Automated anonymization and encryption of sensitive datasets

Purpose

1.1. Ensure all sensitive taxpayer and financial information managed by the Revenue Service is automatically anonymized and encrypted before storage, processing, or archival.
1.2. Prevent unauthorized data access, enable secure inter-departmental data exchanges, and facilitate compliance with GDPR and other regulatory mandates.
1.3. Apply uniform data security measures across inbound, in-process, and outbound government dataflows handling PII, tax records, and financial transactions.

Trigger Conditions

2.1. Incoming datasets received via data ingestion portals, ETL jobs, or API endpoints.
2.2. Scheduled data batch processing at specified intervals (e.g., nightly jobs).
2.3. Manual upload events or file drops in designated SFTP/FTP directories.
2.4. Data sharing events to third-party entities or internal government services.

Platform variants

3.1. AWS Lambda
• Feature/Setting: Configure Lambda to trigger on S3 PUT; connect to AWS KMS for encryption.
3.2. Google Cloud Dataflow
• Feature/Setting: Create pipeline step using Cloud DLP API to anonymize fields during data transformation.
3.3. Microsoft Azure Data Factory
• Feature/Setting: Integrate Data Flow activity with Azure Key Vault for encryption and Data Masking policy.
3.4. IBM Cloud Functions
• Feature/Setting: Code function to encrypt and redact input data using IBM Key Protect on trigger.
3.5. Talend Data Fabric
• Feature/Setting: Use tDataMasking/tEncrypt components within jobs; define column-level rules.
3.6. Informatica Cloud Data Integration
• Feature/Setting: Apply Dynamic Data Masking and Field Encryption transformation in mapping tasks.
3.7. Oracle Data Integrator
• Feature/Setting: Develop mappings using DBMS_CRYPTO and Oracle Data Redaction features.
3.8. SAP Data Services
• Feature/Setting: Apply Transform rules for masking; integrate with SAP Cryptographic Library.
3.9. Apache NiFi
• Feature/Setting: Build flow with EncryptContent and ScriptedTransformProcessor for anonymization.
3.10. Knime Analytics Platform
• Feature/Setting: Data anonymization nodes plus Cryptography extensions in ETL workflows.
3.11. Alteryx Designer
• Feature/Setting: Data Masking and Encryption tools in workflow for sensitive field protection.
3.12. Google Cloud Functions
• Feature/Setting: Use GCP DLP library to redact and encrypt on trigger from Cloud Storage.
3.13. Databricks
• Feature/Setting: Implement notebook-based masking/encryption with DBUtils and secrets management.
3.14. Snowflake
• Feature/Setting: Dynamic Data Masking and External Tokenization; configure masking policies.
3.15. MongoDB Atlas
• Feature/Setting: Client-Side Field Level Encryption and custom field masking queries.
3.16. PostgreSQL
• Feature/Setting: Configure pgcrypto module and custom triggers for automatic encryption.
3.17. Microsoft Power Automate
• Feature/Setting: Flow with built-in connectors for Azure Encrypted Blob and DLP API actions.
3.18. Box API
• Feature/Setting: Files uploaded trigger anonymization Lambda via webhook; return encrypted link.
3.19. Dropbox Business API
• Feature/Setting: Configure upload event for webhook, auto-process file with serverless function.
3.20. OpenSSL CLI (Linux automation)
• Feature/Setting: Scripted anonymization using sed/awk; OpenSSL for field encryption in pipeline.

Benefits

4.1. Immediate and consistent compliance with EU and Italian data protection laws.
4.2. Elimination of manual errors and reduced data breach risk for sensitive taxpayer records.
4.3. Streamlined secure data sharing and cross-departmental collaboration.
4.4. Scalable, modular protection adaptable to hybrid or cloud-native government infrastructures.