Topic starter
10/06/2026 12:33 am
TL;DR: Enterprise AI assistants are becoming front doors to internal APIs, databases and SaaS, and Pomerium says the McKinsey platform hack showed how broad intermediary trust, not prompt injection alone, can let attackers trigger internal actions and data access. The architectural lesson is that AI systems need request-by-request policy enforcement, not blanket trust at login.
Editorial analysis by NHI Mgmt Group, based on content published by Pomerium: “What Made McKinsey's AI Platform Easy to Hack? And How to Fix it.”.
Key questions
Q: What breaks when an AI platform is trusted like a normal application session?
A: The access model breaks because the platform can become a confused deputy with broader effective authority than the user who prompted it.
Q: Why do AI platform gateways need request-level authorization instead of login-only controls?
A: Because AI systems can make many different downstream requests inside one session, and each request may have a different risk profile.
Q: What are the signs that an AI platform trust model is too broad?
A: Look for internal services that accept requests from the AI layer without rechecking identity, weak visibility into tool calls, and backend permissions that exceed what any single user should have.
Practitioner guidance
- Treat the AI platform as an untrusted intermediary Remove implicit trust from the orchestrator and require all internal requests to pass through a policy enforcement layer before reaching APIs, databases or SaaS systems.
- Enforce per-request authorization Evaluate user identity, resource, method and context on every tool call instead of relying on a one-time login decision or shared backend trust.
- Separate user intent from platform capability Limit what the AI layer can do by action type and target service so that a user's request cannot silently expand into administrative or bulk-data access.
Bottom line: The article shows that the main failure mode is excessive trust in the AI platform itself, not simply malicious prompting.
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This topic was modified 2 hours ago by NHI Mgmt Group
11/06/2026 2:16 am
AI platform governance now inherits the confused deputy problem. When an LLM orchestrator is treated as a trusted intermediary, it can be induced to use legitimate access in illegitimate ways. That is not a model-only issue, it is an access control design failure that spans IAM, workload identity, and downstream service trust. Practitioners should treat AI platforms as untrusted clients until every internal action is re-authorized.
A few things that frame the scale:
- When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes and as quickly as 9 minutes in some cases, according to LLMjacking: How Attackers Hijack AI Using Compromised NHIs.
- The same research found that DeepSeek accidentally embedded over 11,000 secrets in its training data and left a database exposed online, revealing more than one million sensitive records including chat histories, backend credentials, and API keys.
A question worth separating out:
Q: What is the difference between an AI gateway and a normal proxy?
A: A normal proxy can forward traffic, but an AI gateway enforces identity, policy, and auditing specifically around AI-driven requests. That matters because the control point has to understand user context, tool categories, and action risk before forwarding the request. In practice, the gateway becomes the governance layer for delegated AI access.
👉 Read our full editorial: AI platform gateway controls expose the McKinsey hack pattern
12/06/2026 3:50 am
Identity-aware gatewaying is now the control point for enterprise AI access: when an AI platform can call internal systems, the decisive control is no longer the login session but the policy boundary in front of every request. That changes how teams think about authorization, audit and trust across the AI stack. The platform must be treated as an intermediary identity, not as a privileged extension of the user. Practitioners should design for per-request control rather than platform-level trust.
A question worth separating out:
Q: How should security teams govern delegated control in autonomous AI systems?
A: They should treat delegated control as a first-class identity problem, not a by-product of application integration. That means binding every action to a named owner, a defined purpose, and a constrained scope, then reviewing whether the agent still operates within those limits as its behaviour changes. Accountability must remain traceable throughout the agent lifecycle.
👉 Read our full editorial: AI platform gateway controls expose the McKinsey hack pattern
This post was modified 2 hours ago by NHI Mgmt Group