Governance Policy

A centralized, searchable registry of all models in development and production, tracking their complete lineage. This includes:

• Model metadata: Owner, version, creation date, intended use.
• Artifact provenance: Links to the exact training data, code, hyperparameters, and environment used.
• Deployment history: Records of promotions, rollbacks, and retirement status.

This component is foundational for auditability, enabling teams to answer what model is deployed, who built it, when, and with what.

A tiered system that categorizes models based on their potential risk and impact. Criteria typically include:

• Regulatory exposure: Does the model make decisions in regulated domains (e.g., credit, hiring, healthcare)?
• User impact: What is the consequence of an error (financial loss, safety, reputational damage)?
• Data sensitivity: Does it process personally identifiable information (PII) or other protected data?

High-risk models trigger stricter requirements for explainability, bias testing, approval workflows, and monitoring.

Mandatory technical and ethical checkpoints during model development. This defines the minimum viable model for promotion. Key standards include:

• Performance Baselines: Models must outperform a simple benchmark or previous version.
• Bias & Fairness Testing: Required assessments for high-risk models using metrics like demographic parity or equalized odds.
• Robustness & Security: Testing for adversarial examples and data poisoning vulnerabilities.
• Explainability Requirements: Mandating techniques like SHAP or LIME for critical decisions.
• Documentation: Completion of a Model Card detailing limitations, performance across subgroups, and intended use.

Formalized processes for moving models to production and managing updates. This ensures stability and prevents unauthorized changes.

• Approval Workflows: Mandatory sign-off from technical, business, and compliance stakeholders before production deployment.
• Deployment Strategies: Mandating the use of canary deployments, shadow deployments, or blue-green deployments for controlled rollouts.
• Version Control & Immutability: All production model artifacts must be versioned and immutable.
• Rollback Procedures: Documented, tested processes for reverting to a previous stable model version in case of failure.

Continuous surveillance of live models to ensure they perform as expected. This moves governance from a pre-launch checklist to a continuous activity.

• Performance Drift: Monitoring key accuracy and business metrics for degradation.
• Data Drift & Concept Drift: Automated detection of shifts in input data distribution or underlying relationships.
• Operational Health: Tracking latency, throughput, and error rates of serving endpoints.
• Business Metric Alignment: Ensuring model predictions continue to drive positive business outcomes (e.g., conversion rate, revenue).
• Alerting & Retraining Triggers: Defining thresholds that automatically alert owners or trigger model retraining pipelines.

Clear assignment of accountability and an immutable record of all actions. This creates an organizational structure for governance.

• RACI Matrix: Defining who is Responsible, Accountable, Consulted, and Informed for each governance activity (e.g., model validation, deployment approval).
• Model Custodian vs. Owner: Distinguishing between the technical builder (custodian) and the business stakeholder accountable for outcomes (owner).
• Immutable Audit Trail: Logging every action—model registration, approval, deployment, configuration change—with timestamp and user identity. This is non-negotiable for compliance with regulations like the EU AI Act or financial standards.

A centralized repository that acts as the single source of truth for all model artifacts, enforcing governance by controlling access, versioning, and lineage. It is the foundational system for policy compliance, ensuring only approved, audited models can progress to deployment.

• Enforces Version Control: Mandates systematic tracking of model iterations.
• Stores Metadata: Holds governance-critical information like creator, intended use, and approval status.
• Gatekeeper for Promotion: Integrates with CI/CD pipelines to block models that fail validation gates.

A formalized process requiring human or automated sign-off at key decision points, such as before production deployment or after a significant retraining event. This is the procedural backbone of a governance policy, ensuring accountability and preventing unauthorized changes.

• Defines Roles and Responsibilities: Specifies who (e.g., Lead Data Scientist, Compliance Officer) must approve each stage.
• Creates an Audit Trail: Logs every approval, rejection, and comment for compliance reporting.
• Integrates with Tooling: Often automated within MLOps platforms to block pipeline progression until approvals are granted.

A standardized document that provides essential context for model governance, detailing intended uses, limitations, performance metrics, and ethical considerations. It serves as a key artifact for transparency, enabling stakeholders to understand a model's compliance with policy standards.

• Documents Intended Use: Explicitly states the permissible applications, a core governance requirement.
• Details Performance Disparities: Highlights accuracy variations across different demographic slices for fairness audits.
• Provides Evaluation Results: Includes results from bias, safety, and adversarial testing mandated by policy.

An immutable, chronological log that records all actions, decisions, and changes made to a model and its associated assets. This is a non-negotiable component of governance for accountability, forensic analysis, and regulatory compliance (e.g., EU AI Act).

• Tracks Model Lineage: Logs the exact data, code, and parameters used to create every model version.
• Records Access and Changes: Captures who promoted, approved, or modified any asset.
• Enables Reproducibility: Provides the complete history needed to recreate any past model state for an audit.

A predefined quality or performance checkpoint that a model must pass before it can be promoted to the next stage of the deployment pipeline. These gates operationalize governance policy by translating high-level rules into automated, quantitative tests.

• Examples Include: Minimum accuracy threshold, maximum bias metric, successful security scan, or data schema validation.
• Automates Compliance: Gates are enforced programmatically within CI/CD pipelines, removing human error.
• Prevents Policy Violations: Stops underperforming or non-compliant models from reaching production.

The formal process of decommissioning a model from active production service, governed by a policy that defines triggers and procedures. Governance requires controlled retirement to manage risk, cost, and compliance when a model is obsolete, underperforming, or replaced.

• Policy Triggers: May include sustained performance drift, end of business use case, or supersession by a new champion model.
• Governed Procedure: Involves archiving artifacts, redirecting traffic, updating the registry, and notifying stakeholders.
• Ensures Cleanup: Prevents "orphaned" models from creating security vulnerabilities or unnecessary infrastructure costs.