19. Privacy by Design

Personal AI agents process sensitive user data. OpenCLaw implements privacy by design principles: data minimization, purpose limitation, and user control.

Data Minimization

OpenCLaw collects only data necessary for its functions. Conversation history retention follows configurable policies with automatic expiration.

# privacy.py
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Optional

@dataclass
class RetentionPolicy:
    conversation_days: int = 90
    feedback_days: int = 180
    logs_days: int = 30
    anonymize_after: int = 365

class DataMinimizer:
    def __init__(self, policy: RetentionPolicy, storage):
        self.policy = policy
        self.storage = storage
    
    def apply_retention(self) -> int:
        deleted_count = 0
        
        cutoff = datetime.utcnow() - timedelta(days=self.policy.conversation_days)
        deleted = self.storage.delete_old_conversations(cutoff)
        deleted_count += deleted
        
        cutoff = datetime.utcnow() - timedelta(days=self.policy.feedback_days)
        deleted = self.storage.delete_old_feedback(cutoff)
        deleted_count += deleted
        
        return deleted_count
    
    def anonymize(self, user_id: str) -> None:
        self.storage.remove_identifying_info(user_id)
        self.storage.update_user_id(user_id, f"anon_{hash(user_id)}")

Purpose Limitation

Data collected for one purpose should not be used for another without consent. OpenCLaw tags data with purpose metadata and enforces usage restrictions.

class PurposeTracker:
    PURPOSES = ['core_functionality', 'improvement', 'analytics', 'sharing']
    
    def __init__(self, storage):
        self.storage = storage
    
    def tag_data(self, data_id: str, purpose: str):
        self.storage.set_purpose(data_id, purpose)
    
    def can_use_for(self, data_id: str, requested_purpose: str) -> bool:
        original_purpose = self.storage.get_purpose(data_id)
        
        if original_purpose == 'core_functionality':
            return True
        if original_purpose == requested_purpose:
            return True
        
        return False

User Control Interface

Users must have visibility into and control over their data. OpenCLaw provides a privacy dashboard showing data collection, retention, and export options.

class PrivacyDashboard:
    def __init__(self, storage, minimizer: DataMinimizer):
        self.storage = storage
        self.minimizer = minimizer
    
    def get_data_summary(self, user_id: str) -> dict:
        return {
            'conversation_count': self.storage.count_conversations(user_id),
            'oldest_conversation': self.storage.get_oldest_conversation(user_id),
            'feedback_entries': self.storage.count_feedback(user_id),
            'estimated_storage_mb': self.storage.estimate_size(user_id) / (1024*1024)
        }
    
    def export_data(self, user_id: str, format: str) -> str:
        if format == 'json':
            return self.storage.export_json(user_id)
        elif format == 'csv':
            return self.storage.export_csv(user_id)
    
    def delete_all_user_data(self, user_id: str) -> bool:
        self.storage.purge_user(user_id)
        return True