Hi all,

As an AI software development company, we are deeply committed to maintaining the highest standards of data privacy and security. We are seeking advice on the most effective strategies and protocols to implement during the development and deployment of AI models. Specifically, we are interested in best practices for handling sensitive data, compliance with regulations such as GDPR, and measures to prevent data breaches.

Ensuring data privacy and security in AI development involves several key strategies:

1. Data Anonymization: Use techniques such as masking, encryption, and tokenization to protect sensitive data.
2. Access Controls: Implement strict access controls and regular audits to ensure only authorized personnel can access data.
3. Compliance: Ensure compliance with regulations like GDPR by conducting regular data protection impact assessments (DPIAs).
4. Security Protocols: Use robust encryption protocols (e.g., AES-256) for data at rest and in transit.
5. Tools: Employ tools like IBM Guardium or Microsoft Azure's Confidential Computing for enhanced data security.

Hello,

My apologies for the delay in responding, we've had a number of people on our team out on vacation. Are you looking to talk to someone about our AI Governance offering, watsonx.goverance?