G.O.D. Framework

Introduction

ai_data_privacy_manager.py focuses on ensuring data privacy compliance within the G.O.D. Framework. It provides tools to mask, secure, and audit user data, making the system compliant with regulatory requirements such as GDPR, CCPA, and HIPAA.

Purpose

• Data Masking: Protect sensitive user data by masking it when required for analytics or testing.
• Compliance: Ensure the framework adheres to data privacy regulations like GDPR (General Data Protection Regulation).
• Audit Trails: Maintain logs for data access and usage to track accountability.
• Data Encryption: Encrypt sensitive user information before persisting it in a database.

Key Features

• Data Masking: Replace sensitive values with obfuscated versions for non-production use cases.
• Encryption/Decryption: Encrypt data using secure algorithms (e.g., AES) for storage and decrypt during access.
• Access Management: Provide restricted access to sensitive data based on user roles.
• Audit Logs: Record all actions related to sensitive data for regulatory tracking purposes.

Logic and Implementation

The script integrates essential data privacy techniques through a modular approach. Below is an example:

            import logging
            from cryptography.fernet import Fernet
            import pandas as pd

            class DataPrivacyManager:
                def __init__(self, encryption_key):
                    """
                    Initializes the DataPrivacyManager with an encryption key.
                    :param encryption_key: Key used for securing sensitive data.
                    """
                    self.cipher = Fernet(encryption_key)

                def mask_data(self, dataframe, columns):
                    """
                    Mask sensitive data by replacing values with masked equivalents.
                    :param dataframe: Input Pandas DataFrame.
                    :param columns: List of columns to mask.
                    """
                    for col in columns:
                        dataframe[col] = dataframe[col].apply(lambda x: '***MASKED***' if pd.notna(x) else x)
                    return dataframe

                def encrypt_data(self, text):
                    """
                    Encrypt sensitive text data.
                    :param text: String data to be encrypted.
                    :return: Encrypted data in byte format.
                    """
                    return self.cipher.encrypt(text.encode())

                def decrypt_data(self, encrypted_text):
                    """
                    Decrypt previously encrypted text data.
                    :param encrypted_text: Encrypted data in byte format.
                    :return: Decrypted string.
                    """
                    return self.cipher.decrypt(encrypted_text).decode()

                def log_data_access(self, action, user, data_id):
                    """
                    Log user actions related to sensitive data.
                    :param action: Action performed (e.g., "read", "modify").
                    :param user: User performing the action.
                    :param data_id: Identifier for the accessed data.
                    """
                    logging.info(f"User '{user}' performed '{action}' on data ID '{data_id}'.")

            if __name__ == "__main__":
                # Example usage
                encryption_key = Fernet.generate_key()
                privacy_manager = DataPrivacyManager(encryption_key)

                # Mock dataset
                df = pd.DataFrame({
                    'Name': ['Alice', 'Bob', 'Charlie'],
                    'SSN': ['123-45-6789', '987-65-4321', '111-22-3333']
                })
                print("Original Data:")
                print(df)

                # Mask the SSN column
                masked_df = privacy_manager.mask_data(df, columns=['SSN'])
                print("\nMasked Data:")
                print(masked_df)

                # Encrypt and decrypt a sample text
                encrypted_ssn = privacy_manager.encrypt_data('123-45-6789')
                print("\nEncrypted SSN:", encrypted_ssn)
                decrypted_ssn = privacy_manager.decrypt_data(encrypted_ssn)
                print("Decrypted SSN:", decrypted_ssn)
            

Dependencies

This script relies on the following libraries:

• pandas: For handling tabular data.
• cryptography: Provides secure encryption and decryption capabilities.
• logging: For audit trail purposes.

How to Use This Script

To deploy ai_data_privacy_manager.py, follow these steps:

1. Provide an encryption key (can be generated using cryptographic tools).
2. Identify sensitive columns in the dataset that require masking or encryption.
3. Implement access management as per project requirements to restrict access to sensitive data.
4. Run the script and monitor audit logs for compliance tracking.

            # Example Usage
            from ai_data_privacy_manager import DataPrivacyManager

            key = Fernet.generate_key()
            manager = DataPrivacyManager(key)

            # Encrypt and decrypt example
            text = "Sensitive Information"
            encrypted = manager.encrypt_data(text)
            print("Encrypted:", encrypted)
            decrypted = manager.decrypt_data(encrypted)
            print("Decrypted:", decrypted)
            

Role in the G.O.D. Framework

• Data Integrity: Ensures data handled by components like ai_data_validation.py complies with privacy standards.
• System Logging: Works with ai_audit_logger.py to record actions concerning sensitive data.
• Masking in Pipelines: Connects directly with ai_data_preparation.py to obfuscate private information prior to analysis.

Future Enhancements

• Role-Based Access Control: Introduce more granular access privileges tied to organizational roles.
• Data Tokenization: Implement advanced tokenization techniques for structured and unstructured data.
• Multi-Cloud Support: Integrate with cloud-native security frameworks for hybrid environments.