Data Pipeline Validation Test Plan

Plan BUILD

Purpose

Test plan for validating AI data pipelines covering data quality, lineage, bias detection, and integrity checks.

Data Pipeline Validation

ISO A.6 NIST MP-4

Identify the data pipeline under test and its purpose.

Pipeline Name: [PIPELINE NAME]

System: [SYSTEM NAME]

Owner: [NAME], [ROLE TITLE]

Test Date: [DATE]

Tester: [NAME], [ROLE TITLE]

Pipeline Purpose: [Description of what data the pipeline processes and why]

Data Sources: [List all input data sources]

Data Destinations: [List all output destinations]

Data Classification: Public / Internal / Confidential / Restricted

Define tests that validate data quality at each pipeline stage.

Test ID	Test Description	Expected Result
DQ-01	Schema validation — all required fields present and correctly typed	100% compliance
DQ-02	Null/missing value check — critical fields have no nulls	<1% null rate on required fields
DQ-03	Duplicate detection — no duplicate records in output	0 duplicates
DQ-04	Range validation — numeric fields within expected bounds	100% within range
DQ-05	Referential integrity — foreign keys resolve to valid records	100% valid references
DQ-06	Freshness check — data timestamp within acceptable window	Within [X] hours

Validate that the data pipeline does not introduce or amplify bias.

[ ] Demographic distribution — Protected attribute distributions in output match expected population distributions (within [X]% tolerance)
[ ] Label balance — Training labels are balanced across protected groups or appropriate weighting is applied
[ ] Proxy variable check — No features that serve as proxies for protected attributes (zip code → race, name → gender)
[ ] Historical bias audit — Training data reviewed for historical biases that could be learned by the model
[ ] Sampling methodology — Data sampling method documented and verified as representative

Validate data handling security and privacy compliance.

[ ] Encryption in transit — All data transfers use TLS 1.2+
[ ] Encryption at rest — All stored data is encrypted (AES-256 or equivalent)
[ ] Access controls — Pipeline service accounts have least-privilege access
[ ] PII handling — PII is masked, tokenized, or encrypted per data classification policy
[ ] Data minimization — Pipeline only processes data necessary for the stated purpose
[ ] Retention compliance — Data retention aligns with organizational policy; automated deletion confirmed
[ ] Audit logging — All data access and transformations are logged with timestamps and actor identity
[ ] Cross-border transfers — Data does not cross jurisdictional boundaries unless authorized

Summarize overall test results and document the go/no-go decision.

Overall Result: Pass / Fail / Conditional Pass

Tests Passed: [X] / [TOTAL]

Critical Failures: [COUNT — any critical failure = overall Fail]

Open Issues:

Approval:

Next Scheduled Validation: [DATE]