Organizations want to leverage the potential of this technology, but the first step is to analyze the market and work with providers whose commitment to security aligns with their needs.

Recommendations

When implementing GenAI solutions, consider the following options:

• Execute a basic model in a private environment so that the training data and results remain separate. This approach trades some of the breadth and power of dynamic “live” data from LMM for the security that your queries will not expose your organization’s confidential data to outsiders.
• Use retrieval-augmented generation, which leverages validated external data to refine the accuracy of foundational models without feeding them additional training data. This approach reduces security and accuracy risks.
• Run data loss prevention as a filter on input in a public LMM.
• Talk to your GenAI provider and adapt your use cases with data security in mind. Examine privacy and security settings. Can you prohibit your data from being saved? Can you do it manually? With a time limit? Can you run queries with anonymized data?
• If you use third-party applications or software-as-a-service providers that have integrated GenAI into their tools, ask the same questions and determine how they protect your data and results. Best practices include:
  • • Implementing strict access controls. Limiting the use of certain datasets to authorized users.
    • Using technologies that enhance privacy through data obfuscation (adding “noise” or removing identifying details—anonymization), encrypted data processing (homomorphic encryption, multi-party computation), federated/distributed analysis over centrally hosted data (processors cannot see the content), and data “accountability” tools (user-defined control).
• Pay close attention to the volume of data. The more data provided, the greater the likelihood of leakage.
• Train the team using the model to reflect best practices, compliance, and threat awareness.

“The only limit to AI is human imagination.”Criss duffey

AI has the potential to transform countless aspects of our society. In medicine, it can help diagnose diseases with unprecedented accuracy and personalize treatments. In education, it can offer adaptive and accessible learning experiences for everyone. In industry, it can optimize processes, reduce costs, and increase efficiency. But all these applications stem from the imagination of those who see beyond current limitations and dare to dream of what is possible.