Data validation with maintained industry rule libraries — HIPAA, FDA, FINRA
Validate every location record against the rules that apply to your industry — HIPAA, ABV, FDA, FINRA — before it ever goes live downstream.
The problem
You just realized a healthcare-network location has been live for three weeks without the HIPAA-required practitioner credentials in the database. Your generic validation passed it through. A license at one of your stores lapsed in March and marketing kept running ads for two months. Your compliance officer catches these things, but only at the quarterly audit.
Generic data-validation tools (Great Expectations, dbt tests, Soda.io) can validate any data shape — but your data team has to write the industry-specific rules from scratch. HIPAA fields, ABV labeling, FDA supplement metadata, FINRA disclosure language license status — each rule set is custom development that the compliance officer cannot maintain and the data team does not have time to update as regulations shift.
The result is that violations get caught after they ship, not before. Records go live with HIPAA gaps. License lapses propagate into ad spend. Supplement claims ship without FDA-required metadata. The audit happens after the regulator finds the violation, not before.
What success looks like
Every record validates against maintained industry rule libraries before it goes live. HIPAA fields are enforced on healthcare locations. license status is confirmed within window. Alcohol ABV labeling is complete. FDA supplement metadata is present. FINRA disclosures appear on financial-services pages.
Records that fail validation never propagate. The failure routes to your compliance officer with the rule cited and the source data shown. The record stays in staging until it is corrected or your officer explicitly overrides with documented rationale.
Multi-state operators get state-by-state rules layered on top. A pharmacy in California that sells supplements gets HIPAA plus state pharmacy board plus plus FDA supplement validation at the same time. The audit trail builds itself for regulator defense.
How most operators solve this today
Generic data-validation tools exist. None of them ship maintained industry rule libraries for regulated multi-location operations:
Generic data validation (Great Expectations, dbt tests, Soda.io, Monte Carlo, Datafold, Anomalo)
Open source / $100 to $100,000+/year
Built for data engineering teams validating warehouse data shapes. Your team has to write the industry-specific rules. HIPAA, FDA, FINRA, and libraries are not included.
Validation bundled with ETL (Fivetran column tests, Airbyte schema checks)
Included with ETL subscription
Light-touch validation for type errors and missing fields. Not built for regulated-vertical schema enforcement.
Excel validation rules + custom code
Ops analyst time
Ops analyst builds rules into the master spreadsheet. Custom SQL or Python validates after the fact. Falls apart at 50-plus locations and across regulated verticals.
Compliance officer + quarterly manual audit
$120,000 to $180,000/year
Catches violations after they ship, not before they go live. Quarterly cadence means months of exposure between audits.
Build it in-house
Senior engineer ($130-220k) + compliance officer time + ongoing maintenance
You can write rules for one framework. Maintaining the cross-product of HIPAA + + FDA + FINRA + alcohol + pharmacy across 50 states is the part that never ends.
What changes when this is an agent skill
Maintained rule libraries for the frameworks that hit multi-location operators today: HIPAA for healthcare, FTC ad-substantiation, FINRA and SEC for financial services, alcohol marketing, state pharmacy boards, state lottery, FDA supplements.
Records that fail validation never go live. The failure routes to your compliance officer with the rule cited, the source data shown, and a recommended correction. The record stays in staging until your officer corrects it or explicitly overrides with documented rationale.
State-by-state rules layer on top automatically. A multi-state operator gets the right rule combination per location without writing the cross-product by hand. Fast pattern checks (regex, keyword, type, format) run first and cheaply. An AI semantic check follows for things pattern matching cannot detect — implied health claims, undisclosed sponsorship language, context-dependent violations.
Every validation decision logs the rule cited, the input data, the outcome, and the reviewer if any. Retained for six to seven years for regulator defense.
This is the data-layer companion to the marketing compliance check — same rule libraries, enforced before records propagate downstream instead of before content publishes.
Agents that include this skill
Skills live inside agent rentals. To get this skill in production, hire any of the agents below — context-tuning at onboarding is included in the first month.
Master Record Canonicalization Agent
Ingests every operator source system, resolves per-location fact conflicts, and emits the canonical master record downstream agents consume.
FAQ
- What does the validation actually check?
- Every location record gets checked against the rules that apply to your industry before it goes live. Healthcare records check HIPAA fields. records confirm license status within window. Alcohol records check ABV labeling. Supplement records check FDA metadata. Financial services records check FINRA disclosure language.
- How is this different from Great Expectations or Soda.io?
- Those tools validate any data shape if you write the rules. Your team has to maintain HIPAA, FDA, FINRA, and rules from scratch. This ships those rule libraries already built and maintained.
- Is this the same as the marketing compliance check?
- Closely related, different layer. This validates the underlying data record before it goes live downstream. The marketing compliance check validates AI-generated content before it publishes. Same rule libraries, different enforcement points.
- Which industries are supported at launch?
- HIPAA for healthcare, FTC ad-substantiation, FINRA and SEC for financial services, alcohol, state pharmacy boards, state lottery, FDA supplements. New verticals get added as customer demand drives them.
- What happens when validation fails?
- The record does not propagate downstream. The failure routes to your compliance officer with the rule cited and source data shown. The record stays in staging until corrected or explicitly overridden with documented rationale.
- Can a single record be checked against multiple frameworks?
- Yes. A pharmacy in California that sells supplements gets HIPAA plus state pharmacy board plus plus FDA supplement rules applied at the same time. A record has to pass every rule that applies to it.
- How does this fit with our data ingestion?
- Ingestion pulls data from each source. Validation checks each source contribution against your industry rules. Reconciliation resolves cross-source disagreements. Your master record then propagates the changes downstream.