# WIA-AI-018 Specification - Phase 1: Foundation

## Overview

Phase 1 establishes the foundational infrastructure for AI decision audit systems. This phase focuses on core logging capabilities, basic data schemas, and storage infrastructure.

**Status**: ✅ Stable
**Version**: 1.0
**Last Updated**: 2025-01-15

## Philosophy

弘益人間 (Hongik Ingan) - Benefit All Humanity

AI decision audit systems must serve all stakeholders: regulators, affected individuals, data scientists, and society at large. Phase 1 creates the foundation for transparent, accountable AI.

## Core Requirements

### 1. Decision Logging Schema

Every AI decision must be logged with the following required fields:

```typescript
interface DecisionAuditLog {
  // Unique identification
  decision_id: string;           // UUID or similar
  correlation_id?: string;       // Links related decisions

  // Temporal information
  timestamp: string;             // ISO 8601 format
  timezone: string;              // IANA timezone
  processing_duration_ms: number;

  // System information
  system: {
    name: string;
    version: string;
    environment: "production" | "staging" | "development";
    deployment_id: string;
  };

  // Model information
  model: {
    name: string;
    version: string;
    type: "neural_network" | "decision_tree" | "ensemble" | "rule_based" | "other";
    training_date: string;
    training_dataset_id?: string;
  };

  // Input data
  input: {
    raw_data: any;
    preprocessed_data?: any;
    feature_vector?: number[];
    data_sources: string[];
  };

  // Decision output
  output: {
    decision: any;
    confidence: number;           // 0.0 to 1.0
    alternatives?: Array<{
      decision: any;
      confidence: number;
    }>;
    flags: string[];              // "high_risk", "low_confidence", etc.
  };

  // Context
  context: {
    user_id?: string;             // Pseudonymized
    session_id?: string;
    request_id?: string;
    geographic_location?: string;
    decision_type: string;
    business_impact: "low" | "medium" | "high" | "critical";
  };

  // Audit metadata
  audit: {
    log_version: string;
    integrity_hash: string;
    previous_hash?: string;       // For hash chaining
    signature?: string;           // Cryptographic signature
  };
}
```

### 2. Storage Requirements

#### 2.1 Database Schema

Minimum database requirements:

```sql
CREATE TABLE audit_logs (
  decision_id VARCHAR(64) PRIMARY KEY,
  created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
  model_name VARCHAR(255) NOT NULL,
  model_version VARCHAR(32) NOT NULL,
  decision_type VARCHAR(64) NOT NULL,
  business_impact VARCHAR(16) NOT NULL,
  confidence DECIMAL(5,4),
  input_data JSONB NOT NULL,
  output_data JSONB NOT NULL,
  context JSONB,
  audit_metadata JSONB NOT NULL,
  hash VARCHAR(64) NOT NULL,
  previous_hash VARCHAR(64)
);

-- Required indexes
CREATE INDEX idx_audit_logs_created_at ON audit_logs(created_at);
CREATE INDEX idx_audit_logs_model ON audit_logs(model_name, model_version);
CREATE INDEX idx_audit_logs_decision_type ON audit_logs(decision_type);
CREATE INDEX idx_audit_logs_impact ON audit_logs(business_impact);

-- Prevent updates and deletes (append-only)
CREATE OR REPLACE RULE audit_logs_no_update AS
  ON UPDATE TO audit_logs
  DO INSTEAD NOTHING;

CREATE OR REPLACE RULE audit_logs_no_delete AS
  ON DELETE TO audit_logs
  DO INSTEAD NOTHING;
```

#### 2.2 Storage Tiers

- **Hot storage**: Last 90 days - SSD/fast access
- **Warm storage**: 90 days to 2 years - Standard storage
- **Cold storage**: 2-7 years - Archive storage (S3 Glacier, etc.)

### 3. Hash Chaining for Integrity

Implement cryptographic hash chaining to ensure tamper evidence:

```typescript
function calculateHash(log: DecisionAuditLog): string {
  const content = JSON.stringify({
    decision_id: log.decision_id,
    timestamp: log.timestamp,
    model: log.model,
    input: log.input,
    output: log.output,
    context: log.context,
    previous_hash: log.audit.previous_hash || '0'
  });

  return crypto
    .createHash('sha256')
    .update(content)
    .digest('hex');
}
```

### 4. Logging API Endpoints

Minimum required API endpoints:

```
POST   /api/v1/audit/log           - Log a decision
GET    /api/v1/audit/log/{id}      - Retrieve specific log
GET    /api/v1/audit/logs          - Query logs (with filters)
GET    /api/v1/audit/verify        - Verify hash chain integrity
GET    /api/v1/audit/health        - System health check
```

### 5. Performance Requirements

- **Asynchronous logging**: Must not block decision delivery
- **Latency**: < 100ms for async log submission
- **Throughput**: Support 1000+ decisions/second
- **Availability**: 99.9% uptime

### 6. Privacy and Security

#### 6.1 Data Protection

- Encrypt sensitive fields at rest
- Pseudonymize personal identifiers
- Implement role-based access control
- Audit all access to audit logs

#### 6.2 Retention Policy

- Minimum retention: As required by applicable regulations
- Default retention: 7 years
- Legal hold support: Indefinite retention when required

## Implementation Checklist

- [ ] Deploy logging service infrastructure
- [ ] Implement decision schema
- [ ] Set up database with required tables and indexes
- [ ] Implement hash chaining
- [ ] Configure storage tiers
- [ ] Deploy API endpoints
- [ ] Implement access controls
- [ ] Set up encryption at rest
- [ ] Configure backup and disaster recovery
- [ ] Document system architecture
- [ ] Establish monitoring and alerting
- [ ] Complete load testing

## Compliance Mapping

### GDPR
- Schema supports right to explanation (Article 22)
- Pseudonymization protects personal data (Article 25)
- Retention policy supports storage limitation (Article 5)

### CCPA
- Schema supports disclosure requirements
- Access controls enable consumer data requests

### EU AI Act
- Logging supports high-risk system requirements
- Hash chaining provides integrity verification

## Testing Requirements

1. **Unit tests**: All logging functions
2. **Integration tests**: End-to-end logging flow
3. **Performance tests**: 1000+ decisions/second
4. **Security tests**: Encryption, access control
5. **Integrity tests**: Hash chain verification

## Migration Path

For existing systems:

1. Deploy audit infrastructure in parallel
2. Add logging to new decisions first
3. Backfill historical data if required
4. Gradually expand coverage
5. Enforce 100% coverage for high-risk decisions

## Success Metrics

- **Coverage**: % of decisions logged
- **Latency**: Average time to persist log
- **Completeness**: % of logs with all required fields
- **Integrity**: Hash chain verification success rate
- **Availability**: System uptime %

## Next Steps

After completing Phase 1:

- **Phase 2**: Add compliance checking and risk assessment
- **Phase 3**: Implement bias detection and drift monitoring
- **Phase 4**: Deploy federation and ecosystem integration

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弘益人間 · Benefit All Humanity