Every Enterprise Will Have Its Own AI Harness

Описание и применение

Внутренние AI‑приложения в крупных компаниях дублируют инфраструктуру и безопасность. Создание общего «хранилища» с плагинами позволяет централизовать идентификацию, доступ к моделям, коннекторы, политику и аудит, ускоряя разработку и обеспечивая единый контроль.

Задача

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

Результат

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.

Рабочий процесс

• 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.

Доказательства

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

Источник

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

Источник