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24/06/2026 7:14 pm
TL;DR: Enterprise AI gateway decisions now hinge on policy granularity, identity integration, MCP governance, and operational commitments, while LiteLLM is positioned as a baseline option for initial AI connectivity, according to Kong. The practical issue is not feature count alone, but whether AI traffic can be governed as part of existing IAM and security controls rather than a parallel stack.
NHIMG editorial — based on content published by Kong: LiteLLM vs Kong: Choosing the Right Enterprise AI Gateway for Production
By the numbers:
- Kong’s AI PII Sanitizer enforces DLP across 20+ PII categories on both prompts and responses under one audit trail.
- Kong backs Konnect with a 99.9% uptime SLA, while Severity 1 incidents receive a 30-minute, 1-hour, or 2-hour initial response depending on support tier.
Questions worth separating out
Q: How should security teams govern AI gateway access in production?
A: They should treat the gateway as an enforcement point for identity, policy, and audit, not as a convenience layer for routing.
Q: Why do AI gateways create new identity governance concerns?
A: AI gateways sit between users, service accounts, agents, and models, so they become the place where identity, authorisation, and data controls either stay coherent or fragment.
Q: What breaks when MCP tool access is not default-deny?
A: Tool discovery and invocation become open-ended privilege expansion paths.
Practitioner guidance
- Consolidate AI gateway policy into one enforcement layer Avoid splitting routing, budgets, DLP, and access checks across separate tools unless you can prove the combined policy path is still auditable end to end.
- Bind AI traffic to enterprise identity controls Require OIDC, mTLS, ACLs, or equivalent IAM-native controls for model calls, service accounts, and agent traffic so access does not become a parallel trust model.
- Treat MCP tool access as privileged access Apply default-deny rules to tool discovery and invocation, and review each exposed tool as an increment in identity blast radius.
What's in the full article
Kong's full article covers the operational detail this post intentionally leaves for the source:
- A feature-by-feature breakdown of how LiteLLM and Kong differ across routing, policy composition, and identity enforcement.
- Detailed benchmark context for throughput, latency, and production scaling assumptions that this analysis only summarises.
- Specific implementation detail on PII sanitisation, DLP behaviour, and guardrail integration paths.
- Operational coverage of patching commitments, uptime posture, and release integrity signals for enterprise procurement.
👉 Read Kong’s comparison of LiteLLM and Kong AI Gateway for enterprise production →
LiteLLM vs Kong: what changes for AI gateway governance?
Explore further
25/06/2026 4:26 am
AI gateways are becoming identity enforcement points, not just traffic routers. Once AI systems are used in production, the control question shifts from whether a model can be reached to whether the call is authorised, attributable, and auditable. That makes gateway choice an identity architecture decision as much as a platform decision. Practitioners should treat the gateway as part of the access-control boundary, not a separate AI feature layer.
A few things that frame the scale:
- Kong’s AI PII Sanitizer enforces DLP across 20+ PII categories on both prompts and responses under one audit trail, according to DeepSeek breach.
- Kong’s benchmark comparison also reports 859% higher throughput and 86% lower latency in the tested environment.
A question worth separating out:
Q: Should organisations evaluate uptime and patch SLAs for AI gateways?
A: Yes, because the gateway is now part of the security boundary and the operational blast radius. If patch timing, incident response, or release integrity are unclear, security teams inherit uncertainty at the exact layer that protects prompts, data, and agent access.
👉 Read our full editorial: Kong vs LiteLLM for enterprise AI gateway governance in production
