AI-Driven Compliance Monitoring: How to Automate Regulatory Tracking Without Losing Your Sanity

import requests
from bs4 import BeautifulSoup
import schedule
import time
from datetime import datetime

class RegulatoryMonitor:
    def __init__(self):
        self.regulatory_sources = {
            'sec': 'https://www.sec.gov/rules',
            'fdic': 'https://www.fdic.gov/regulations',
            'gdpr': 'https://eur-lex.europa.eu/legal-content',
            # Add your relevant regulatory sources
        }
        self.change_threshold = 0.15  # 15% content change triggers alert
    
    def fetch_regulatory_updates(self, source_url):
        """
        Fetch latest regulatory content from source
        Returns: parsed content and metadata
        """
        try:
            response = requests.get(source_url, timeout=30)
            soup = BeautifulSoup(response.content, 'html.parser')
            
            # Extract text content and remove navigation/ads
            content = soup.get_text(strip=True)
            
            return {
                'content': content,
                'last_modified': response.headers.get('last-modified'),
                'content_hash': hash(content),
                'fetch_time': datetime.now()
            }
        except Exception as e:
            print(f"Error fetching {source_url}: {e}")
            return None
    
    def detect_changes(self, current_content, previous_content):
        """
        Compare content and detect significant changes
        Uses difflib for content comparison
        """
        import difflib
        
        if not previous_content:
            return True  # First time fetch
        
        # Calculate similarity ratio
        similarity = difflib.SequenceMatcher(
            None, 
            previous_content['content'], 
            current_content['content']
        ).ratio()
        
        # Return True if change exceeds threshold
        return (1 - similarity) > self.change_threshold

# Schedule monitoring every 4 hours
monitor = RegulatoryMonitor()
schedule.every(4).hours.do(monitor.scan_all_sources)

import spacy
from transformers import pipeline
import re

class ComplianceAnalyzer:
    def __init__(self):
        # Load pre-trained models for legal text analysis
        self.nlp = spacy.load("en_core_web_lg")
        self.classifier = pipeline(
            "text-classification",
            model="nlpaueb/legal-bert-base-uncased"
        )
    
    def extract_key_changes(self, regulatory_text):
        """
        Extract and categorize important changes from regulatory text
        Returns: structured change information
        """
        doc = self.nlp(regulatory_text)
        
        changes = {
            'new_requirements': [],
            'deadline_changes': [],
            'penalty_updates': [],
            'scope_modifications': []
        }
        
        # Pattern matching for common regulatory language
        patterns = {
            'requirements': r'(?i)(shall|must|required to|obligated to).{0,100}',
            'deadlines': r'(?i)(by|before|within|no later than)\s+\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4}',
            'penalties': r'(?i)(fine|penalty|violation|non-compliance).{0,50}(\$[\d,]+)',
            'scope': r'(?i)(applies to|covers|includes|excludes).{0,100}'
        }
        
        for category, pattern in patterns.items():
            matches = re.findall(pattern, regulatory_text)
            if matches:
                changes[category] = matches
        
        return changes
    
    def assess_business_impact(self, changes, business_profile):
        """
        Determine how regulatory changes impact specific business operations
        """
        impact_score = 0
        affected_areas = []
        
        # Simple impact scoring based on business profile
        for area in business_profile['regulated_activities']:
            for change_type, change_list in changes.items():
                for change in change_list:
                    if any(keyword in change.lower() for keyword in area['keywords']):
                        impact_score += area['risk_weight']
                        affected_areas.append({
                            'area': area['name'],
                            'change_type': change_type,
                            'description': change
                        })
        
        return {
            'impact_score': impact_score,
            'affected_areas': affected_areas,
            'priority': self._calculate_priority(impact_score)
        }
    
    def _calculate_priority(self, score):
        """Convert impact score to priority level"""
        if score >= 8: return "CRITICAL"
        elif score >= 5: return "HIGH"
        elif score >= 2: return "MEDIUM"
        else: return "LOW"

import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
import json
from datetime import datetime, timedelta

class ComplianceAlertSystem:
    def __init__(self, config):
        self.smtp_server = config['smtp_server']
        self.smtp_port = config['smtp_port']
        self.email_user = config['email_user']
        self.email_password = config['email_password']
        self.stakeholders = config['stakeholders']
    
    def generate_compliance_report(self, changes, impact_analysis):
        """
        Generate formatted compliance report
        Returns: HTML formatted report
        """
        report = f"""
        <html>
        <body>
            <h2>🚨 Regulatory Change Alert - {datetime.now().strftime('%Y-%m-%d')}</h2>
            
            <div style="background-color: {'#ff4444' if impact_analysis['priority'] == 'CRITICAL' else '#ffa500' if impact_analysis['priority'] == 'HIGH' else '#44ff44'}; 
                        padding: 10px; margin: 10px 0; border-radius: 5px;">
                <h3>Priority: {impact_analysis['priority']}</h3>
                <p>Impact Score: {impact_analysis['impact_score']}/10</p>
            </div>
            
            <h3>📋 Affected Business Areas:</h3>
            <ul>
        """
        
        for area in impact_analysis['affected_areas']:
            report += f"""
                <li><strong>{area['area']}</strong> - {area['change_type']}<br>
                    <em>{area['description'][:100]}...</em></li>
            """
        
        report += """
            </ul>
            
            <h3>⚡ Recommended Actions:</h3>
            <ul>
                <li>Review affected policies and procedures</li>
                <li>Assess current compliance status</li>
                <li>Update training materials if necessary</li>
                <li>Schedule legal review for high-impact changes</li>
            </ul>
            
            <p><strong>Generated by AI Compliance Monitor</strong><br>
            <em>This automated report was generated based on regulatory source monitoring and impact analysis.</em></p>
        </body>
        </html>
        """
        
        return report
    
    def send_alert(self, report, priority):
        """
        Send compliance alert to relevant stakeholders
        """
        # Determine recipient list based on priority
        recipients = self.stakeholders['all']
        if priority in ['CRITICAL', 'HIGH']:
            recipients.extend(self.stakeholders['executives'])
        
        subject = f"🚨 {priority} Compliance Alert - Regulatory Changes Detected"
        
        try:
            msg = MIMEMultipart('alternative')
            msg['Subject'] = subject
            msg['From'] = self.email_user
            msg['To'] = ', '.join(recipients)
            
            # Attach HTML report
            html_part = MIMEText(report, 'html')
            msg.attach(html_part)
            
            # Send email
            server = smtplib.SMTP(self.smtp_server, self.smtp_port)
            server.starttls()
            server.login(self.email_user, self.email_password)
            server.send_message(msg)
            server.quit()
            
            print(f"Alert sent successfully to {len(recipients)} recipients")
            
        except Exception as e:
            print(f"Failed to send alert: {e}")
    
    def create_audit_trail(self, change_event):
        """
        Create detailed audit trail for compliance tracking
        """
        audit_record = {
            'timestamp': datetime.now().isoformat(),
            'event_type': 'regulatory_change_detected',
            'source': change_event['source'],
            'change_summary': change_event['summary'],
            'impact_assessment': change_event['impact'],
            'actions_taken': change_event['actions'],
            'responsible_team': change_event['assigned_team']
        }
        
        # Store in database or compliance system
        # This creates the paper trail auditors love
        return audit_record

class ComplianceIntegration:
    def __init__(self):
        self.grc_systems = {
            'servicenow': ServiceNowConnector(),
            'archer': ArcherConnector(),
            'metricstream': MetricStreamConnector()
        }
    
    def create_compliance_tasks(self, regulatory_changes):
        """
        Automatically create tasks in GRC systems based on detected changes
        """
        for change in regulatory_changes:
            if change['priority'] in ['CRITICAL', 'HIGH']:
                # Create immediate action items
                task = {
                    'title': f"Review Regulatory Change: {change['regulation']}",
                    'description': change['summary'],
                    'due_date': datetime.now() + timedelta(days=7),
                    'assigned_to': self._get_subject_matter_expert(change['domain']),
                    'priority': change['priority']
                }
                
                # Push to GRC system
                self.grc_systems['servicenow'].create_task(task)
    
    def update_risk_registers(self, impact_analysis):
        """
        Update risk registers with new regulatory risks
        """
        for affected_area in impact_analysis['affected_areas']:
            risk_entry = {
                'risk_category': 'Regulatory Compliance',
                'risk_description': affected_area['description'],
                'likelihood': self._assess_likelihood(affected_area),
                'impact': impact_analysis['impact_score'],
                'mitigation_plan': self._generate_mitigation_plan(affected_area)
            }
            
            self.grc_systems['archer'].update_risk_register(risk_entry)

class PredictiveCompliance:
    def predict_regulatory_trends(self, historical_data):
        """
        Analyze patterns to predict upcoming regulatory focus areas
        """
        # Machine learning model to identify regulatory trends
        # Helps you prepare for changes before they're announced
        pass
    
    def compliance_gap_analysis(self, current_state, upcoming_regulations):
        """
        Identify potential compliance gaps before they become violations
        """
        # Compare current practices against predicted requirements
        # Generate proactive remediation plans
        pass