Minimum AI Standardisation Contract

Purpose

As AI systems become embedded in operational workflows, the quality, lineage, and trustworthiness of the data they consume becomes a governance risk, not just a data engineering problem.

MASC answers the question: what must be true about data before an AI system is permitted to act on it?

It sets a floor — a minimum contract — not a ceiling. Organisations may implement stricter controls. MASC defines what no AI-integrated system should fall below.

Scope

MASC governs data that flows into, through, or out of AI systems. This includes:

• Structured data (databases, API responses, CSV exports)
• Semi-structured data (JSON payloads, event streams, logs)
• Unstructured content (documents, emails, transcripts processed by AI)
• External data feeds (third-party integrations, webhooks, scraped data)
• Records and evidence (compliance documents, audit exports)

MASC explicitly does not govern human-originated work artifacts — tasks, instructions, and requests created by people. Those are governed by WNSC.

Core Principles

Requirements

Data quality

Lineage

Every field processed by an AI system SHALL record:

• Source system identifier
• Extraction or ingestion timestamp
• Transformations applied (rule set name and version)
• Any enrichment or inference steps

Lineage records MUST be immutable and queryable. Retroactive modification of lineage is a MASC violation.

Trust score

Each record MUST carry a computable trust score based on:

• Source agreement (cross-system consistency)
• Freshness relative to declared staleness policy
• Validation outcome (pass / advisory / fail)
• Conflict detection (contradictions with known records)

Privacy and security

• Processing is stateless by default — no PII retained beyond session unless explicitly required
• Sensitive fields must be masked or tokenised before AI processing unless explicit consent and legal basis exist
• Region-aware data residency must be enforced
• Least-privilege access controls apply to all AI pipeline components

Enforcement and quarantine

Records that fail MASC checks MUST be:

• Quarantined from the AI pipeline
• Annotated with the specific failure reason
• Hashed for traceability (to enable future reconciliation)

Raw content MUST NOT be retained in AI pipeline logs unless explicitly permitted by data governance policy.

Certification Levels

MASC defines three progressive certification levels. Higher levels are supersets — L3 compliance implies L1 and L2.

MASC-L3 is the minimum level required for any AI system that produces governance outputs, compliance findings, or narratives that inform organisational decisions.

Reference Implementation

MASC is an abstract governance contract. Any system that satisfies its requirements may claim MASC compliance at the relevant level.

Catalyst by Stratogenic AI is the reference implementation of MASC. It enforces MASC compliance at every ingestion and execution boundary:

• All data ingested via the Catalyst API is validated against canonical schemas before entering the governance pipeline
• Lineage is recorded in the immutable, SHA-256-chained audit ledger on every state transition
• Trust scoring is computed and stored as risk_effort on each flow item
• The AI narrative layer (Safe Narrative Engine) operates at MASC-L3 — identity-locked, output-constrained, no individual evaluation permitted
• Non-compliant records are quarantined as proposals for human review before entering the flow cycle

Alternative implementations may exist provided they satisfy MASC's behavioural requirements at the claimed certification level.

Relationship to WNSC

MASC and WNSC (Work Normalisation & Systemic Clarity) are complementary frameworks that together define a complete AI-safe governance layer:

• WNSC governs human-originated work: the tasks, instructions, and requests that people create. It ensures they are canonical, unambiguous, and safe for automation.
• MASC governs the data those tasks flow through: AI systems must only act on data that meets MASC's quality, lineage, and trust requirements.

Together, WNSC + MASC ensure that AI-influenced actions are visible, controlled, and auditable at runtime — without requiring AI systems to act as compliance arbiters themselves.

Intellectual Property & Licensing

Related