G.O.D Framework

Introduction

The ai_secure_data_handler.py module is dedicated to enforcing secure practices across the G.O.D framework’s data handling components. This includes encryption, secure storage, data integrity verification, and controlled data access. It serves as a backbone for ensuring compliance with security regulations like GDPR, HIPAA, and other data privacy standards.

Purpose

This module is aimed to:

• Encrypt sensitive data before storage or transmission.
• Provide mechanisms for decryption and access control to authorized entities only.
• Ensure data integrity with checksum or hash-based validation techniques.
• Implement secure key management for encryption purposes.
• Detect and mitigate unauthorized access attempts.

Key Features

• Data Encryption and Decryption: Utilizes industry-standard encryption algorithms such as AES-256 for securing data.
• Access Control: Implements role-based or identity-based access to sensitive data fields.
• Hashing and Checksum Validation: Ensures data integrity during file transfer or storage operations using SHA-256 or related algorithms.
• Secure Key Management: Stores and secures encryption keys using hardware security modules (HSMs) or encrypted environments.
• Audit Logs for Security Events: Maintains logs of all access attempts for compliance demands and forensic analysis.

Logic and Implementation

The implementation centers around strong encryption tools and secure key handling. Below is a simplified example of how data encryption and decryption is performed using the cryptography library:

from cryptography.fernet import Fernet
import logging

class SecureDataHandler:
    """
    Handles secure data operations such as encryption, decryption, and integrity validation.
    """
    def __init__(self, encryption_key=None):
        if encryption_key is None:
            encryption_key = Fernet.generate_key()
        self.cipher = Fernet(encryption_key)
        logging.info("Secure Data Handler initialized.")

    def encrypt_data(self, data):
        """
        Encrypts plain text data.

        Args:
            data (str): The data to be encrypted.

        Returns:
            str: Encrypted data.
        """
        try:
            encrypted = self.cipher.encrypt(data.encode('utf-8'))
            logging.info("Data encrypted successfully.")
            return encrypted
        except Exception as e:
            logging.error(f"Error encrypting data: {e}")
            return None

    def decrypt_data(self, encrypted_data):
        """
        Decrypts previously encrypted data.

        Args:
            encrypted_data (str): The encrypted data.

        Returns:
            str: Decrypted data.
        """
        try:
            decrypted = self.cipher.decrypt(encrypted_data).decode('utf-8')
            logging.info("Data decrypted successfully.")
            return decrypted
        except Exception as e:
            logging.error(f"Error decrypting data: {e}")
            return None

# Example Usage
if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)

    # Example initialization
    handler = SecureDataHandler()

    # Encryption and decryption example
    sensitive_data = "This is a secure message."
    encrypted = handler.encrypt_data(sensitive_data)
    print(f"Encrypted Data: {encrypted}")
    decrypted = handler.decrypt_data(encrypted)
    print(f"Decrypted Data: {decrypted}")
        

Dependencies

• cryptography: Core library used for encryption and decryption mechanisms.
• logging: Facilitates event logging for secure operations.

Integration with G.O.D Framework

The ai_secure_data_handler.py module integrates seamlessly with the following system components:

• ai_data_registry.py: Ensures that all data stored in the registry is encrypted and secure.
• ai_audit_logger.py: Logs every access, modification, and operation performed on secured data.
• ai_data_privacy_manager.py: Collaborates to enforce organization-wide data privacy policies.

Future Enhancements

Possible improvements and extensions:

• Integration with blockchain technologies for tamper-proof data logs.
• Enhanced hashing schemes for ensuring authenticity and identity-based access.
• Automated identification of sensitive data fields requiring encryption.
• Support for post-quantum cryptography methods to future-proof encryption.