Skip to main content
Trust Centre·AI Governance Framework·Clarvus AIOS

01. Governance

AI Governance Framework

Clarvus AIOS treats governance as an architectural property — not a policy document, not an audit checklist, and not a control retrofitted after deployment.

The governance framework described here is the operational reality of how Clarvus AIOS behaves in production. Every property stated is a structural constraint enforced at inference time, not an aspiration.

1. Governance Architecture

REAPS: five governance constraints enforced at the infrastructure layer

REAPS (Responsible, Explainable, Auditable, Policy-Driven, Sovereign) is not a framework applied to Clarvus AIOS. It is the architecture of Clarvus AIOS. Each principle is a structural enforcement mechanism compiled into the operating system.

The significance of this distinction is governance permanence. When governance is a platform property, it cannot be disabled, optimised away, or deferred under delivery pressure. When it is a separate framework applied to a system, it can be.

R
Responsible
Automated fairness layer at inference
E
Explainable
Reasoning chains generated at inference time
A
Auditable
Hash-chained immutable decision records
P
Policy-Driven
Layered YAML policy enforcement: L0–L3
S
Sovereign
Data residency and BYOM/BYOL enforcement

2. Policy Architecture

Four-layer policy enforcement

Governance in Clarvus AIOS is expressed as layered declarative policy. When a regulatory requirement changes, administrators update a policy file. No application code changes. No redeployment. The constraint propagates to every product and tenant through the infrastructure layer.

L0
Platform FrameworkArthaVedh — immutable

The non-negotiable governance baseline encoded into Clarvus AIOS. Defines the minimum safety envelope, REAPS enforcement requirements, and audit trail standards that no deployment can override. Every Clarvus AIOS installation inherits L0 unconditionally.

L1
Institutional DefaultsEnterprise operator

Governance configuration at the deploying institution level. Defines default explainability thresholds, escalation routing, regulatory framework selection (EU AI Act, RBI, SEBI, SOX), and institutional data residency policy.

L2
Product ConfigurationProduct team

Application-specific governance parameters. CertiVus applies different policy weights than Stanli. Each product team configures domain-appropriate constraints within the L1 institutional envelope — without accessing the L0 platform framework.

L3
Tenant CustomisationEnterprise tenant

Per-client configuration within the L2 product envelope. Tenants may configure additional constraints, notification preferences, and reporting formats. Tenants cannot relax L0, L1, or L2 controls — they can only tighten them.

3. Audit Trail

Chain-of-custody from request to output

The audit trail in Clarvus AIOS is a first-class architectural output — not a logging side-effect. Every LLM call, skill invocation, policy evaluation, and decision boundary crossing is recorded, hash-chained, and accessible to authorised parties on demand.

Immutability

Every audit record is hash-chained to its predecessor. Altering a single record breaks the chain and surfaces immediately — the trail cannot be quietly amended after the fact.

Completeness

The audit trail captures every LLM call, skill invocation, policy evaluation, and decision boundary crossing. There are no gaps in the chain of custody from request to output.

Exportability

Compliance teams can export complete evidence packages for regulatory examination without manual document construction. The export format is designed for regulatory examiners, not developers.

On-demand access

Audit records are accessible on demand to regulators, boards, and internal compliance functions. No data engineering pipeline is required to reconstruct a decision trail.

4. Oversight

Human-in-the-loop escalation architecture

Human oversight in Clarvus AIOS is a governance mechanism, not a UI feature. The system identifies inference scenarios that exceed configured confidence thresholds or enter regulatory risk zones, and routes them to human reviewers through a structured escalation protocol — before output reaches the end user.

Escalation routing is configurable at L1 (institutional) and L2 (product) policy layers. Institutions can define routing by regulatory classification, decision boundary type, or risk score. Escalations are logged in the audit trail with the same chain-of-custody properties as automated decisions.

Board-level governance dashboards aggregate AI operational metrics across all deployments — model performance, policy violations (expected: zero), escalation volumes, and regulatory compliance status — in a format designed for executive review, not engineering analysis.

Need governance documentation for a procurement evaluation?

Architecture diagrams, security questionnaire responses, and detailed policy documentation available on request.