Give AI agents the same front door as your apps—because audit beats novelty
Route agent traffic through the gateway so tokens, rate limits, logs, and policy stay one story for security teams and regulators.
- ai
- security
- governance
The fastest way to create compliance debt is to let AI tools bypass the same controls you spent years building for customer apps. The temptation is always the same: a pilot needs to ship, someone generates a service account key, traffic goes around the gateway “temporarily,” and six months later that path is load-bearing—with no SLO, no unified audit, and no clean story for SOC or regulators.
The better default is almost boring: AI traffic is API traffic. It should hit the same gateway, with the same enforcement and observability, as your mobile, web, and partner clients. Novelty is not a reason to weaken policy; it is a reason to tighten measurement.
Why “parallel stacks” fail in production
When AI tools use separate keys, separate routes, or informal integrations:
- Logs fragment — Your SOC cannot correlate abuse, mis-use, or incident timelines across human and agent traffic.
- Policy drifts — Rate limits, scopes, and IP allowlists diverge from what production apps use; exceptions become permanent.
- Audit narratives break — You cannot answer “who called what, when” with a straight face in a customer audit or regulatory inquiry.
- Cost opacity — Finance sees API spend in one place and AI pilots in shadow infrastructure.
Zerq’s model is that AI agent access is an additional route on the same deployment—not a second product you bolt on after the pilot “graduates.” Product framing: For AI agents.
What “same front door” means in practice
Identity and scopes
Credentials for agents should be issued, rotated, and revoked through the same IAM processes as applications—not long-lived shared keys checked into repositories. Scopes and API products should match what partners and internal services already use.
Rate limits and abuse
Tool loops can amplify QPS without malice. Per-partner and per-product limits at the edge protect upstreams from runaway automation—see Rate limits that protect upstreams without punishing partners.
Structured logs and metrics
AI-originated requests should land in the same SIEM-friendly schema as everything else—same fields for identity, product, outcome. Observability covers metrics and logging posture.
Discovery without bypass
MCP and similar protocols help clients discover what to call—they should not become a backdoor to operations outside published bundles. Authorization remains authoritative at the gateway.
Platform automation without a shadow admin plane
Teams automate catalog and config via APIs or MCP-style interfaces. That power is only safe when automation uses the same RBAC as your admin console—otherwise you have built a shadow control plane with weaker logging and no separation of duties.
If you are evaluating automation endpoints, read Platform automation. For a longer architectural treatment of unified rules for REST and AI, see Why your AI gateway needs the same security rules as your REST APIs.
Copilot-style features: same session, same audit
If you also use Zerq Copilot for natural-language operations, guardrails belong in identity: OIDC sessions, role-bound actions, server-side model keys—not browser-held secrets. Every tool call that mutates state should be auditable equivalently to clicking the same button in the UI.
Bottom line: If an AI agent can do something your junior admin shouldn’t, that is not an AI problem—it is a policy and routing problem. Fix it at the gateway boundary.
Request an enterprise demo and we will map your agents, portals, and gateways to a single audit story.