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14/09/2026 9:44 am
TL;DR: Kong AI Gateway and Straiker combine discovery, adversarial testing, runtime defense, and agent-level containment into a control loop for enterprise AI agents, according to Straikerai. The core issue is not just approving traffic, but governing what a compromised agent can still do after a valid request is blocked.
NHIMG editorial — based on content published by Straikerai: Kong + Straiker: From AI Gateway Security to a Full Agentic Control Loop
By the numbers:
- Straiker reports a 98.1% true-positive rate for its purpose-built runtime detection.
- Across successful attack scenarios, 85% involved agents operating beyond their intended authority.
Questions worth separating out
Q: How should security teams handle a compromised AI agent that keeps trying new actions after a block?
A: They should move from blocking single transactions to containing the actor.
Q: Why is an AI gateway not enough on its own for agent security?
A: A gateway can enforce traffic policy, but it does not fully explain what the agent is trying to do with that access.
Q: When should teams move from runtime blocking to agent-level shutdown?
A: They should escalate when a blocked action does not end the malicious sequence.
Practitioner guidance
- Map the agentic estate to a control point Use gateway traffic, model endpoints, MCP servers, and agent-to-agent paths to build a live inventory of what is actually connected and what can be enforced centrally.
- Test the agents that matter most first Run adversarial campaigns against the highest-risk workflows and record which prompt injection, tool manipulation, and lateral movement paths succeed before production rollout.
- Separate request blocking from actor containment Define escalation criteria that move from denying one unsafe action to revoking tools, freezing memory, suspending sessions, or taking the agent offline.
What's in the full article
Straikerai's full blog post covers the operational detail this post intentionally leaves for the source:
- The end-to-end Kong and Straiker integration pattern for AI gateway enforcement and agent-specific security decisions.
- How Discover AI, Ascend AI, Defend AI, and the Agentic Kill Switch fit together in a production operating model.
- The runtime flow for evaluating agent traffic, returning a verdict, and escalating to containment when risk persists.
- Deployment guidance for teams already using Kong AI Gateway and wanting to operationalise the control loop.
👉 Read Straikerai's analysis of the Kong and Straiker agentic control loop →
Agentic control loops for AI gateways: what changes for security teams?
Explore further
14/09/2026 9:45 am
Agentic control cannot stop at request approval: once an AI agent has independent runtime behavior, the security problem is no longer only whether one call should be allowed. The article shows why transaction-level guardrails are insufficient when the same actor can keep reasoning and try another path. Practitioners should treat the agent itself as the containment boundary.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to AI Agents: The New Attack Surface report.
- 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: Why are AI discovery tools not enough for agent governance?
A: Because discovery tells you what exists and how risky it looks, but it does not remove the credential that makes the action possible. A security team can still be left with standing OAuth grants, API keys, or connector secrets even after a platform identifies the agent as high risk.
👉 Read our full editorial: Kong and Straiker show why agentic control loops matter
