Threat Model Before Features

Public AI gateways expand attack surface quickly: token theft, prompt-injection relay, tool abuse, and admin-plane drift are common. Security posture improves when teams start with threat model constraints, then design features inside those boundaries.

Baseline Security Controls

• Authentication: mandatory token/password auth for all non-loopback binds.
• Network policy: restrictive ingress rules and explicit IP allowlists where possible.
• Tool minimization: deny high-risk tool paths by default and allow only required tools.
• Session isolation: separate operational, automation, and user-facing contexts.

Hardening Operations

• Automated config validation and restart health checks
• Periodic token rotation and key hygiene audits
• Immutable logging for command and config changes
• Rate limiting and abuse detection for external endpoints

Incident Readiness

Build containment playbooks before you need them: revoke exposed credentials, drop risky bindings, reduce tool scope, and capture forensics quickly.

What Actually Moves Risk

In practice, the highest impact controls were strict auth, reduced tool privileges, and disciplined change management. Security outcomes improved without materially slowing engineering throughput.