Design a Secure Enterprise AI Assistant for Fortune 500 Companies

The Core Architecture

The design separates the platform into trusted zones, with controlled communication between the application, data stores, model gateway, and tool execution environments.

• Identity and access: Corporate SSO, MFA, least-privilege authorisation, and permission-aware retrieval.
• Knowledge retrieval: Hybrid vector and keyword search with source citations and access-control filters.
• Model gateway: Routes requests according to data classification, residency, model capability, and cost.
• Tool broker: Executes approved actions using the employee's delegated identity and requires confirmation for sensitive operations.
• Security controls: DLP, prompt-injection defenses, sandboxing, output verification, and controlled network egress.
• Governance and audit: Retention policies, legal holds, immutable audit records, compliance workflows, and privacy-aware analytics.

What You'll Learn

• Permission-aware RAG: Prevent unauthorised documents, summaries, and derived facts from appearing in answers.
• Classification-based model routing: Select eligible models based on sensitivity, region, and enterprise policy.
• Secure AI agents: Apply delegated credentials, approval workflows, restricted tool capabilities, and sandboxed code execution.
• Prompt-injection defense: Restrict data exfiltration through structural controls instead of relying solely on model instructions or classifiers.
• Enterprise privacy: Implement encryption, controlled administrator access, retention, eDiscovery, and employee transparency.
• Production operations: Manage model failover, connector synchronization, budgets, evaluation, incident response, and compliance evidence.

Why Permission-Aware Retrieval Matters

A shared service account with access to every company document creates a dangerous security boundary. Even if the assistant attempts to filter results, a single authorisation mistake can expose confidential information.

Instead, retrieval must be filtered using the requesting employee's current entitlements, followed by independent permission checks before content enters the model context. When permission state is stale or unavailable, the system must fail closed.

Security Must Be Structural

Never give an AI assistant more authority than the user who invoked it.

Prompt-injection detection and DLP provide additional protection, but they cannot guarantee that a model will always follow instructions. Least-privilege tools, default-deny egress, classification-aware routing, human approval, and permission enforcement must remain effective even when the model behaves unexpectedly.

Key Takeaway

A secure internal ChatGPT is not simply an LLM connected to company documents. It is a governed enterprise platform where identity, permissions, data classification, model routing, tool execution, privacy, and auditability are enforced end to end.