""" NIST Privacy Framework IDENTIFY Function — Assessment Processor Automates the privacy risk identification process including data action inventorying, problematic data action screening, and risk scoring aligned with NIST Privacy Framework subcategories ID.BE, ID.DA, ID.IM, and ID.RA. """ from dataclasses import dataclass, field from datetime import datetime, timezone from enum import Enum from typing import Optional import json import uuid class RiskLevel(Enum): LOW = "low" MEDIUM = "medium" HIGH = "high" CRITICAL = "critical" class ResponseStrategy(Enum): MITIGATE = "mitigate" TRANSFER = "transfer" AVOID = "avoid" ACCEPT = "accept" class OrganizationalRole(Enum): CONTROLLER = "controller" PROCESSOR = "processor" JOINT_CONTROLLER = "joint_controller" PROBLEMATIC_DATA_ACTIONS = [ "appropriation", "distortion", "induced_disclosure", "insecurity", "re-identification", "stigmatization", "surveillance", "unanticipated_revelation", "unwarranted_restriction", ] @dataclass class DataAction: action_id: str name: str description: str data_categories: list[str] data_subjects: list[str] systems: list[str] owner: str legal_basis: str retention_period: str organizational_role: OrganizationalRole third_party_sharing: bool = False cross_border_transfer: bool = False @dataclass class ProblematicDataAction: pda_type: str description: str affected_data_action: str likelihood: int # 1-5 impact: int # 1-5 risk_score: int = 0 risk_level: RiskLevel = RiskLevel.LOW def __post_init__(self): self.risk_score = self.likelihood * self.impact if self.risk_score >= 20: self.risk_level = RiskLevel.CRITICAL elif self.risk_score >= 12: self.risk_level = RiskLevel.HIGH elif self.risk_score >= 6: self.risk_level = RiskLevel.MEDIUM else: self.risk_level = RiskLevel.LOW @dataclass class RiskResponse: risk_id: str strategy: ResponseStrategy controls: list[str] owner: str deadline: datetime residual_risk_level: RiskLevel rationale: str @dataclass class IdentifyAssessment: assessment_id: str organization: str scope: str assessed_by: str assessment_date: datetime data_actions: list[DataAction] = field(default_factory=list) problematic_data_actions: list[ProblematicDataAction] = field(default_factory=list) risk_responses: list[RiskResponse] = field(default_factory=list) class IdentifyFunctionProcessor: """ Process NIST Privacy Framework IDENTIFY function assessments. Automates: - Data action inventory compilation (ID.DA) - Problematic data action screening (ID.RA-P1) - Risk scoring and prioritization (ID.RA-P3) - Business environment analysis (ID.BE) """ def __init__(self, organization: str): self.organization = organization def create_assessment(self, scope: str, assessed_by: str) -> IdentifyAssessment: """Create a new IDENTIFY function assessment.""" return IdentifyAssessment( assessment_id=str(uuid.uuid4()), organization=self.organization, scope=scope, assessed_by=assessed_by, assessment_date=datetime.now(timezone.utc), ) def add_data_action( self, assessment: IdentifyAssessment, name: str, description: str, data_categories: list[str], data_subjects: list[str], systems: list[str], owner: str, legal_basis: str, retention_period: str, role: OrganizationalRole, third_party_sharing: bool = False, cross_border_transfer: bool = False, ) -> DataAction: """Add a data action to the assessment inventory (ID.DA-P1).""" action = DataAction( action_id=str(uuid.uuid4()), name=name, description=description, data_categories=data_categories, data_subjects=data_subjects, systems=systems, owner=owner, legal_basis=legal_basis, retention_period=retention_period, organizational_role=role, third_party_sharing=third_party_sharing, cross_border_transfer=cross_border_transfer, ) assessment.data_actions.append(action) return action def screen_problematic_data_actions( self, assessment: IdentifyAssessment ) -> list[ProblematicDataAction]: """ Screen data actions for problematic data actions (ID.RA-P1). Evaluates each data action against the NIST catalog of problematic data actions and assigns initial risk scores. """ findings = [] for action in assessment.data_actions: # Check for re-identification risk if any( cat in action.data_categories for cat in ["pseudonymized_data", "anonymized_data", "aggregated_data"] ): pda = ProblematicDataAction( pda_type="re-identification", description=f"Risk of re-identification in '{action.name}' processing pseudonymized/anonymized data", affected_data_action=action.action_id, likelihood=3, impact=4, ) findings.append(pda) # Check for surveillance risk if any( cat in action.data_categories for cat in ["location_data", "device_data", "browsing_history", "biometric_data"] ): pda = ProblematicDataAction( pda_type="surveillance", description=f"Surveillance risk in '{action.name}' processing tracking-capable data", affected_data_action=action.action_id, likelihood=3, impact=4, ) findings.append(pda) # Check for insecurity risk based on third-party sharing if action.third_party_sharing: pda = ProblematicDataAction( pda_type="insecurity", description=f"Insecurity risk from third-party data sharing in '{action.name}'", affected_data_action=action.action_id, likelihood=3, impact=3, ) findings.append(pda) # Check for unanticipated revelation in cross-border transfers if action.cross_border_transfer: pda = ProblematicDataAction( pda_type="unanticipated_revelation", description=f"Risk of unanticipated data exposure from cross-border transfer in '{action.name}'", affected_data_action=action.action_id, likelihood=2, impact=4, ) findings.append(pda) # Check for induced disclosure from data collection if any( cat in action.data_categories for cat in ["health_data", "financial_data", "genetic_data", "biometric_data"] ): pda = ProblematicDataAction( pda_type="induced_disclosure", description=f"Risk of induced disclosure of sensitive data in '{action.name}'", affected_data_action=action.action_id, likelihood=2, impact=5, ) findings.append(pda) assessment.problematic_data_actions = findings return findings def prioritize_risks( self, assessment: IdentifyAssessment ) -> list[ProblematicDataAction]: """Prioritize identified risks by score (ID.RA-P3).""" return sorted( assessment.problematic_data_actions, key=lambda p: p.risk_score, reverse=True, ) def add_risk_response( self, assessment: IdentifyAssessment, pda: ProblematicDataAction, strategy: ResponseStrategy, controls: list[str], owner: str, deadline: datetime, residual_risk: RiskLevel, rationale: str, ) -> RiskResponse: """Add a risk response for a problematic data action (ID.RA-P3).""" response = RiskResponse( risk_id=str(uuid.uuid4()), strategy=strategy, controls=controls, owner=owner, deadline=deadline, residual_risk_level=residual_risk, rationale=rationale, ) assessment.risk_responses.append(response) return response def generate_report(self, assessment: IdentifyAssessment) -> dict: """Generate the IDENTIFY function assessment report.""" prioritized = self.prioritize_risks(assessment) return { "assessment_id": assessment.assessment_id, "organization": assessment.organization, "scope": assessment.scope, "assessed_by": assessment.assessed_by, "date": assessment.assessment_date.isoformat(), "summary": { "total_data_actions": len(assessment.data_actions), "total_problematic_data_actions": len(assessment.problematic_data_actions), "risk_distribution": { "critical": sum(1 for p in prioritized if p.risk_level == RiskLevel.CRITICAL), "high": sum(1 for p in prioritized if p.risk_level == RiskLevel.HIGH), "medium": sum(1 for p in prioritized if p.risk_level == RiskLevel.MEDIUM), "low": sum(1 for p in prioritized if p.risk_level == RiskLevel.LOW), }, "total_risk_responses": len(assessment.risk_responses), }, "data_actions": [ { "id": da.action_id, "name": da.name, "owner": da.owner, "role": da.organizational_role.value, "data_categories": da.data_categories, "third_party_sharing": da.third_party_sharing, "cross_border_transfer": da.cross_border_transfer, } for da in assessment.data_actions ], "prioritized_risks": [ { "type": p.pda_type, "description": p.description, "risk_score": p.risk_score, "risk_level": p.risk_level.value, "likelihood": p.likelihood, "impact": p.impact, } for p in prioritized ], } if __name__ == "__main__": processor = IdentifyFunctionProcessor("Cipher Engineering Labs") assessment = processor.create_assessment( scope="Privacy Analytics Platform", assessed_by="Privacy Engineering Team", ) processor.add_data_action( assessment, name="Customer profile data collection", description="Collection of customer profile information during account registration", data_categories=["contact_information", "professional_information"], data_subjects=["customers"], systems=["registration-service", "customer-db"], owner="Product Team", legal_basis="consent", retention_period="Duration of account + 3 years", role=OrganizationalRole.CONTROLLER, ) processor.add_data_action( assessment, name="Usage analytics processing", description="Processing of platform usage data for product improvement", data_categories=["usage_data", "device_data", "browsing_history"], data_subjects=["customers"], systems=["analytics-pipeline", "data-warehouse"], owner="Data Team", legal_basis="legitimate_interest", retention_period="26 months", role=OrganizationalRole.CONTROLLER, third_party_sharing=True, ) findings = processor.screen_problematic_data_actions(assessment) report = processor.generate_report(assessment) print(json.dumps(report, indent=2))