Privacy Assessment Chain

Duration: 60 minutes Difficulty: Advanced Pattern: Sequential Chain + Governance Gate

This scenario demonstrates a privacy impact assessment workflow that flows through ethics review with formal approval gates, combining privacy and responsible AI personas.

Scenario Overview

Problem: A new user profiling feature collects behavioral data and needs a privacy impact assessment (PIA) and ethics review before launch.

Goal: Execute a four-persona privacy assessment chain that produces a PIA report, risk matrix, ethics review, and formal approval decision.

Persona Team

Persona	ID	Role	Category
Privacy Impact Assessor	PIA	Conducts the formal privacy impact assessment	privacy
Compliance Risk Manager	CRM	Evaluates regulatory compliance risks	governance
Data Ethics Officer	DEO	Reviews ethical implications	responsible_ai
AI Ethics Advisor	AEA	Advises on AI-specific ethical concerns	responsible_ai

Setup

from fcc.personas.registry import PersonaRegistry
from fcc.simulation.engine import SimulationEngine
from fcc.simulation.messages import SimulationMessage
from fcc.messaging.bus import EventBus
from fcc.messaging.events import Event, EventType

registry = PersonaRegistry.from_yaml_directory("src/fcc/data/personas")
bus = EventBus()
engine = SimulationEngine(registry=registry, mode="deterministic")

feature_description = {
    "name": "User Behavioral Profiling",
    "data_collected": [
        "browsing patterns", "click sequences", "session duration",
        "feature usage frequency", "content preferences",
    ],
    "purpose": "Personalized content recommendations",
    "retention_period": "90 days",
    "third_party_sharing": False,
}

Phase 1: Privacy Impact Assessment

The Privacy Impact Assessor conducts the formal PIA:

pia_message = SimulationMessage(
    sender="orchestrator",
    receiver="PIA",
    content=(
        f"Conduct a Privacy Impact Assessment for the following feature:\n\n"
        f"Feature: {feature_description['name']}\n"
        f"Data collected: {', '.join(feature_description['data_collected'])}\n"
        f"Purpose: {feature_description['purpose']}\n"
        f"Retention: {feature_description['retention_period']}\n"
        f"Third-party sharing: {feature_description['third_party_sharing']}\n\n"
        "Assess against GDPR Article 35 criteria:\n"
        "- Necessity and proportionality\n"
        "- Risks to data subjects\n"
        "- Safeguards and security measures\n"
        "- Data minimization compliance\n"
        "- Consent mechanism adequacy\n"
        "Produce a structured PIA report with risk ratings."
    ),
    phase="find",
)

pia_report = engine.step(pia_message)
print(f"PIA Report: {len(pia_report.content)} chars")

Phase 2: Compliance Risk Evaluation

The Compliance Risk Manager evaluates regulatory risks:

crm_message = SimulationMessage(
    sender="PIA",
    receiver="CRM",
    content=(
        f"Evaluate the regulatory compliance risks based on this PIA:\n\n"
        f"{pia_report.content[:600]}\n\n"
        "Assess compliance against:\n"
        "- GDPR (EU General Data Protection Regulation)\n"
        "- CCPA (California Consumer Privacy Act)\n"
        "- ePrivacy Directive\n"
        "- Sector-specific regulations\n\n"
        "Produce a risk matrix with likelihood, impact, and "
        "mitigation strategies for each identified risk."
    ),
    phase="critique",
)

risk_matrix = engine.step(crm_message)
print(f"Risk Matrix: {len(risk_matrix.content)} chars")

Phase 3: Ethics Review

The Data Ethics Officer reviews ethical implications:

deo_message = SimulationMessage(
    sender="CRM",
    receiver="DEO",
    content=(
        f"Review the ethical implications of the user profiling feature:\n\n"
        f"PIA findings:\n{pia_report.content[:400]}\n\n"
        f"Risk assessment:\n{risk_matrix.content[:400]}\n\n"
        "Evaluate against ethical principles:\n"
        "- Autonomy: Does the user have meaningful choice?\n"
        "- Beneficence: Does profiling serve user interests?\n"
        "- Non-maleficence: Could profiling cause harm?\n"
        "- Justice: Is profiling applied fairly across groups?\n"
        "- Transparency: Can users understand how they are profiled?\n"
        "Produce an ethics review with recommendations."
    ),
    phase="critique",
)

ethics_review = engine.step(deo_message)
print(f"Ethics Review: {len(ethics_review.content)} chars")

Phase 4: AI Ethics Advisory

The AI Ethics Advisor provides AI-specific ethical guidance:

aea_message = SimulationMessage(
    sender="DEO",
    receiver="AEA",
    content=(
        f"Provide AI-specific ethical guidance for the profiling feature:\n\n"
        f"Ethics review:\n{ethics_review.content[:400]}\n\n"
        "Focus on:\n"
        "- Algorithmic bias in behavioral profiling\n"
        "- Filter bubble and echo chamber risks\n"
        "- Manipulation through persuasive design\n"
        "- Explainability of profiling decisions\n"
        "- Right to not be profiled\n"
        "Produce actionable recommendations with implementation priority."
    ),
    phase="critique",
)

ai_ethics_advisory = engine.step(aea_message)
print(f"AI Ethics Advisory: {len(ai_ethics_advisory.content)} chars")

Approval Gate

Aggregate all assessments and make a formal approval decision:

from fcc.collaboration.scoring import ScoringEngine
from fcc.collaboration.models import ApprovalGate, ApprovalStatus

scorer = ScoringEngine()

assessment_scores = {
    "privacy_impact": scorer.score_text(pia_report.content),
    "compliance_risk": scorer.score_text(risk_matrix.content),
    "ethics_review": scorer.score_text(ethics_review.content),
    "ai_ethics": scorer.score_text(ai_ethics_advisory.content),
}

overall = sum(assessment_scores.values()) / len(assessment_scores)

print("\nAssessment Scores:")
for area, score in assessment_scores.items():
    print(f"  {area}: {score:.2f}")
print(f"  Overall: {overall:.2f}")

# Determine approval status
# All areas must be above 0.5, and overall must be above 0.6
all_above_minimum = all(s >= 0.5 for s in assessment_scores.values())
status = (
    ApprovalStatus.APPROVED
    if all_above_minimum and overall >= 0.6
    else ApprovalStatus.REJECTED
)

gate = ApprovalGate(
    gate_id="privacy_ethics_gate",
    gate_name="Privacy and Ethics Approval",
    required_approvers=("PIA", "DEO"),
    status=status,
)

print(f"\nApproval Gate: {gate.status.value}")

bus.publish(Event(
    event_type=EventType.COLLABORATION_GATE_DECIDED,
    source="privacy_assessment_chain",
    payload={
        "gate_id": gate.gate_id,
        "status": gate.status.value,
        "scores": assessment_scores,
        "overall": overall,
    },
))

Final Report

import json

final_report = {
    "feature": feature_description["name"],
    "assessment_chain": ["PIA", "CRM", "DEO", "AEA"],
    "scores": assessment_scores,
    "overall_score": overall,
    "approval_status": gate.status.value,
    "artifacts": {
        "pia_report_length": len(pia_report.content),
        "risk_matrix_length": len(risk_matrix.content),
        "ethics_review_length": len(ethics_review.content),
        "ai_ethics_advisory_length": len(ai_ethics_advisory.content),
    },
}

print("\nFinal Report:")
print(json.dumps(final_report, indent=2))

Exercises

1. Conditional paths: If the PIA identifies high risks, skip directly to AEA for urgent review before CRM assessment.
2. Remediation loop: When the gate rejects, send specific findings back to the development team and re-assess after changes.
3. Knowledge graph: Build a knowledge graph from the assessment chain showing relationships between risks, controls, and recommendations.
4. Cross-project: Use federation to compare privacy standards across partner organizations.

Summary

In this scenario you executed a privacy assessment chain:

• PIA conducted a formal privacy impact assessment
• CRM evaluated regulatory compliance risks with a risk matrix
• DEO reviewed ethical implications against five principles
• AEA provided AI-specific ethical guidance
• An approval gate aggregated scores and made a formal decision

Next Steps

• Governance Audit Flow -- Broader compliance auditing
• Cross-Project Federation -- Cross-org privacy standards