AI-Assisted Validator - SR-007

Program Architecture Layer	Business Logic Layer
Module	Data Management
Component	Data Matching Engine
Level of Importance	Optional
Priority	Low
Social Protection Delivery Chain Stage	Intake and Registration, Assessment of Needs and Conditions

Requirement Description	SR ideally should incorporate AI capabilities for advanced data validation and anomaly detection
Justification	Enhances data quality and reduces manual effort in data cleaning and validation
Use Case	Use AI capabilities for advanced data validation and anomaly detection.
Data Elements Required	Data Validation Inputs, Anomaly Detection Data, AI Model Metrics
Minimum Technical Specifications	Validation: Python script for anomaly detection. Integration: REST API for invoking validation. Data Export: CSV reports on anomalies detected.
Standard Technical Specifications	Validation: ML-based validation with TensorFlow. Integration: Event-driven validation triggers. Data Export: Integration with BI tools for reporting.
Advanced Technical Specifications	Validation: AI-driven real-time anomaly detection with reinforcement learning. Integration: Real-time federated validation with Kafka. Data Export: AR-enabled insights and visualizations for anomalies.
Security & Privacy Requirements	Token-based API access for validation, encryption for anomaly data.
Scalability Considerations	AI-driven anomaly detection for scalable data validation.
Interoperability Requirements	Integration with external validation systems for enhanced data accuracy.
Compliance with International Standards	Compliance with GDPR for data validation and handling.
User Interface Requirements	Dashboard for viewing data validation results and anomalies.

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