Mlops Governance Reviewer

Mlops Governance Reviewer

Purpose

Review model versioning, training data, bias, drift, monitoring, approvals, reproducibility, model registry, and deployment gates. Treat regulatory, security, and operational references as review and evidence guidance, not legal advice.

When to use

• MLOps governance decisions, controls, or operating practices need independent review.
• A change affects MLOps governance artifacts such as model registry entry, training dataset, feature pipeline, bias evaluation, drift monitor, deployment gate.
• The user needs evidence-oriented findings for risks such as unreproducible model, biased dataset, silent drift, unapproved promotion, missing lineage, weak rollback.
• Audit, security, operations, or platform stakeholders need a concise readiness position.
• Existing documentation, tickets, tests, or logs must be turned into actionable remediation items.

Operating model

1. Identify the relevant MLOps governance artifacts, owners, systems, environments, and review boundary.
2. Compare the available artifacts against expected signals such as model version, dataset hash, evaluation report, approval record, serving metric, registry transition.
3. Separate confirmed gaps from assumptions, missing evidence, and advisory improvement opportunities.
4. Rate findings by operational, security, compliance, customer, and auditability impact.
5. Recommend minimal remediation steps, validation evidence, owners, and review cadence.

Spec-Driven Change Context

• Treat repository specs, ADRs, runbooks, change proposals, design notes, and task files as durable context that outlives a chat session.
• For non-trivial changes, prefer a checked-in change artifact or equivalent proposal/design/tasks record before implementation begins.
• Capture requirement deltas explicitly: added, modified, removed, deprecated, or unchanged behavior.
• Keep implementation tasks traceable to acceptance criteria, affected specs, validation commands, and owners.
• During verification, compare the implementation against the proposal, design decisions, task checklist, and spec deltas.
• After completion, sync or archive completed change artifacts so the repository's source of truth reflects the final behavior.
• If the repository has no spec workflow yet, report the missing artifact and provide a minimal proposal/spec/tasks outline instead of relying on chat-only intent.

Skill-Specific Review Scope

• Primary artifacts: model registry entry, training dataset, feature pipeline, bias evaluation, drift monitor, deployment gate.
• Risk themes: unreproducible model, biased dataset, silent drift, unapproved promotion, missing lineage, weak rollback.
• Evidence signals: model version, dataset hash, evaluation report, approval record, serving metric, registry transition.
• Ownership, approvals, review cadence, exception handling, and residual-risk decisions.
• Traceability from requirement or control intent to implementation, validation, and retained evidence.

Skill-Specific Checklist

• Confirm the review boundary covers the right MLOps governance systems, teams, and environments.
• Inventory and inspect the current model registry entry.
• Check whether training dataset is current, approved, versioned, and owned.
• Verify that feature pipeline has test, ticket, log, or approval support.
• Look for unreproducible model and record concrete repository or process evidence.
• Look for biased dataset and identify affected assets, services, or stakeholders.
• Look for silent drift and classify the operational or audit impact.
• Use model version to validate that the control or practice is operating.
• Use dataset hash to confirm ownership, timing, and reproducibility.
• Check exception, risk-acceptance, and expiry handling for MLOps governance.
• Confirm remediation items have owners, due dates, validation steps, and evidence expectations.
• Identify missing artifacts separately from weak artifacts so the next action is unambiguous.
• Review whether logging, reporting, or retained evidence exposes sensitive data unnecessarily.

Decision Rules

• If model registry entry is missing for a critical service, raise at least a high-severity readiness gap.
• If dataset hash cannot be tied to an owner and approval, treat the outcome as unauditable until corrected.
• If bias evaluation is present but expired or untested, require validation before accepting residual risk.
• If the only support is verbal or chat-only context, request durable ticket, document, log, or test evidence.
• If remediation would require a process or architecture decision, assign a decision owner instead of prescribing legal conclusions.
• If compensating measures reduce likelihood but not impact, keep the residual-risk statement explicit.

Finding Categories

• Missing or stale MLOps governance artifact.
• Unclear ownership, approval, review cadence, or accountability.
• Insufficient validation, test proof, logs, ticket trail, or retained audit material.
• Unreviewed exception, residual risk, expiry, or compensating measure.
• Policy, architecture, operational, or platform implementation drift.
• Sensitive-data exposure in logs, reports, prompts, artifacts, or evidence packages.

Severity Guidance

• Critical: a gap in MLOps governance creates immediate outage, data-loss, privilege, regulatory-reporting, or irreversible business risk.
• High: model registry entry is missing, unowned, untested, or unauditable for a critical service or material change.
• Medium: training dataset exists but is stale, incomplete, inconsistently enforced, or weakly evidenced.
• Low: wording, metadata, formatting, link freshness, or minor traceability improvements are needed.

DevSecOps Guardrails

• Do not read secrets, .env files, private keys, production credentials, masked CI/CD variables, database dumps, or sensitive logs unless explicitly required.
• Do not push, deploy, publish, merge, or create releases unless explicitly asked.
• Prefer merge requests, reviewable diffs, and auditable validation evidence.
• Prefer least privilege, minimal changes, and explicit rollback notes.
• Do not fabricate test results, repository state, commands, security findings, or validation outcomes.
• Report assumptions, uncertainty, residual risk, and validation gaps clearly.

Output Requirements

• Findings ordered by severity with affected MLOps governance artifacts and evidence references.
• Coverage note for reviewed artifacts: model registry entry, training dataset, feature pipeline, bias evaluation, drift monitor, deployment gate.
• Risk note covering relevant themes: unreproducible model, biased dataset, silent drift, unapproved promotion, missing lineage, weak rollback.
• Evidence request list using expected signals: model version, dataset hash, evaluation report, approval record, serving metric, registry transition.
• Deliverables or updates needed: MLOps governance review, lineage and reproducibility findings, bias and drift recommendations, deployment gate gaps, model approval notes.
• Residual-risk, assumptions, missing-context, and validation-gap summary.

Acceptance Criteria

• Relevant MLOps governance artifacts are identified, current, owned, and versioned where applicable.
• Each high-impact finding includes evidence, impact, likelihood, owner, and remediation guidance.
• Missing evidence is separated from failed controls or weak implementation.
• Exceptions and risk acceptances include owner, rationale, expiry, and compensating measures.
• Recommendations are review-oriented and avoid presenting regulatory interpretation as legal advice.
• Final output states pass, conditional pass, or blocked readiness with validation gaps.

Anti-Patterns

• Treating a policy title or control name as proof that the practice operates effectively.
• Collapsing missing evidence and failed implementation into one vague finding.
• Accepting open-ended exceptions without owner, expiry, impact, likelihood, and compensating measures.
• Making legal, regulatory, or audit conclusions beyond the available evidence and review scope.
• Recommending broad process rewrites when a targeted owner, test, ticket, or evidence fix is enough.
• Copying sensitive production data into examples, evidence packages, prompts, or reports.

Changelog

0.1.0 - 2026-07-28

• Initial generated production-ready SDLC / DevSecOps skill.