Act as a Principal QA Architect specializing in MLops and AI system validation. I need you to create a comprehensive Integration Test Plan for the following AI system:

**System Context**: [AI_SYSTEM_DESCRIPTION]
**Integration Points**: [INTEGRATION_POINTS] 
**Technology Stack**: [TECH_STACK]
**Compliance & Security Requirements**: [COMPLIANCE_REQUIREMENTS]
**Testing Scope**: [TEST_SCOPE] (e.g., end-to-end, API-only, model-serving layer, data pipeline)
**Non-Functional Requirements**: [NFR_REQUIREMENTS] (latency, throughput, availability SLAs)

Develop a detailed test plan that includes:

## 1. Integration Architecture Risk Mapping
- Identify all API contracts, data schemas, and serialization formats between components
- Map the complete data lineage from ingestion → feature engineering → model inference → output consumption
- Highlight probabilistic vs. deterministic boundaries where integration failures commonly occur
- Document schema versioning conflicts between model artifacts and consumer applications

## 2. Critical Test Scenario Categories
**A. Data Pipeline Integration**
- Feature store synchronization tests
- Training-serving skew detection
- Data drift impact on downstream APIs

**B. Model Serving Layer**
- A/B testing infrastructure validation
- Canary deployment verification
- Model versioning rollback scenarios
- Batch vs. Real-time inference consistency

**C. Downstream Consumer Integration**
- Webhook delivery reliability for async predictions
- Event streaming (Kafka/SQS) message durability
- Client SDK backward compatibility

**D. Resilience & Degradation**
- Circuit breaker activation when ML service times out
- Fallback to cached predictions or rule-based systems
- Graceful handling of model confidence thresholds below minimum thresholds

## 3. Detailed Test Case Specifications
Provide 6-8 concrete test cases including:
- Test ID and objective
- Pre-conditions (model state, data availability)
- Step-by-step execution flow
- Expected results (including acceptable prediction variance ranges)
- Validation criteria (assertions for both technical contracts and business logic)
- Edge cases: malformed model outputs, schema evolution conflicts, timeout cascades

## 4. Test Data & Environment Strategy
- Synthetic data generation requirements for integration testing (preserve statistical distributions)
- PII/PHI masking strategies for production-like test environments
- Golden dataset maintenance for regression testing across model versions
- Shadow testing configuration for safe production validation

## 5. Automation Framework Architecture
- Recommended tools: Contract testing (Pact), API testing (Postman/Newman), Data validation (Great Expectations)
- CI/CD pipeline integration points (pre-deployment validation gates)
- Infrastructure-as-Code testing for ML serving environments
- Automated rollback triggers based on integration health metrics

## 6. Observability & Monitoring Validation
- Distributed tracing verification across the ML pipeline (Jaeger/Zipkin)
- Log aggregation validation for debugging model-consumer mismatches
- Metrics to assert: p95/p99 latency, prediction throughput, error rate budgets
- Alerting threshold testing for integration degradation

## 7. Compliance & Security Validation
- Data encryption verification in transit and at integration boundaries
- Access control testing for model endpoints (JWT/OAuth validation)
- Audit trail completeness for regulatory requirements
- Bias detection integration points in the data flow

## 8. Execution Roadmap
- Prioritized test phases (smoke → contract → E2E → chaos engineering)
- Environment progression strategy (dev → staging → prod-shadow)
- Risk mitigation strategies for high-impact integration points

Format the output as a professional test plan document with markdown tables for test cases, mermaid diagrams for data flow (if applicable), and implementation checklists.