Mitigating privacy and security risks isn't just about compliance.

It's about designing trust into the system.

Here are three foundational best practices. Anonymization.

Personally identifiable information should be removed from training data while ensuring that re-identification is mathematically improbable.

Encryption data should be secure both in transit and address using strong cryptographic methods, particularly for sensitive financial and biometric data.

Access controls who can view, modify, or export data should be limited.

This includes role-based access and multi-factor authentication.

Industry leaders are also adopting privacy by design principles, embedding safeguards during system architecture, not after deployment.

For example, some institutions use federated learning, which allows AI models to train across decentralized data sources without centralizing the raw data.

Data privacy and security are not just IT issues.

There are foundational to ethical AI in finance.

Every decision made by an AI system is only as ethical as the data is built on and the way the data is handled.