Before AI Takes Control of Your Computer, Is There a Way to Check Its 'Thoughts'?

AI Summary

Introducing 'Conduct,' an open-source security layer that allows you to pre-screen and monitor for dangerous commands when AI assistants use external tools for tasks.

Imagine this: You wake up in the morning and tell your smartphone’s AI assistant, “Read all my emails, pick out the important ones, and share them in my work Slack channel.” A very convenient feature, right? But what if this AI has access beyond your email account—what if it also has permission to delete files on your computer? Or what if it accidentally posts a private document to Slack?

To solve the anxiety hidden behind this convenience, an open-source security project has emerged. It is called Conduct.

Why It Matters

Recent AI models have moved beyond simple conversation; they have begun to process tasks directly by using external tools like a human would. One of the core technologies enabling this is the MCP (Model Context Protocol, a standard communication protocol that connects AI assistants with external data or tools). [Reference: What is the Model Context Protocol (MCP)?]

As AI becomes more convenient, the ‘permissions’ it can execute on your computer or server are becoming more powerful. The biggest obstacle for enterprises adopting AI for work is security incidents. It is difficult to perfectly control the risk of an AI accidentally deleting important files or leaking them externally. Conduct acts as a kind of ‘seatbelt’ to help companies deploy AI assistants safely. [Reference: Conduct开源详解：为LLM与MCP… - OpenAI Hub]

The Explainer

To use a simple analogy, Conduct is like a ‘security checkpoint’ at the entrance of a company building.

While the process of an AI assistant executing a tool until now has been at the level of “Please proceed,” Conduct acts as a checkpoint that stops the AI when it issues a command to “Delete this file” and says, “Wait a moment, I will verify which file you are going to and what you are doing.” [Reference: Conduct开源详解：为LLM与MCP… - OpenAI Hub]

As another example, just as we have ‘access permission filters’ that ask if we want to allow photo editing apps to access our photo albums directly, Conduct is a monitoring filter that intercepts the ‘execution intent’ of an AI model to determine if the task is safe.

This system monitors three main areas. [Reference: GitHub - sseshachala/conductai: AI agent governance for teams.]

MCP Layer: Verifies all MCP tool calls where the AI sends and receives external data.
Router: Monitors all LLM (Large Language Model) commands called by the AI, regardless of which SDK is used.
LLM Calls: Inspects the specific command calls generated by the AI model itself.

If the AI attempts suspicious behavior, Conduct blocks it before the command is delivered to the external tool, or leaves an audit trail so that the security team can review it later.

Where We Stand

Currently, Conduct is an open-source security guardrail project (a control device for AI safety). [Reference: Conduct开源详解：为LLM与MCP… - OpenAI Hub] [Reference: ConductOpenSourceGuardrailsFORLLMANDMCPToolCalls]

One interesting point about this project is that its failure mode follows a ‘Fail-open (soft)’ approach. [Reference: GitHub - sseshachala/conductai: AI agent governance for teams.] It is designed so that even if the security system itself encounters an error, all functions of the AI assistant do not stop, which is an advantageous choice for organizations that prioritize business continuity.

Of course, installing just this tool will not eliminate all security threats. AI safety in actual work environments must have a ‘stack’ structure where multiple layers of guardrails are overlapped. [Reference: LLMGuardrails: Production Safety Layers Reference 2026] Conduct is an important layer responsible for the ‘tool execution stage’ among those many layers.

What’s Next

In the future, AI will evolve into ‘agents’ that go beyond simply reading and writing text to executing code, managing servers, and performing work automation. Accordingly, the importance of tools like Conduct, which inspect all AI tool calls, will continue to grow. We are entering an era where users must personally check tool input values and verify the results. [Reference: Tools- Model Context Protocol]

Developers will no longer just ponder “what an AI can do,” but rather “how we can control it safely.”

MindTickleBytes AI Reporter’s Perspective

Expanding the capabilities of AI is a domain of technology, but controlling that authority is a domain of trust. Open-source guardrails like Conduct are an important trend establishing a foundation where AI can safely coexist as a human tool. Transparent verification processes will actually accelerate technological progress.

References

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Test Your Understanding

Q1. What is the primary function of Conduct?

Developing AI models directly
Monitoring and blocking AI assistant tool executions before they run
Collecting training data for AI models

Conduct is a security project that captures an AI's intent to execute external tools (like MCP), checks for risks before the tool actually runs, and blocks it if necessary.

Q2. What are the main surfaces that Conduct monitors?

Web browser access history
Three surfaces: MCP layer, routers, and LLM calls
Users' personal password vaults

Conduct applies security policies across three enforcement surfaces: the MCP layer, routers, and LLM calls.

Q3. Which mode does Conduct use for failure (Failure mode)?

Fail-close (blocking)
Fail-open (allow/soft)
Forced immediate shutdown

Conduct adopts a 'Fail-open (soft)' approach, which prioritizes maintaining operations when issues arise in the security system.