I. The Moment: PFAS as Data Infrastructure

The EPA’s revised PFAS rule under TSCA §8(a)(7) isn’t just another environmental regulation. It is a blueprint for the next generation of data governance in public infrastructure.

Every manufacturer or importer that handled PFAS between 2011 and 2022 must now report granular data—chemical identities, production volumes, uses, disposal, and exposure pathways.

This rule creates the largest-ever national dataset on chemical flows, and it’s being refined in real time through collaboration between the EPA and the Office of Management and Budget (OMB).
That partnership—environmental policy aligning with data governance—is the future model of federal-state coordination.

II. The Meaning: Why This Matters to Us

Every new regulation—PFAS today, AI governance tomorrow—is an information pipeline.
When properly captured, structured, and governed, that pipeline becomes the fuel for:

  • Compliance automation
  • Risk modeling
  • Infrastructure funding optimization
  • Public trust through transparent data

For us, PFAS is not a chemical problem; it is a data readiness opportunity.

It allows us how to treat regulation as structured data—how to normalize, trace, govern, and operationalize it.

III. The Framework: NIDD → Provenance → GRC → Projects

Every dataset, regardless of origin, must pass through our four-stage system:

Raw Submissions (EPA CDX)
        ↓
NIDD Normalization (common data dictionary)
        ↓
Provenance (trace and verify data origin)
        ↓
GRC Monitoring (governance, risk, compliance)
        ↓
Projects Layer (AI models, permitting, funding)

This flow ensures that regulation → data → action becomes a single continuum.
No project, dashboard, or AI agent should operate outside this loop.

IV. The Expansion: Regulation as a Living Pipeline

We are now formalizing a permanent Regulatory Intelligence R&D Pipeline.

Three Core Pipelines

  1. AI & Data Governance Regulations — OMB, NIST, ISO 42001
  2. Environmental & Infrastructure Regulations — EPA, DOE, DOT, FDEP
  3. PFAS & Chemical Rules — TSCA, CERCLA, SDWA

Each pipeline will feed our NIDD, Provenance, and GRC layers, creating a live national regulatory map that evolves with each federal or state update.

V. The System: GRC as the Guardian Layer

GRC isn’t bureaucracy—it’s the trust layer.
It verifies every incoming data element, measures compliance, and assigns risk.
It is how we turn information into proof and accountability.

Sample GRC Schema (PFAS Context)

Field Source Description Owner Risk Level Trigger
rule_id OIRA docket Unique identifier Data Ops Medium New docket issued
agency EPA / OMB Origin of rule NIDD — —
status Proposed / Final Lifecycle state Compliance High Status change
affected_entities Utilities / Manufacturers / Local Govs Target groups Governance High Cross-match
impact_score Derived Magnitude of impact GRC High > Threshold = Alert
action_plan Internal link to project Ops — Pending Execution Flag

This schema transforms regulations into structured, auditable intelligence.

VI. The Mandate: Research & Development

Every rule and dataset is a R&D artifact.
PFAS is our first active test case.
We’ll apply the same logic to AI governance, OMB directives, and infrastructure funding frameworks as we have been discussing and as noted in the last article.

R&D Priorities

  • Decode EPA CDX data structure and map to NIDD fields.
  • Crosswalk TSCA 8(a)(7) with OMB M-24-10, NIST AI RMF, and FDEP codes.
  • Build Regulation Pulse Tracker to monitor EPA–OMB interactions. (GRC)
  • Map federal data to local impacts: utilities, stormwater, solid waste, manufacturers. (Discussion on tech and how?)
  • Prototype AI ingestion agents to parse and annotate regulatory PDFs. (Reg OS)

VII. The Execution: Asana-Ready Actions

# Task Owner Deliverable / Output Timeframe
1 PFAS → NIDD → GRC Pipeline Prototype Data Ops + Eng Working schema + JSON template 2 weeks
2 PFAS Data Schema (v0.1) Data Ops Supabase schema + Notion doc 1 week
3 Regulation Pulse Tracker MVP R&D n8n/Lovable workflow + Slack alerts 3 weeks
4 Cross-Agency Mapping Matrix Compliance + Data Gov TSCA ↔ OMB ↔ NIST ↔ FDEP table 3 weeks
5 Integrate Reg Feed into GRC Dashboard Eng + GRC Live “Reg Tracker” tab + impact scores 4 weeks
6 GRC Monitoring Schema (v0.2) GRC Team JSON/YAML schema + triggers 2 weeks
7 PFAS R&D Sprint Deck Policy R&D 10-slide summary deck 2 weeks
8 AI Ingestion Agent for Reg PDFs AI Team PDF parser → NIDD → GRC flags 4 weeks
9 PFAS Impact Map for Utilities + Vendors Program Team Risk-tier visualization 3 weeks
10 Monthly Regulation Intelligence Brief Comms + R&D One-page internal digest Recurring

Implementation Notes

  • Create Asana project “PFAS & Regulatory R&D Pipeline.”
  • Add all 10 tasks with subtasks for schema builds, automation, and reporting.
  • Conduct first review meeting in two weeks (items 1–3).

VIII. The Vision: Regulation to Readiness

This isn’t about one rule—it’s about creating an operating system for governance.
Each dataset, each regulation, each AI agent feeds a continuous improvement loop:

NIDD → Provenance → GRC → Projects → R&D Feedback.

Endnote


Every new federal or state regulation is now a potential data stream.
Our job is to turn that stream into structure, that structure into accountability, and that accountability into resilience.
This is how we will future-proof governance and incrementally operationalize governance
Most importantly, this is how we are actually going to practice what we preach by saying that "data is infrastructure"