Security must go beyond basic RBAC and certifications. Generative AI platforms often need to connect to multiple data sources, each with varying user access rights across active directories. For instance, consolidating access control for systems like ERP, CRM, and HR platforms into a single AI interface ensures secure and seamless integration without compromising data integrity. To achieve this, an advanced user access rights management system is essential, capable of merging and reconciling diverse access rights from different active directories across systems.

A robust data privacy screen ensures sensitive data is masked before sending requests to the LLM. Post-processing, the data can be unmasked before being returned to the user. This mechanism enables organizations to leverage generative AI without breaching privacy regulations.

An organization’s taxonomy—its structured understanding of concepts, terms, and relationships—must be well-defined. A taxonomy knowledge graph ensures:

• Inputs are accurately understood

• Outputs are factually grounded, reflecting the company’s domain expertise and minimizing hallucinations

For example, a retail company’s knowledge graph could map product categories, customer preferences, and supplier details, ensuring AI outputs align with the business’s specific context.

AI guardrails are essential to manage the tone, language, and appropriateness of outputs. Beyond preventing vulgarities or offensive content, guardrails enforce corporate language and branding standards, ensuring a consistent customer and employee experience.

For instance, AI outputs for marketing materials should align with the company’s creative and promotional guidelines.