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09/06/2026 11:01 pm
TL;DR: AI gateways now route enterprise model access, but they do not inspect prompt content, response content, or tool calls, leaving organisations blind to prompt injection, jailbreaks, and sensitive data leakage, according to Lasso Security. The practical shift is that AI traffic inspection is becoming an identity and policy control, not just an observability add-on.
NHIMG editorial — based on content published by Lasso Security: How Lasso Secures AI Gateway Traffic Across Kong, Portkey, LiteLLM, Envoy, and More
Questions worth separating out
Q: How should security teams govern AI gateway traffic that carries prompts and tool calls?
A: Security teams should govern AI gateway traffic as a runtime policy problem, not just a routing problem.
Q: Why are AI gateways not enough to stop prompt injection and data leakage?
A: AI gateways control where traffic goes, but they do not understand what the traffic means.
Q: How do security teams know whether AI traffic controls are actually working?
A: They should look for evidence that policy decisions are consistent across prompts, responses, and tool calls, and that every block, mask, or alert can be traced back to a specific interaction.
Practitioner guidance
- Inspect AI content at the gateway layer Add prompt, response, and tool-call inspection on top of routing so policy decisions are made on the interaction content, not just traffic metadata.
- Classify tool calls as governed actions Treat any model-initiated action that reaches external systems as a policy-bearing event and apply controls before execution continues.
- Unify logging for forensic reconstruction Record what was sent, what was returned, which policy applied, and what action was taken so security review can reconstruct the full decision path.
What's in the full article
Lasso Security's full blog post covers the operational detail this post intentionally leaves for the source:
- How the integration behaves across Kong, Portkey, LiteLLM, Envoy, and other gateway stacks
- What the real-time detection layer inspects in prompts, responses, and tool calling
- How autonomous response actions such as masking, blocking, and alerting are triggered
- What the audit trail records for later review and incident reconstruction
👉 Read Lasso Security's analysis of AI gateway traffic inspection for enterprise AI →
AI gateway traffic inspection: what it means for IAM teams?
Explore further
10/06/2026 1:12 am
AI gateway traffic inspection is becoming an identity control, not a monitoring feature. The gateway already represents an access chokepoint for models, but the real risk sits inside the interaction where prompts, responses, and tool calls can move sensitive material or adversarial instructions. That makes content-level enforcement part of the identity plane rather than a separate detection concern. Practitioners should treat AI gateway inspection as policy enforcement at runtime, not as a passive analytics layer.
A few things that frame the scale:
- 24,008 unique secrets were exposed in MCP configuration files in 2025 alone, the protocol's first year of widespread adoption, according to The State of Secrets Sprawl 2026.
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers.
A question worth separating out:
Q: What is the difference between gateway routing and AI traffic inspection?
A: Gateway routing moves requests between services and models. AI traffic inspection evaluates the content of those requests and responses for policy violations, sensitive data, and adversarial manipulation. The first is an access-path function. The second is a security enforcement function.
👉 Read our full editorial: AI gateway traffic inspection is becoming an identity control
