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23/06/2026 7:21 pm
TL;DR: Existing gateway and WAF models assume payloads are understandable without semantic context, which autonomous workflows break, and Kong’s partnership with Noma centers on agentic AI runtime protection, combining traffic orchestration with AI-native guardrails to govern agents, MCP tools, and LLM flows in real time, according to Kong.
NHIMG editorial — what this means for AI and NHI governance
Questions worth separating out
Q: How should security teams govern AI agents that can call tools at runtime?
A: Security teams should govern agent tool use as a runtime authorization problem, not a static integration problem.
Q: Why do AI gateways matter for agentic AI security?
A: AI gateways matter because they become the enforcement point where traffic, identity, and policy converge.
Q: What do security teams get wrong about prompt injection in enterprise AI?
A: Teams often treat prompt injection as a content-filtering problem, when it is really a control problem.
Practitioner guidance
- Define runtime policy for agent workflows Map every AI agent, MCP server, and LLM path to an explicit policy owner, then require approval for any tool or data connection that is not already in the governed inventory.
- Separate model access from tool access decisions Treat model selection, tool invocation, and data exposure as distinct authorization events so one permission does not silently grant the others.
- Instrument shadow AI detection at the control plane Monitor for unauthorized LLM connections, unregistered gateways, and agent traffic that bypasses central configuration distribution.
What's in the full announcement
Kong's full article covers the architectural implementation details this post intentionally leaves for the source:
- How Kong Konnect pushes AI security policy to data-plane nodes across clusters and environments
- How the AI A2A, MCP, and LLM gateway plugins normalize traffic and enforce identity validation
- How Noma Security Cloud handles runtime inspection, behavioural analysis, and tool-abuse prevention
- How the partner program packages validation and support for enterprise integrations
👉 Read Kong's analysis of agentic AI runtime security and governance →
Agentic AI runtime security: what Kong and Noma change?
Explore further
25/06/2026 3:18 am
Agentic AI runtime security is now an identity governance problem, not just an application security problem. Kong’s framing is accurate because the control surface is no longer limited to model prompts or API endpoints. Once agents can reach tools, data, and peers, the question becomes who or what is allowed to act at runtime and under which policy state. Practitioners should treat AI gateways as part of the identity control plane, not a separate security layer.
A few things that frame the scale:
- 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so, according to AI Agents: The New Attack Surface report.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
A question worth separating out:
Q: What should organisations do before expanding autonomous AI workflows?
A: Organisations should prove that governance is enforced consistently across agents, tools, and models before they expand deployment. That means testing policy distribution, inventorying shadow AI connections, and confirming that runtime blocks happen before the action executes, not during cleanup.
👉 Read our full editorial: Kong and Noma target runtime security gaps in agentic AI
