Document Type: Framework
Status: Canon
Version: v1.0
Authority: Compliance Brain
Applies To: All MWMS outputs requiring compliance review
Parent: Compliance Brain Canon
Last Reviewed: 2026-04-15
Purpose
Compliance Classification Framework defines how MWMS classifies compliance issues into structured risk categories.
Classification improves consistency.
Consistency improves review reliability.
Reliable review improves enforcement prevention.
Without classification discipline, compliance review becomes inconsistent and difficult to compare across outputs.
Compliance classification ensures that similar issues are identified, named, and escalated consistently across MWMS.
Scope
This framework applies to:
claim-related compliance issues
policy-related compliance issues
disclosure-related compliance issues
privacy-related compliance issues
misrepresentation-related compliance issues
billing and refund transparency issues
jurisdiction-sensitive compliance conflicts
This framework governs how compliance findings are categorised.
It does not govern:
final escalation decision by itself
platform strategy
execution changes
legal interpretation
Those remain governed by:
Compliance Brain Canon
Compliance Brain Policy Escalation Framework
HeadOffice
Compliance classification improves review consistency.
Core Principle
Compliance findings must be classified before they are escalated.
Unclassified issues reduce review clarity.
Reduced clarity increases inconsistency.
Inconsistent severity treatment increases enforcement exposure.
Classification must remain structured, repeatable, and comparable across outputs.
Primary Compliance Categories
Claim Risk
Applies when an output includes statements that may be:
unsupported
unverifiable
overstated
misleading
Examples:
performance guarantees
medical cure language
income certainty language
unsupported superiority claims
Claim risk increases when proof quality is weak.
Policy Risk
Applies when output may violate platform or network rules.
Examples:
prohibited content framing
restricted category wording
platform-sensitive targeting language
affiliate network policy conflict
Policy risk increases when content conflicts with external rule environments.
Disclosure Risk
Applies when required transparency elements are missing or unclear.
Examples:
missing affiliate disclosure
unclear sponsorship identification
missing risk disclosure
unclear billing disclosure
Disclosure risk increases when user visibility is reduced.
Data Privacy Risk
Applies when tracking, storage, or consent structures are unclear or potentially non-compliant.
Examples:
missing consent clarity
unclear data collection purpose
unclear pixel or webhook flows
unclear personal data usage
Data privacy risk increases when data visibility is weak.
Misrepresentation Risk
Applies when identity, offer, proof, urgency, or context is presented in a misleading way.
Examples:
false scarcity
fake testimonials
fabricated endorsements
hidden relationship framing
Misrepresentation risk increases when perception is manipulated beyond defensible truth.
Billing and Consumer Protection Risk
Applies when commercial terms are unclear or potentially unfair.
Examples:
hidden fees
unclear refund conditions
unclear rebilling
unclear subscription terms
Consumer protection risk increases when transaction clarity is weak.
Jurisdiction Conflict Risk
Applies when execution may be acceptable in one jurisdiction but problematic in another.
Examples:
different disclosure requirements
different privacy consent expectations
different health claim sensitivity
different financial promotion restrictions
Jurisdiction conflict increases when universal safe posture is unclear.
Secondary Classification Fields
Each compliance finding must also identify:
Platform Surface
Jurisdiction Surface
Evidence Availability
Disclosure Status
Urgency Sensitivity
Repeat Pattern Status
Secondary fields improve interpretation depth.
Severity Mapping Rule
Each classified compliance finding must be paired with a severity level.
Severity levels are:
Level 1 Minor Deviation
Level 2 Material Risk
Level 3 High Violation Risk
Level 4 Critical Enforcement Risk
Classification identifies the type of issue.
Severity identifies the intensity of issue.
Both must be present.
Multi-Category Rule
A single finding may belong to multiple categories.
Example:
an income guarantee may be:
Claim Risk
Policy Risk
Misrepresentation Risk
Multi-category classification improves review accuracy.
Do not force single-category classification when overlap exists.
Repeat Pattern Rule
If the same classification appears repeatedly across outputs, repeat pattern status must be recorded.
Examples:
repeated claim risk
repeated missing disclosure
repeated privacy ambiguity
Repeat patterns increase enforcement sensitivity.
Repeat patterns must inform escalation logic.
Relationship to Other Frameworks
Compliance Brain Canon
defines overall compliance authority posture
Compliance Brain Policy Escalation Framework
defines when findings must escalate
Compliance Brain Claims Risk Framework
deepens claim-specific compliance analysis
Compliance Brain Data and Platform Compliance Framework
deepens data and platform-specific rule alignment
Classification improves consistency across all compliance reviews.
Failure Modes Prevented
inconsistent naming of similar issues
severity confusion across reviews
policy issues being confused with claim issues
privacy issues being hidden under generic risk language
misrepresentation patterns being missed
weak review comparability across outputs
Classification discipline improves enforcement prevention reliability.
Drift Protection
The system must prevent:
compliance issues being reviewed without category assignment
similar issues being classified differently without reason
multi-category issues being oversimplified
repeat patterns being ignored
classification logic drifting based on preference
Classification must remain structured and comparable.
Architectural Intent
Compliance classification creates a common language for external rule risk across MWMS.
Common language improves review stability.
Stable review improves escalation quality.
Improved escalation quality reduces enforcement disruption risk.
Classification strengthens compliance consistency across the ecosystem.
Final Rule
If compliance issues are not classified consistently, severity treatment becomes unstable.
Unstable severity treatment increases enforcement exposure.
Classification clarity must precede escalation.
Change Log
Version: v1.0
Date: 2026-04-15
Author: MWMS HeadOffice
Change:
Initial creation of Compliance Brain Compliance Classification Framework defining structured compliance issue categories for consistent review and escalation across MWMS.
END COMPLIANCE BRAIN COMPLIANCE CLASSIFICATION FRAMEWORK v1.0