KLYNX GOVERNANCE STANDARD · VERSION 1.0 · MARCH 2026
Klynx Governance Standard
KGS-1.0
A specification for enterprise AI governance infrastructure — defining how AI signals are validated, how inference is gated, and how every decision is audited for regulatory compliance.
Overview
KGS-1.0 defines the architecture, interfaces, and enforcement model for AI governance in enterprise environments. It establishes how AI systems must validate input signals, gate inference execution, evaluate output risk, and record immutable audit lineage.
The standard is designed to align with the EU Artificial Intelligence Act (EU AI Act 2024), SOX AI disclosure requirements, HIPAA AI provisions, and GDPR Article 22 automated decision-making provisions.
Core Principle: Fail-Closed by Default
Every AI action is blocked unless explicitly permitted through the governance pipeline. A system compliant with KGS-1.0 cannot execute inference on degraded, incomplete, or high-risk signals.
Three-Layer Architecture
KGS-1.0 defines a mandatory three-layer governance pipeline. All layers must execute sequentially. A failure at any layer terminates the pipeline.
Signal Integrity Validation
Validates completeness, confidence, and schema of upstream signals
Inference Gate
Binary gate that permits or suppresses model invocation based on L1 result
Governance Evaluation
Policy enforcement, risk scoring, and audit trail generation
Layer 1 — Signal Integrity Validation
All signals entering the governance pipeline must pass integrity validation before inference is permitted. A signal is a structured data payload representing the context for an AI workload.
Required Signal Fields
inference_confidence_score
Float 0.0–1.0. Confidence that the input data is fit for inference. Threshold: ≥ 0.70
channel_coverage_ratio
Float 0.0–1.0. Coverage of data channels in the signal. Threshold: ≥ 0.60
event_capture_ratio
Float 0.0–1.0. Ratio of events captured vs expected. Threshold: ≥ 0.50
signal_confidence_score
Float 0.0–1.0. Composite signal quality score. Threshold: ≥ 0.65
signal_coverage_ratio
Float 0.0–1.0. Spatial/temporal coverage completeness.
modality_shift_flag
Boolean. True if a significant modality shift was detected. Triggers anomaly review.
modality_distribution_ratio
Float 0.0–1.0. Distribution balance across modalities.
project_intensity_index
Float 0.0–1.0. Operational intensity metric for the source project.
KGS-RULE-001: Any signal missing one or more required fields MUST be quarantined. The L2 gate MUST be set to CLOSED. Inference MUST NOT proceed.
Layer 2 — Inference Gate
The Inference Gate is a binary enforcement point. It evaluates the L1 result and either opens the gate (permits inference) or closes it (suppresses inference). The gate is fail-closed by default.
GATE: OPEN (layer2_gate = true)
- All required fields present
- All thresholds met
- No anomaly flags
- composite confidence ≥ 0.70
GATE: CLOSED (layer2_gate = false)
- One or more fields missing
- Threshold failure on critical field
- Anomaly flag raised
- Validation status = quarantined
KGS-RULE-002: The L2 gate result MUST be recorded in the lineage chain before any model is invoked. A closed gate is immutable — it cannot be overridden by downstream systems.
Layer 3 — Governance Evaluation
Layer 3 evaluates the workload against the active policy set, computes a composite risk score, and records a GovernanceDecision. It runs only when the L2 gate is OPEN.
GovernanceDecision
Every workload must produce exactly one GovernanceDecision object with decision_id, risk_score, decision_status, and audit_ref.
risk_score
Composite risk score. Weighted sum of task_sensitivity, data_sensitivity, compliance_impact, and signal_confidence.
decision_status
APPROVED | BLOCKED | REQUIRES_APPROVAL | DEGRADED_ALLOWED. BLOCKED and REQUIRES_APPROVAL must prevent execution.
audit_ref
Cryptographic reference tying the GovernanceDecision to the lineage chain entry. Format: AUD-{hex10}.
policy_evaluations
Array of PolicyEvaluation records — one per policy checked. Result: pass | fail | skip.
Built-in Policy Set
KGS-1.0 defines six mandatory baseline policies. Implementations may add domain-specific policies but may not remove baseline policies.
Blocks inference when signal confidence falls below the minimum threshold, regardless of other parameters.
Trigger
composite confidence < 0.70 OR validation_status = quarantined
Enforcement Action
BLOCKED — inference suppressed
Requires human approval before any protected health information leaves the governed environment.
Trigger
data_classification = phi AND output_type = export
Enforcement Action
REQUIRES_APPROVAL — dual sign-off required
High-risk production deployments require explicit governance approval and dual-approval workflow.
Trigger
task_type = production_deploy AND risk_score > 60
Enforcement Action
REQUIRES_APPROVAL — change advisory review
Financial AI decisions above materiality threshold require two independent approvers.
Trigger
task_type = financial_decision AND risk_score > 55
Enforcement Action
REQUIRES_APPROVAL — CFO + Compliance sign-off
Any workload scoring above the critical risk threshold is automatically blocked, with no override path.
Trigger
risk_score >= 85
Enforcement Action
BLOCKED — hard stop, no override
Personal data processed by AI must not leave the declared residency region without explicit data transfer agreement.
Trigger
data_classification = personal AND cross_region = true
Enforcement Action
BLOCKED — residency violation
Audit Lineage Chain
Every governed workload must produce an immutable lineage chain — a sequence of hops recording what happened at each layer. Lineage chains are the primary artefact for regulatory audit under EU AI Act Article 13 (transparency) and Article 17 (quality management).
signal hop
Recorded at L1 validation. Must include: signal_id, confidence_score, layer2_gate, gate_reason, recorded_at.
governance hop
Recorded at L3 evaluation. Must include: decision_id, risk_score, risk_level, decision_status, audit_ref.
execution hop
Recorded on workload completion. Must include: workload_id, provider, model, execution_status, cost_usd.
hop immutability
Lineage hops are append-only. No hop may be modified or deleted after recording. Keyed by signal_id.
KGS-RULE-003: Lineage recording is best-effort and non-blocking. A lineage failure MUST NOT prevent legitimate workload execution. However, gaps in lineage chains MUST be flagged in the audit report.
Risk Scoring Model
The KGS risk score is a composite 0–100 integer computed from four weighted dimensions.
Task Sensitivity
35%Risk inherent to the task type (e.g. production deploy = high, read-only query = low)
Data Sensitivity
30%Classification of data being processed (PHI, PII, financial, public)
Compliance Impact
20%Whether the workload triggers any compliance frameworks (SOX, HIPAA, GDPR)
Signal Confidence
15%Inverse of the composite signal confidence score from Layer 1
Low
0–39
Medium
40–69
High
70–84
Critical
85–100
Regulatory Compliance Mapping
KGS-1.0 maps directly to the following regulatory frameworks.
| KGS Component | EU AI Act | GDPR | SOX | HIPAA |
|---|---|---|---|---|
| Signal Validation (L1) | Art. 9 — Risk Management | Art. 25 — Data Protection | — | §164.312(b) |
| Inference Gate (L2) | Art. 6 — Prohibited Practices | Art. 22 — Automated Decisions | — | §164.308(a)(1) |
| Governance Evaluation (L3) | Art. 13 — Transparency | Art. 17 — Right to Erasure | Sec. 302/404 | §164.312(c)(1) |
| Audit Lineage Chain | Art. 17 — Quality Management | Art. 30 — Records of Processing | Sec. 802/906 | §164.312(b) |
| Risk Score | Art. 9 — Risk Classification | Art. 35 — DPIA | Sec. 404 | §164.308(a)(8) |
Implementation (SDK)
The reference implementation of KGS-1.0 is available as an open-source Python SDK.
# Install
pip install klynx
# Evaluate any AI action through the governance pipeline
from klynx import evaluate
result = evaluate(
action="marketing_campaign",
signal={"inference_confidence_score": 0.88, ...}
)
print(result.decision) # "approved"
print(result.risk_score) # 24
print(result.audit_ref) # "AUD-3F8A2C91B4"
The SDK has zero hard dependencies and requires only Python 3.8+. It connects to the Dragon governance API at dragon.klynxai.com by default.
Try it live in the demo →KGS-1.0 · Published March 2026 · Klynx AI · klynxai.com
Reference implementation: Dragon · dragon.klynxai.com