Data and model control library32 practices
Intended purpose and outcome
Define the insurance problem, affected parties, decision, expected benefit, potential harm, success measures, and prohibited uses.
Asset and system boundary
Identify the end-to-end system, data, models, prompts, retrieval, rules, tools, interfaces, human roles, and downstream decisions.
Accountable ownership
Assign business, data, model, technology, risk, compliance, privacy, security, validation, and operational owners.
Risk tier and control profile
Classify consequence, autonomy, scale, data sensitivity, complexity, reversibility, and regulatory exposure.
Source and provenance record
Record origin, provider, collection method, creation date, transformations, lineage, known gaps, and chain of custody.
Lawful and permitted use
Confirm licenses, consents, notices, contracts, privacy bases, purpose limits, geographic restrictions, and downstream rights.
Third-party and public data review
Assess vendor, broker, open, synthetic, scraped, consortium, and public data for reliability, rights, representativeness, and change.
Training retrieval and secondary use
Control whether data may train, fine-tune, evaluate, retrieve, personalize, log, or support future unrelated uses.
Accuracy completeness and consistency
Profile correctness, missingness, duplicates, contradictions, schema conformance, range, and reconciliation to authoritative sources.
Timeliness stability and drift
Measure latency, freshness, seasonality, source change, distribution shift, and operational availability.
Representativeness and coverage
Assess populations, geographies, products, channels, time periods, rare events, protected groups, and proxy effects.
Label target and construct validity
Validate labels, outcomes, sampling, leakage, proxy targets, observation windows, human judgments, and construct alignment.
Architecture and methodology
Document algorithms, foundation models, prompts, retrieval, embeddings, rules, tools, orchestration, thresholds, and dependencies.
Training tuning and configuration
Record datasets, splits, preprocessing, features, hyperparameters, prompts, fine-tunes, retrieval settings, policies, and tool permissions.
Performance limitations and uncertainty
Document metrics, benchmarks, group results, uncertainty, failure modes, out-of-scope behavior, and known limitations.
Human use and decision integration
Explain how outputs are interpreted, reviewed, overridden, communicated, appealed, and translated into insurance decisions.
Unique identity and versioning
Assign stable identifiers and versions to datasets, features, models, prompts, embeddings, indexes, rules, agents, tools, and releases.
End-to-end lineage
Connect source data and transformations to training, evaluation, retrieval, configuration, release, output, decision, and retained record.
Reproducible build and environment
Preserve code, packages, images, parameters, seeds, infrastructure, dependencies, and instructions needed to recreate results.
Release and configuration baseline
Define the approved production bundle and detect unauthorized or unexplained differences across environments.
Risk-based validation plan
Define hypotheses, datasets, metrics, groups, scenarios, thresholds, reviewers, independence, and evidence before testing.
Integrated system evaluation
Test the configured workflow, data, model, retrieval, prompts, tools, humans, controls, and downstream decision—not components alone.
Approval exceptions and residual risk
Record criteria, results, failures, remediation, exceptions, compensating controls, acceptance authority, and expiration.
Material change and revalidation
Assess changes to data, labels, model, prompts, retrieval, dependencies, policies, tools, users, scale, or context and repeat affected tests.
Production quality and drift
Monitor data quality, performance, fairness, retrieval, grounding, overrides, latency, cost, and business outcomes against thresholds.
Traceability and decision records
Retain inputs, outputs, versions, retrieval, tool calls, approvals, overrides, reasons, notices, and decisions proportionate to risk.
Incident affected-case analysis
Identify failures, impacted decisions and consumers, data or model causes, containment, correction, notification, and recurrence controls.
Feedback complaints and correction loop
Link complaints, appeals, user feedback, corrections, expert review, and outcome data to improvement and revalidation.
Role-based access and segregation
Control who can view, change, approve, deploy, query, export, or delete data and model assets; separate incompatible duties.
Retention preservation and defensible deletion
Set retention by asset, decision, jurisdiction, claim, dispute, audit, regulator, litigation hold, and operational need.
Third-party evidence and continuity
Obtain rights to documentation, versions, changes, testing, incidents, records, regulator support, portability, and transition.
Retirement and archival
Stop use, remove access, preserve required records, archive reproducible artifacts, resolve downstream dependencies, and verify deletion.