Security, Compliance & Governance

Yes. You can restrict an AI chatbot by enforcing geo-based access controls at the application and retrieval layers—using IP-based location checks, user account metadata, and region-specific content permissions. The chatbot only responds when the request originates from, or is authorized […]

Enterprise AI knowledge hubs in customGPT.ai require SSO-based authentication, most commonly SAML 2.0, OIDC/OAuth 2.0, and centralized identity provider (IdP) enforcement. These methods ensure only authorized users can access sensitive knowledge, align AI access with existing IAM policies, and provide […]

You prevent hallucinated or biased answers by constraining what the AI is allowed to say in customGPT.ai. This means grounding every response in approved sources, blocking answers when evidence is missing, enforcing retrieval and verification rules, and monitoring outputs over […]

RAG is safer because enterprise data is never embedded into the model itself. With customGPT.ai, data is retrieved at query time from controlled sources and used temporarily to generate answers. Fine-tuning permanently alters model weights with your data, making deletion, […]

Uploading financial reports to a public LLM carries risks around data retention, loss of control, lack of auditability, and potential reuse beyond your intent. A private RAG system like customGPT.ai mitigates these risks by keeping documents under your control, preventing […]

Yes. You can deploy a RAG chatbot in a private cloud (VPC/VNet) or on-prem by self-hosting the key components app/API, vector database, and (optionally) the model runtime inside your network using customGPT.ai. This improves data sovereignty and reduces exposure by […]