, then show where the handoff happens.

TL;DR

The vendor here is a governance platform: AI discovery, risk assessment, framework mapping, and a Guardian Agents layer that supervises and can intervene on agent behavior. 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 governs the program and supervises agents against policy. The other authorizes each individual call and produces its evidence.

The governance platform: discover, assess, supervise

The core is program governance. Every AI system gets discovered and registered with an owner, a purpose, a risk rating, and a control set mapped to the frameworks that apply, and the platform runs bias, safety, and performance testing to support those ratings. That gives a Chief Risk Officer a defensible, current picture of the program.

Guardian Agents extend governance toward runtime. Sentinel Agents observe an agent's actions and evaluate them against policy, and Operative Agents step in when a threshold is crossed, which is closer to live control than a static registry. The supervision operates at the level of agent behavior and program policy, orchestrating gates, approvals, and kill switches across systems.

The platform governs and supervises. It records that a system is under control, tests how it behaves, and watches agent actions against the rules set for them. That is a program-and-behavior question, distinct from whether a specific caller was authorized to send specific data on a specific request.

Program supervision versus request authorization

DeepInspect is a stateless proxy sitting between authenticated users or agents and any LLM endpoint. It runs a control on each request rather than supervising a program.

The gap between these layers is where EU AI Act programs get caught. Supervising an agent's behavior against policy is a different thing from deciding, on a single HTTP call, whether this authenticated identity in this role may send this class of data to this model. When an auditor points at a control marked green for Article 12 logging and asks to see the per-decision records behind it, a governance platform references evidence rather than generating it. Something on the request path has to have produced it. The requirement 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 commits a structured record of each decision to storage the application cannot alter. The governance platform references that evidence; DeepInspect creates it. The distinction between a governance claim and independent evidence is the argument in signed audit logs for AI requests. The relationship between the two ideas is developed in AI governance and risk management.

Feature comparison

| Capability | The governance platform | DeepInspect | |---|---|---| | AI discovery and inventory | Yes, core | No | | Risk, bias, and safety assessment | Yes, core | No | | Framework mapping (EU AI Act, NIST, ISO 42001) | Yes, core | No | | Agent supervision and intervention | Yes (Guardian Agents) | Policy at the call boundary | | Inline decision on a single model call | No | Core | | Identity-bound per-request policy | No | Core | | Per-decision audit record | Governance evidence | Core, tamper-evident | | Blocks a non-compliant request in real time | Behavior-level | Yes, per call | | Model-agnostic across HTTP LLM endpoints | Not applicable | Yes | | Primary buyer | Governance, risk, compliance | Security and compliance |

The overlap is thin, and that is the point. The platform governs the program and supervises agents. DeepInspect enforces on the individual request and produces the evidence the program's documentation points to.

Where the governance platform 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 an agent stayed inside its policy, a governance-and-supervision platform is the answer and a proxy is not. Program governance, cross-framework mapping, and agent-behavior supervision are its job, and DeepInspect has nothing to say about any of them. Any organization above a handful of AI systems needs that layer.

Where the gap opens

Governance documents what should be true and supervises behavior. Enforcement makes a policy decision on each call 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 supervisory record of agent behavior is not the same as a per-decision authorization record keyed to identity, committed independently of the application. The reason applications cannot be trusted to prove their own compliance is in AI vendor liability.

Request-level prevention is the second half. Supervision that evaluates agent actions still runs above the individual call. An inline policy layer refuses a single non-compliant request before it reaches the provider, keyed to the caller's identity, which is the post-authentication gap.

Pick the governance platform if

You need AI discovery and a system of record for your governance program.
Your priority is mapping systems to the EU AI Act, NIST AI RMF, ISO 42001, or NYC LL144.
Risk, bias, and safety assessment across models is a stated need.
Agent-behavior supervision with gates and kill switches fits your program.
Request-level authorization evidence is already handled elsewhere.

Pick DeepInspect if

You need individual model calls 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 governance claim into an audit's proof. Enforcement adds under 50 ms in internal testing against inference times of 500 ms to several seconds. A governance-and-supervision platform and a request-layer enforcement layer belong in the same program, at different layers.

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