Every Enterprise Will Have Its Own AI Harness

说明与用途

大型企业的内部 AI 应用往往重复基础设施和安全措施。通过插件化的共享 AI 容器集中身份、模型访问、连接器、策略执行和审计，实现轻量化应用和一致的治理。

问题

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

结果

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.

工作流

• 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.

证据

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

来源

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

来源