How to Stop Customer Data From Being Used to Train AI?

In today's data-driven world, ensuring that customer data does not inadvertently end up in AI training datasets is crucial for maintaining privacy and compliance. Here's a comprehensive guide to prevent customer data from being used to train AI models.

Steps to Ensure Customer Data Does Not Reach AI Models

1. Discover Customer Data in Cloud Databases

The first step in safeguarding customer data is to identify where it resides. Use automated tools to scan and discover customer data across all cloud databases. This process involves:

• Automated Scanning: Employ tools that can scan through databases to identify records that contain customer data.
• Comprehensive Coverage: Ensure all databases, including SQL and NoSQL databases, are covered. Checkout https://strac.io/integrations

2. Classify and Tag Customer Data in Cloud Databases

Once the data is discovered, it needs to be classified and tagged appropriately. This involves:

• Classification Algorithms: Implement robust classification algorithms that can accurately tag different types of sensitive data, such as PII (Personally Identifiable Information), PHI (Protected Health Information), and financial data.
• Regular Updates: Regularly update the classification system to recognize new types of sensitive data.

3. Remediate via Automatically Removing Customer Data, Pseudonymizing, or Redacting Sensitive Data in Documents

To prevent sensitive data from reaching AI models, apply remediation actions:

• Automated Removal: Automatically remove any customer data that is not necessary for the task at hand.
• Pseudonymization: Replace identifiable information with pseudonyms to protect individual identities.
• Redaction: Redact sensitive information in documents to ensure it cannot be used to identify individuals.

4. Create a New Database with Above Remediation Actions

After remediation, create a new database that contains only the non-sensitive data:

• Thorough Testing: Perform thorough testing to ensure no sensitive data remains in the new database.
• Encryption: Encrypt the new database to secure data during transit and storage.

5. Provide the Above Database to ML/Research Scientists

When the new database is ready, ensure it is securely provided to the ML/research scientists:

• Access Control: Implement strict access control mechanisms to ensure only authorized personnel can access the data.
• Training: Provide training to ML/research scientists on data handling best practices and the importance of maintaining data privacy.

Additional Steps for Post-Training AI Models

6. Continuous Monitoring and Auditing

Set up continuous monitoring and auditing to ensure ongoing compliance:

• Monitoring: Continuously monitor databases to identify and remediate new sensitive data.
• Audits: Conduct regular audits to ensure compliance with data protection policies and procedures.

7. Access Logging and Monitoring

Maintain detailed logs and monitor access to the data:

• Access Logs: Keep detailed logs of who accessed the data and when.
• Real-Time Monitoring: Implement real-time monitoring to detect and respond to suspicious activities.