🔐Establish strict data governance policies with clear access control and encryption standards. 📋Perform due diligence on vendor security practices, ensuring they meet industry standards. 🔄Regularly audit and monitor vendor data handling to detect breaches or inconsistencies early. 🤝Set up clear contractual agreements that hold vendors accountable for data security. 🛠Use data masking or anonymization for sensitive information when possible. 📊Require vendors to provide audit logs and reports on their data practices for transparency. 🚀Implement a quick response plan for any identified data integrity issues with vendors.

When working with external partners, we ensure data protection by enforcing strict data governance policies, conducting thorough vendor assessments for security compliance, and continuously auditing data handling practices to quickly detect and resolve any vulnerabilities.

From my experience, set out clear data management policies upfront. These should be part of the contract and violations, penalties should be clearly outlined. That being said, ensure team is trained and understands the process to manage data. A quick 1-hour refresher with the whole team is of valur. Also, include it in the onboarding material for new team members.

Before even considering them, you must first request their data policy. Scrutinize them severely and even use AI to find any loop holes. Everything must be questioned before you even get in bed with any external AI vendors. We've consulted with many companies to ensure that their client's data stays safe, especially when working with 3rd party AI vendors. Our core values at Augmented AI are all about integrity and your company should not take any shortcuts in this regard.

1) Priorize LLMs open source. 2) Para dados sensíveis, envie para IA apenas se estiverem criptografados 3) Caso for usar um LLM closed source, contrate planos enterprises que evitem o uso dos dados sensíveis para o treinamento de modelos de LLM 4) Contrate LLMs a partir de parceiros oficiais dos principais LLMs, como Microsoft, e faça com que os seus dados estejam seguros para o uso.

In my experience, to safeguard data integrity when collaborating with external AI vendors, I ensure strict data governance by implementing encryption, access controls, and data anonymization. I also require adherence to compliance standards and regularly audit vendor practices to maintain security and data accuracy throughout the collaboration.

When collaborating with external AI vendors, safeguarding data integrity is crucial. My two cents: implement strict data governance, including access controls and encryption for data in transit and at rest. Vendors are required to follow the organization's compliance standards, and regular audits must be conducted to assess their security practices. Data sharing is minimized and protected using anonymization or pseudonymization. Vendor agreements with clear SLAs ensure accountability for data handling. Continuous monitoring flags any integrity issues, ensuring data remains secure, accurate, and aligned with security goals throughout the collaboration.