Regulatory Compliance Guide

Regulatory Compliance Guide

A skill for mining regulatory texts, tracking regulatory changes, and conducting compliance research. Covers accessing regulatory databases, parsing regulatory language, change detection in regulations, compliance gap analysis, and computational policy analysis.

Regulatory Data Sources

US Federal Regulatory Data

Accessing Federal Register Data

import requests
from datetime import date, timedelta

class FederalRegisterClient:
    """Client for the Federal Register API."""

    BASE_URL = "https://www.federalregister.gov/api/v1"

    def search_rules(self, query: str, agency: str = None,
                     date_from: str = None, per_page: int = 20) -> dict:
        """
        Search for rules and proposed rules in the Federal Register.
        """
        params = {
            "conditions[term]": query,
            "conditions[type][]": ["RULE", "PRORULE"],
            "per_page": per_page,
            "order": "newest",
        }
        if agency:
            params["conditions[agencies][]"] = agency
        if date_from:
            params["conditions[publication_date][gte]"] = date_from

        resp = requests.get(f"{self.BASE_URL}/documents", params=params)
        data = resp.json()
        return {
            "count": data.get("count", 0),
            "results": [
                {
                    "title": r["title"],
                    "document_number": r["document_number"],
                    "publication_date": r["publication_date"],
                    "agency_names": r.get("agency_names", []),
                    "type": r["type"],
                    "abstract": r.get("abstract", ""),
                    "html_url": r["html_url"],
                }
                for r in data.get("results", [])
            ],
        }

    def get_document(self, document_number: str) -> dict:
        """Retrieve full document details by document number."""
        resp = requests.get(
            f"{self.BASE_URL}/documents/{document_number}.json"
        )
        return resp.json()

Regulatory Text Parsing

Identifying Regulatory Obligations

Regulatory language follows predictable patterns that indicate obligation strength:

import re
from enum import Enum

class ObligationLevel(Enum):
    MANDATORY = "mandatory"       # shall, must, required
    PROHIBITIVE = "prohibitive"   # shall not, must not, prohibited
    PERMISSIVE = "permissive"     # may, is permitted
    RECOMMENDED = "recommended"   # should, is recommended
    INFORMATIVE = "informative"   # for information, note

OBLIGATION_PATTERNS = {
    ObligationLevel.MANDATORY: [
        r"\bshall\b(?!\s+not)", r"\bmust\b(?!\s+not)",
        r"\bis required to\b", r"\bare required to\b",
    ],
    ObligationLevel.PROHIBITIVE: [
        r"\bshall not\b", r"\bmust not\b",
        r"\bis prohibited\b", r"\bmay not\b",
    ],
    ObligationLevel.PERMISSIVE: [
        r"\bmay\b(?!\s+not)", r"\bis permitted\b",
        r"\bis authorized\b",
    ],
    ObligationLevel.RECOMMENDED: [
        r"\bshould\b(?!\s+not)", r"\bis recommended\b",
        r"\bit is advisable\b",
    ],
}

def classify_obligations(text: str) -> list[dict]:
    """
    Extract and classify regulatory obligations from text.
    Returns sentences tagged with their obligation level.
    """
    sentences = re.split(r'(?<=[.!?])\s+', text)
    results = []
    for sent in sentences:
        level = ObligationLevel.INFORMATIVE
        for obl_level, patterns in OBLIGATION_PATTERNS.items():
            if any(re.search(p, sent, re.IGNORECASE) for p in patterns):
                level = obl_level
                break
        results.append({"sentence": sent.strip(), "obligation": level.value})
    return results

CFR Section Parsing

def parse_cfr_section(xml_text: str) -> dict:
    """
    Parse an eCFR XML section into structured components.
    Extracts the section number, heading, paragraphs, and cross-references.
    """
    root = ET.fromstring(xml_text)
    section = {
        "number": root.findtext(".//SECTNO", ""),
        "heading": root.findtext(".//SUBJECT", ""),
        "paragraphs": [],
        "cross_references": [],
    }

    for para in root.iter("P"):
        text = "".join(para.itertext()).strip()
        if text:
            section["paragraphs"].append(text)
            # Extract cross-references to other CFR sections
            xrefs = re.findall(r"\d+\s+CFR\s+[\d.]+(?:\([a-z]\))?", text)
            section["cross_references"].extend(xrefs)

    return section

Regulatory Change Detection

Tracking Amendments Over Time

from difflib import SequenceMatcher, unified_diff

def compare_regulation_versions(old_text: str, new_text: str,
                                  section_id: str) -> dict:
    """
    Compare two versions of a regulation section to identify changes.
    Returns a structured diff with change classification.
    """
    old_lines = old_text.splitlines(keepends=True)
    new_lines = new_text.splitlines(keepends=True)

    diff = list(unified_diff(old_lines, new_lines,
                              fromfile=f"{section_id} (old)",
                              tofile=f"{section_id} (new)"))

    additions = sum(1 for l in diff if l.startswith("+") and not l.startswith("+++"))
    deletions = sum(1 for l in diff if l.startswith("-") and not l.startswith("---"))

    similarity = SequenceMatcher(None, old_text, new_text).ratio()

    return {
        "section": section_id,
        "similarity": round(similarity, 4),
        "lines_added": additions,
        "lines_removed": deletions,
        "change_magnitude": "major" if similarity < 0.8 else
                           "minor" if similarity < 0.95 else "trivial",
        "diff": "".join(diff),
    }

Compliance Gap Analysis

Mapping Requirements to Controls

def compliance_gap_analysis(requirements: list[dict],
                             controls: list[dict]) -> pd.DataFrame:
    """
    Map regulatory requirements to organizational controls.
    Identify gaps where requirements lack corresponding controls.

    requirements: [{id, text, obligation_level, cfr_section}]
    controls: [{id, description, implemented, evidence}]
    """
    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.metrics.pairwise import cosine_similarity

    req_texts = [r["text"] for r in requirements]
    ctrl_texts = [c["description"] for c in controls]

    vectorizer = TfidfVectorizer(stop_words="english", max_features=5000)
    all_texts = req_texts + ctrl_texts
    tfidf = vectorizer.fit_transform(all_texts)

    req_vecs = tfidf[:len(req_texts)]
    ctrl_vecs = tfidf[len(req_texts):]

    similarity_matrix = cosine_similarity(req_vecs, ctrl_vecs)

    gaps = []
    for i, req in enumerate(requirements):
        best_match_idx = similarity_matrix[i].argmax()
        best_score = similarity_matrix[i][best_match_idx]
        matched_ctrl = controls[best_match_idx] if best_score > 0.3 else None

        gaps.append({
            "requirement_id": req["id"],
            "cfr_section": req["cfr_section"],
            "obligation": req["obligation_level"],
            "matched_control": matched_ctrl["id"] if matched_ctrl else "NONE",
            "match_score": round(best_score, 3),
            "status": "covered" if matched_ctrl and matched_ctrl["implemented"]
                      else "gap" if not matched_ctrl
                      else "planned",
        })

    return pd.DataFrame(gaps)

Regulatory Domains

Key regulated sectors with their primary frameworks:

Tools and Resources

• RegInfo.gov: Unified Agenda of regulatory actions
• Regulations.gov API: Public comments on proposed rules
• GovInfo.gov: Official publications of all branches of government
• LexisNexis / Westlaw: Commercial legal research platforms
• RegTech tools: Ascent, Compliance.ai, Clausematch
• spaCy + custom pipelines: NLP for regulatory text extraction