Every Enterprise Will Have Its Own AI Harness

Description and use

Enterprise AI apps often duplicate infrastructure and security. A shared harness with plugins centralizes identity, model access, connectors, policy enforcement, and audit, enabling thin apps and consistent governance.

Problem

Large enterprises build many duplicated AI applications, each re‑implementing identity, model access, connectors, policy checks, and audit, leading to inefficiency and inconsistent security.

Outcome

By centralizing identity, model access, connectors, policy enforcement, and audit in a plugin‑based harness, enterprises reduce duplicated effort, enforce consistent security, and enable rapid experimentation and deployment of AI tools.

Workflow

• 1. Define enterprise policy and enforcement plugins.
• 2. Build or integrate connectors for internal systems (Confluence, Jira, ServiceNow, etc.).
• 3. Configure model gateway and DLP checks.
• 4. Deploy harness with plugin architecture.
• 5. Build thin applications that call harness APIs for identity, policy, connectors, and audit.
• 6. Use harness to route requests, enforce policies, obtain approvals, and record audit events.

Evidence

The first release composes three plugin roles: enterprise-atlassian, enterprise-policy, enterprise-audit.

Source

With a shared harness, an application can focus on the job it was built to do.

Source