Performing Privacy Impact Assessment

# Run the risk assessment
risk_report = engine.assess_privacy_risks(
    activity_id=activity["activity_id"],
    assessment_type="full_dpia",
)

# Display risk matrix results
for risk in risk_report["risks"]:
    print(f"[{risk['severity']}] {risk['category']}: {risk['description']}")
    print(f"  Likelihood: {risk['likelihood']}/5 | Impact: {risk['impact']}/5 | Score: {risk['risk_score']}/25")
    print(f"  Mitigation: {risk['recommended_mitigation']}")

# GDPR compliance check
gdpr_report = engine.check_gdpr_compliance(activity_id=activity["activity_id"])
print(f"GDPR Score: {gdpr_report['compliance_score']}/100")
for finding in gdpr_report["findings"]:
    print(f"  [{finding['status']}] Art.{finding['article']}: {finding['description']}")

# CCPA/CPRA compliance check
ccpa_report = engine.check_ccpa_compliance(activity_id=activity["activity_id"])
print(f"CCPA Score: {ccpa_report['compliance_score']}/100")
for finding in ccpa_report["findings"]:
    print(f"  [{finding['status']}] Sec.{finding['section']}: {finding['description']}")

# Generate remediation plan
remediation = engine.generate_remediation_plan(
    activity_id=activity["activity_id"],
    risk_report=risk_report,
    gdpr_report=gdpr_report,
    ccpa_report=ccpa_report,
)

for item in remediation["action_items"]:
    print(f"[{item['priority']}] {item['action']}")
    print(f"  Owner: {item['owner']} | Deadline: {item['deadline']}")
    print(f"  Addresses: {', '.join(item['addresses_risks'])}")

# Generate formal DPIA report
engine.generate_dpia_report(
    activity_id=activity["activity_id"],
    output_path="dpia_report_customer_analytics.json",
    format="json",
)
print("[+] DPIA report generated")

engine = PrivacyImpactAssessmentEngine()

screening = engine.run_screening_checklist(
    uses_special_category_data=False,
    large_scale_processing=True,
    systematic_monitoring=True,
    automated_decision_making=True,
    cross_border_transfer=True,
    vulnerable_data_subjects=False,
    innovative_technology=True,
    denial_of_service_or_rights=False,
)
print(f"DPIA Required: {screening['dpia_required']}")
print(f"Triggers: {screening['triggers']}")
# Output: DPIA Required: True
# Triggers: ['large_scale_processing', 'systematic_monitoring',
#            'automated_decision_making', 'cross_border_transfer',
#            'innovative_technology']

engine = PrivacyImpactAssessmentEngine()

activities = [
    {"name": "Email Marketing", "data_categories": ["email", "name"],
     "legal_basis": "consent", "cross_border_transfer": False},
    {"name": "HR Analytics", "data_categories": ["employee_id", "performance_scores",
     "health_data"], "legal_basis": "legitimate_interest", "cross_border_transfer": True},
    {"name": "Fraud Detection", "data_categories": ["transaction_data", "ip_address",
     "device_fingerprint"], "legal_basis": "legitimate_interest",
     "automated_decision_making": True, "cross_border_transfer": False},
]

for act_def in activities:
    activity = engine.register_processing_activity(**act_def)
    risk = engine.assess_privacy_risks(activity_id=activity["activity_id"])
    print(f"{act_def['name']}: Overall Risk={risk['overall_risk_level']} "
          f"({risk['risk_count_by_severity']})")

engine = PrivacyImpactAssessmentEngine()

profile = engine.generate_nist_privacy_profile(
    activity_id=activity["activity_id"],
    target_tier="tier_3",  # Repeatable
)

for function_id, outcomes in profile["functions"].items():
    print(f"\n{function_id}:")
    for outcome in outcomes:
        status = "PASS" if outcome["implemented"] else "GAP"
        print(f"  [{status}] {outcome['subcategory']}: {outcome['description']}")