The Philosophy of Secure AI: Let LLMs Think, Let Tools Execute
For a secure AI native, the real security challenge isn’t keeping bad actors out — it’s keeping powerful AI from making dangerous moves by accident.
Krunal Sabnis
September 10, 2025
As AI gets sharper and faster at reasoning, a new security challenge is emerging. The real risk isn’t keeping bad actors out — it’s keeping powerful AI from making dangerous moves by accident. In my earlier post, I explained why the old cloud-native security playbook is failing. The fix is to fundamentally rethink the relationship between AI reasoning and execution.
A Refund Too Far
Imagine an AI agent handling customer support: it can pull up customer records, view billing history, and check refund eligibility. But in the same system, there’s also a processRefund tool — a destructive action that changes financial records.
If the LLM reasons “this customer should be refunded” and tries to call that tool, you don’t want it to have the ability to execute — even if its logic seems sound.
The AI can think. It can reason. It can plan. But the actual act — the irreversible change — must be controlled.
This isn’t just a refund problem. It’s any scenario where AI reasoning meets sensitive execution:
- Modifying financial data.
- Deleting customer records.
- Triggering operational workflows with real-world consequences.
Reasoning vs. Execution: A Necessary Split
Large Language Models are incredible at breaking down complex requests and planning steps. But they’re also unpredictable. Their “reasoning” is what makes them powerful — and what makes them risky in real world application.
Your internal APIs and services, on the other hand, are precise and deterministic. They do exactly what they’re told, the same way every time. That reliability is an asset — and it’s one you don’t want an unpredictable reasoning engine to bypass.
This is where emerging standards like the Model Context Protocol (MCP) fit in. MCP defines a structured way for AI agents to discover and interact with tools — exactly the kind of “reasoning-to-execution” bridge we need. But in the enterprise, it still requires a governance layer to ensure the agent only sees, and only calls, the tools it’s allowed to.
The safest, most scalable approach is to let the LLM do the thinking, but only let trusted, controlled systems — connected through MCP — do the acting.
Why This Challenge Is Growing
The Model Context Protocol (MCP) is emerging as the accepted way forward for agent-to-tool communication, and major players like Google are already documenting how to run MCP servers in secure cloud environments.
This is a clear sign that we are moving toward an enterprise landscape with hundreds of internal and external MCP tools. This creates a critical question: should an LLM have access to all of them?
This isn't a problem that can be solved by traditional IAM, which was built for predictable, human-driven workflows. Agents have a real identity crisis, and we need a new control plane to manage it
This is the philosophy behind neurelay.ai.
The neurelay.ai Philosophy
Let the LLM think freely — explore, reason, plan. Let only trusted systems execute — and only within the permissions you define.
No matter how convincing the reasoning, no matter how valid the plan seems — execution always goes through a governance layer that you control.
We’re building Neurelay.ai to be that governance layer for AI-native stack. It’s the bridge between the AI brain that thinks and the hands that do the work — ensuring the two are connected only on your terms.
But we don't want to build it in a vacuum. We are looking for a select group of innovative companies to become our first Design Partners. If you're facing these issues and want to help shape the solution, I'd love to talk.