What kind of fencing is needed for LLM

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To that end, Nutanix has developed what it calls “GPT-in-a-box,” which uses open-source models and trains them using private data. “Users retain control over their data because it stays within the organization,” Perreira explains.

Finally, beyond technical infrastructure and programming skills, it is important that software development teams use appropriate safeguards to ensure security, privacy, and compliance when building enterprise AI applications. Business leaders also need to consider the ethical implications of using LLM, auditability, and the need to provide explanations to demonstrate to auditors that they have implemented policies and procedures to ensure that LLM-based decision-making applications are fair and impartial.

James Tedman, Head of European Region at BlueFlame AI, suggests focusing on the following areas:

Data Security: Implement strong encryption of data at rest and in transit. Regularly review and update security protocols to protect sensitive information processed by various LLMs.
Privacy compliance. Comply with privacy laws such as china mobile database or CCPA. Ensure that any LLMs you use comply with these regulations, especially regarding the handling of user data and consent. Ensure that you have commercial agreements with LLM providers to prevent your data from being used to train models.
Access Control: Implement strict access control and authentication mechanisms to prevent unauthorized access to AI systems and sensitive data.
Audit and Monitor: Regularly audit AI systems for security vulnerabilities and monitor their usage to identify and respond to malicious activity.
Bias and ethical considerations. Regularly assess different LLMs for bias. Implement measures to reduce the impact of these biases on decision making and results.
Compliance with industry standards: Ensure that all AI solutions comply with industry standards and regulations, especially in industries such as healthcare, finance, and legal.
Transparent Use of Data: Maintain transparency in how AI systems use and process data by informing stakeholders about the AI ​​models used and the methods used to handle data.