Governance that executes, not just documents.
Most AI governance products are questionnaires and policy documents describing how AI should behave. NeuroCluster enforces governance as a property of the runtime: an agent physically cannot take a policy-blocked action, and every action it does take produces its own audit trail.
Governance as policy documents vs. governance as runtime control
GRC (governance, risk, and compliance) software is genuinely useful for tracking policies, risk registers, and control frameworks. What it cannot do is stop an AI agent from taking an action in real time — because it operates alongside the AI system, not inside its execution path.
NeuroCluster's governance layer sits directly in the execution path. A policy gate is not a checklist item confirming an agent was configured correctly at deployment; it is a runtime check that blocks the specific action if it violates policy, every time, regardless of what prompted the agent to attempt it.
The three governance primitives
Every governed workflow on NeuroCluster is built from three composable primitives:
- Policy gates: rules that block or require approval for specific tool calls, data access, or outbound actions — evaluated at execution time, not just at deployment review.
- Human-in-the-loop approval: named individuals approve high-risk actions before execution, with the approval recorded against identity and timestamp — not a generic 'reviewed by admin' log entry.
- Deterministic action logging: every model call, retrieval, tool invocation, and approval is recorded automatically as a byproduct of execution, not reconstructed afterward from application logs.
Evidence packs: what a reviewer actually receives
When a security reviewer, internal auditor, or regulator asks a governance question, the answer should not require a meeting with engineering. An evidence pack exports the deployment snapshot (models, prompts, policies, and data sources in force), the complete approval history, and the full action log for the period under review — as a single artifact.
This is the practical difference between compliance as a quarterly documentation exercise and compliance as infrastructure output. Teams running on ungoverned AI APIs reconstruct this evidence by hand, under deadline pressure, after the fact.
Mapped to the frameworks your reviewers cite
Governance-as-runtime maps directly onto the specific obligations regulated organizations are already accountable for:
- EU AI Act (Art. 12/14/26): logging, human oversight, and deployer obligations as native runtime properties, not add-on documentation.
- ISO 27001-aligned ISMS: AI governance decisions (who approved what, under which policy) verifiably logged as part of your management system.
- Sector security baselines (BIO2, DORA, NIS2): auditable evidence of access control and human oversight for AI-touched workflows.
Frequently asked questions
How is this different from AI governance software like Credo AI or Holistic AI?
Those platforms are strong at policy tracking, risk registers, and documentation workflows layered alongside your AI systems. NeuroCluster is the runtime itself — policy gates block or require approval for specific actions as they happen, and the evidence is a byproduct of execution rather than a separate documentation exercise.
Does this replace our existing GRC tooling?
Not necessarily — many organizations use both. Your GRC platform can continue tracking organization-wide policy and risk; NeuroCluster enforces and evidences those policies at the point where an AI agent actually takes action.
What does an evidence pack actually contain?
A deployment snapshot (model versions, prompts, policies, and data sources for the workflow), the complete human-approval history with identity and timestamps, and the full deterministic action log for the reporting period — exportable as a single package for an auditor or reviewer.
Can policy gates be configured per workflow?
Yes. Policies are scoped per workflow and per data classification, so a low-risk internal assistant and a high-risk casework agent can run on the same platform with entirely different approval requirements.
Keep evaluating
EU AI Act readiness
Classification, deployer obligations, and the readiness checklist.
Platform: governed agents
How policy gates and human-in-the-loop approvals work architecturally.
Trust Center
Security controls, residency documentation, and the procurement pack.
Case study: municipality permit processing
Policy gates and human approval in a live regulated workflow.