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Complete folder anatomy for all 649 cybersecurity skills + update LICENSE to Mahipal
- Add scripts/agent.py and references/api-reference.md to all remaining skills - Update all 648 LICENSE files: copyright now reads 'Mahipal' - Add implementing-security-monitoring-with-datadog (new skill with full anatomy) - All 649 skills now have: SKILL.md, LICENSE, scripts/agent.py, references/api-reference.md
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mukul975
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#!/usr/bin/env python3
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"""Agent for assessing and managing GDPR data protection compliance."""
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import json
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import csv
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import argparse
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import re
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from datetime import datetime, timedelta
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from collections import Counter, defaultdict
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GDPR_ARTICLES = {
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"Art5": "Principles of processing",
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"Art6": "Lawful basis",
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"Art13": "Information to data subject (direct collection)",
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"Art14": "Information to data subject (indirect collection)",
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"Art15": "Right of access",
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"Art17": "Right to erasure",
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"Art20": "Right to data portability",
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"Art25": "Data protection by design and by default",
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"Art30": "Records of processing activities",
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"Art32": "Security of processing",
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"Art33": "Breach notification (72h to DPA)",
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"Art34": "Breach communication to data subjects",
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"Art35": "Data Protection Impact Assessment",
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"Art44": "Cross-border transfer principles",
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}
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PII_PATTERNS = {
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"email": r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",
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"phone_eu": r"\b\+?[0-9]{1,3}[\s.-]?[0-9]{6,14}\b",
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"iban": r"\b[A-Z]{2}\d{2}[A-Z0-9]{11,30}\b",
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"ip_address": r"\b\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\b",
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"date_of_birth": r"\b\d{2}[/.-]\d{2}[/.-]\d{4}\b",
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"national_id": r"\b\d{3}-\d{2}-\d{4}\b",
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"passport": r"\b[A-Z]{1,2}\d{6,9}\b",
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}
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def scan_for_pii(file_path, max_bytes=1024 * 1024):
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"""Scan a file for GDPR-relevant personal data patterns."""
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try:
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with open(file_path, "r", encoding="utf-8", errors="ignore") as f:
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content = f.read(max_bytes)
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except (OSError, PermissionError):
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return None
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matches = {}
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for pii_type, pattern in PII_PATTERNS.items():
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found = re.findall(pattern, content)
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if found:
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matches[pii_type] = len(found)
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if not matches:
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return None
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return {"file": str(file_path), "pii_types": matches,
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"total_matches": sum(matches.values()),
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"category": classify_data_category(matches)}
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def classify_data_category(matches):
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"""Classify data into GDPR special categories."""
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if any(k in matches for k in ["national_id", "passport"]):
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return "special_category"
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if "iban" in matches:
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return "financial"
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return "standard_personal_data"
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def assess_ropa_completeness(ropa_json):
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"""Assess Records of Processing Activities (ROPA) completeness per Art 30."""
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with open(ropa_json) as f:
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ropa = json.load(f)
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required_fields = [
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"controller_name", "purposes", "data_categories", "data_subjects",
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"recipients", "transfers", "retention_periods", "security_measures",
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]
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findings = []
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activities = ropa if isinstance(ropa, list) else ropa.get("activities", [])
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for activity in activities:
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missing = [f for f in required_fields if not activity.get(f)]
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findings.append({
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"activity": activity.get("name", activity.get("purpose", "unknown")),
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"complete": len(missing) == 0,
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"missing_fields": missing,
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"compliance": "COMPLIANT" if not missing else "NON_COMPLIANT",
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})
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total = len(findings)
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compliant = sum(1 for f in findings if f["compliance"] == "COMPLIANT")
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return {
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"activities_assessed": total,
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"compliant": compliant,
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"compliance_rate": round(compliant / total * 100, 1) if total else 0,
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"details": findings,
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}
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def assess_dsr_handling(dsr_log_path):
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"""Assess Data Subject Request handling compliance."""
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with open(dsr_log_path) as f:
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requests_log = json.load(f)
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dsrs = requests_log if isinstance(requests_log, list) else requests_log.get("requests", [])
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findings = []
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for dsr in dsrs:
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received = datetime.fromisoformat(dsr.get("received_date", "2024-01-01"))
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completed = dsr.get("completed_date")
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if completed:
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completed_dt = datetime.fromisoformat(completed)
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days = (completed_dt - received).days
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else:
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days = (datetime.utcnow() - received).days
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overdue = days > 30 # GDPR requires response within one month
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findings.append({
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"request_id": dsr.get("id", ""),
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"type": dsr.get("type", dsr.get("right", "")),
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"status": dsr.get("status", "pending"),
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"days_elapsed": days,
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"overdue": overdue,
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"severity": "HIGH" if overdue else "INFO",
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})
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overdue_count = sum(1 for f in findings if f["overdue"])
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return {
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"total_requests": len(findings),
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"overdue": overdue_count,
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"by_type": dict(Counter(f["type"] for f in findings)),
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"details": findings,
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}
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def assess_breach_notification_readiness(breach_log_path):
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"""Assess breach notification compliance (Art 33/34)."""
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with open(breach_log_path) as f:
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breaches = json.load(f)
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breach_list = breaches if isinstance(breaches, list) else breaches.get("breaches", [])
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findings = []
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for breach in breach_list:
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detected = datetime.fromisoformat(breach.get("detected", "2024-01-01"))
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notified = breach.get("dpa_notified")
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if notified:
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notified_dt = datetime.fromisoformat(notified)
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hours = (notified_dt - detected).total_seconds() / 3600
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else:
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hours = None
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compliant = hours is not None and hours <= 72
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findings.append({
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"breach_id": breach.get("id", ""),
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"detected": str(detected),
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"notification_hours": round(hours, 1) if hours else None,
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"art33_compliant": compliant,
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"data_subjects_affected": breach.get("subjects_affected", 0),
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"severity": breach.get("severity", "HIGH"),
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})
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return {"total_breaches": len(findings), "details": findings,
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"art33_compliance_rate": round(
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sum(1 for f in findings if f["art33_compliant"]) / len(findings) * 100, 1
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) if findings else 0}
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def generate_art32_checklist():
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"""Generate Article 32 security measures compliance checklist."""
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return {
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"encryption": {
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"data_at_rest": {"required": True, "standard": "AES-256"},
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"data_in_transit": {"required": True, "standard": "TLS 1.2+"},
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"key_management": {"required": True, "standard": "HSM or KMS"},
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},
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"pseudonymization": {
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"tokenization": {"required": True},
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"key_separation": {"required": True},
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},
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"access_controls": {
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"rbac": {"required": True},
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"mfa": {"required": True},
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"least_privilege": {"required": True},
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"access_reviews": {"required": True, "frequency": "quarterly"},
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},
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"resilience": {
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"backup_strategy": {"required": True},
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"disaster_recovery": {"required": True, "rto": "4h", "rpo": "1h"},
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"business_continuity": {"required": True},
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},
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"testing": {
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"penetration_testing": {"required": True, "frequency": "annual"},
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"vulnerability_scanning": {"required": True, "frequency": "monthly"},
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"security_awareness": {"required": True, "frequency": "annual"},
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},
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}
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def main():
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parser = argparse.ArgumentParser(description="GDPR Data Protection Agent")
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parser.add_argument("--scan-pii", help="File or directory to scan for PII")
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parser.add_argument("--ropa", help="ROPA JSON file for completeness check")
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parser.add_argument("--dsr-log", help="Data Subject Request log JSON")
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parser.add_argument("--breach-log", help="Breach notification log JSON")
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parser.add_argument("--action", choices=["scan", "ropa", "dsr", "breach",
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"art32", "full"], default="full")
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parser.add_argument("--output", default="gdpr_compliance_report.json")
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args = parser.parse_args()
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report = {"generated_at": datetime.utcnow().isoformat(), "results": {}}
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if args.action in ("scan", "full") and args.scan_pii:
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result = scan_for_pii(args.scan_pii)
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report["results"]["pii_scan"] = result
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if result:
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print(f"[+] PII found: {result['total_matches']} matches ({result['category']})")
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else:
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print("[+] No PII detected")
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if args.action in ("ropa", "full") and args.ropa:
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result = assess_ropa_completeness(args.ropa)
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report["results"]["ropa"] = result
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print(f"[+] ROPA compliance: {result['compliance_rate']}%")
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if args.action in ("dsr", "full") and args.dsr_log:
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result = assess_dsr_handling(args.dsr_log)
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report["results"]["dsr"] = result
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print(f"[+] DSRs: {result['total_requests']} total, {result['overdue']} overdue")
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if args.action in ("breach", "full") and args.breach_log:
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result = assess_breach_notification_readiness(args.breach_log)
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report["results"]["breach"] = result
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print(f"[+] Art 33 compliance rate: {result['art33_compliance_rate']}%")
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if args.action in ("art32", "full"):
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checklist = generate_art32_checklist()
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report["results"]["art32_checklist"] = checklist
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print("[+] Article 32 security checklist generated")
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with open(args.output, "w") as f:
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json.dump(report, f, indent=2, default=str)
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print(f"[+] Report saved to {args.output}")
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if __name__ == "__main__":
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main()
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