---
title: "How to Write an Internal AI-Generated Code Policy (Using Debian and Linux's Playbook)"
url: "https://goremy.ai/blog/articles/how-to-write-an-internal-ai-generated-code-policy"
author: Luis Chavez-Mattos
editor: Luis Chavez-Mattos
editor_title: Director, Product
published: "2026-09-02T21:20:06.917Z"
updated: "2026-09-02T21:20:06.917Z"
topic: AI at Work & Governance
description: "Debian, the Linux kernel, and GCC just codified AI code rules. Here's how to turn their disclosure and accountability framework into your company's policy."
---

# How to Write an Internal AI-Generated Code Policy (Using Debian and Linux's Playbook)

> Debian, the Linux kernel, and GCC just codified AI code rules. Here's how to turn their disclosure and accountability framework into your company's policy.

Edited by Luis Chavez-Mattos, Director, Product · Published 2026-09-02

## How do you write an internal AI-generated code policy?

To create an internal AI-generated code policy, borrow the framework open-source projects just finished building: require disclosure of AI assistance in commits, keep a human accountable for every contribution, verify the AI tool's terms don't conflict with your licensing or confidentiality obligations, and require review before any bulk AI-generated change ships. That's the short version. The long version just got tested in public, by Debian, the Linux kernel, and GCC.

## TL;DR

- Open-source projects like Debian, the Linux kernel, and GCC have already **tested AI-generated code policies** in public, and you can borrow them.
- **63% of breached organizations** have no AI governance policy or are still drafting one, which exposes them to shadow AI risks.
- The Linux kernel requires an **Assisted-by tag** on AI-assisted commits so a named human stays accountable.
- GCC adopted a strict policy that **rejects legally significant contributions** derived from AI-generated content.
- Debian's position is that **using an AI tool does not reduce** the contributor's responsibility for the resulting work.
- A workable internal policy covers disclosure, human accountability, licensing and tooling terms, prompt confidentiality, and **review before bulk changes** ship.

## Has open source already fought this fight?

Most companies are still debating whether to write an AI code policy. The infrastructure projects underneath the entire software industry already finished the debate and published the results.

In August 2026, Debian held a General Resolution on LLM usage, with a voting period running from August 15 to August 28. [1] The Linux kernel finalized a formal AI Coding Assistants policy after a maintainer got caught submitting an undisclosed, fully LLM-written patch with a performance bug in it. [2] [3] GCC, the compiler toolchain underneath most of the open-source world, went further and adopted a policy that flatly rejects legally significant AI-derived contributions. [4]

Three major projects, three different risk tolerances, one shared set of mechanics. That's the blueprint worth stealing.

**Three open-source AI code policies compared**

| Option | Overall stance | Strictness | Requires disclosure tag | Human accountability required | Hard size threshold |
| --- | --- | --- | --- | --- | --- |
| Debian (Proposal B) | Neither endorses nor prohibits | medium | Yes | Yes | No |
| Linux kernel | Allowed with Assisted-by tagging | medium | Yes | Yes | No |
| GCC (recommended) | Rejects legally significant AI-derived contributions | high | No | Yes | Yes |

_Ratings are relative across these options, not absolute. Grounded in project policy documents cited in the article (s1, s3, s6, s7)._

## Why does your company already have a shadow AI code problem?

While these projects argued about governance, employees at ordinary companies kept shipping AI-assisted code with no policy at all. IBM's 2025 Cost of a Data Breach Report found that 63% of breached organizations either have no AI governance policy or are still drafting one. [5] Only 37% have any policy to manage AI use or detect shadow AI in the first place. [5] One in five organizations that suffered a breach traced it to shadow AI usage. [5]

**AI governance gap at breached organizations**

- 63%: Breached orgs with no AI governance policy, or still drafting one
- 37%: Breached orgs with any policy to manage AI use or detect shadow AI
- $670,000: Extra breach cost from high shadow AI use

The consequences aren't abstract. Shadow-AI-linked breaches exposed more personal data and intellectual property than the average breach, and organizations with high shadow AI use absorbed an extra $670,000 in breach costs compared to those with low or no shadow AI exposure. [5] We've covered the mechanics of this exposure before in [The Shadow AI Tech Stack](https://goremy.ai/blog/articles/5-shadow-ai-tools-employees-use-to-build-their-own-apps) and in [The Security Threat of Agentic Code](https://goremy.ai/blog/articles/ai-coding-assistant-security-risks-what-copilot-is-breaking). Open source just handed IT departments a tested answer.

## What did Debian's general resolution actually decide?

Debian's winning position, "Responsible Use of Generative AI," is worth reading closely because it states a principle most corporate policies skip entirely: using an AI tool does not reduce the contributor's responsibility for the resulting work, and accepting AI-generated material without human review breaks with established development practice. [1]

Across the eight proposals, a shared set of guardrails emerged:

- **Tooling legal compatibility.** Confirm the AI tool's terms of service don't conflict with your project's licensing. [1]
- **Licensing and attribution.** Track where AI-generated code came from and whether it carries copyright baggage. [1]
- **Accountability.** A named human owns the contribution, not the model. [1]
- **Disclosure.** Significant AI assistance gets flagged, not hidden. [1]
- **Prior discussion of bulk changes.** Large or automated AI-generated changes need sign-off before they land, not after. [1]
- **Confidentiality and privacy.** No feeding embargoed or confidential material into tools that transmit it to a third party. [1]

That's six clauses. It's also, almost verbatim, a policy document.

## What does the Linux kernel's AI Coding Assistants policy require?

The kernel's policy is more mechanical, and that's its value. It draws a hard line: AI agents must not add a Signed-off-by tag, because only a human can legally certify the Developer Certificate of Origin. [2] Instead, contributions carry an Assisted-by tag, for example "Assisted-by: LLM coccinelle sparse." [2]

The human submitter still has to review the code, confirm license compliance, and take full responsibility for what they submit. [2] Linus Torvalds has called outright AI bans "pointless posturing," treating AI as a tool rather than a threat, and the kernel's logic reflects that: hold the human liable, don't police the software. [3]

That logic exists because of an incident, not a theory. NVIDIA engineer Sasha Levin submitted a fully LLM-written patch to kernel 6.15 without disclosing it, and it shipped a performance regression. [3] The Assisted-by tag is the direct fix for that failure mode.

## What is GCC's stricter line when a project just says no?

Not every project wants Debian's nuance or the kernel's tag-and-trust model. GCC adopted a policy in July 2026 that declines any "legally significant" contribution containing or derived from LLM-generated content, using the GNU Project's own threshold of roughly 15 lines of code or text. [4] LLM-generated test cases and non-contribution uses like research and bug triage are still fine. [4]

That's a useful model for high-risk internal codebases: payment systems, authentication, anything touching regulated data. A blanket "no AI-derived changes above a line-count threshold" is blunt, but it's enforceable, and enforceable beats aspirational.

## What baseline rules does the Linux Foundation set for any project?

If Debian's policy is thorough and GCC's is strict, the Linux Foundation's org-wide guidance is the floor. It permits code or other content generated in whole or in part by AI tools to be contributed to Linux Foundation projects. [6] Notably, it explicitly defers to individual employers' stricter policies, acknowledging that a company's internal rules should govern its own developers even when they contribute externally. [6]

That's the right instinct for any internal policy too: set a floor, let stricter teams go further.

## How do you translate this into your own internal AI code policy?

Here's the framework, assembled from what these four projects already tested in public:

1. **Require a disclosure tag on every AI-assisted commit.** Mirror the kernel's Assisted-by format: agent name and model version, attached at commit time, not retrofitted later. [2]
2. **Put a human name on every contribution.** No AI agent signs off. A named engineer certifies they reviewed the code and takes responsibility for it, exactly as the kernel's DCO model requires. [2]
3. **Check licensing and tooling terms before adoption.** Confirm your AI coding tool's terms of service don't grant the vendor rights that conflict with your codebase's license or your client contracts, the same check Debian and the Linux Foundation both require. [1] [6]
4. **Set confidentiality rules for prompts.** Ban feeding proprietary code, credentials, or customer data into tools that transmit prompts to third parties for training, following Debian's confidentiality clause. [1]
5. **Require review before bulk or automated AI changes ship.** Large-scale AI-generated refactors or migrations need sign-off before they land, not after, per Debian's prior-discussion rule. [1]
6. **Draw a hard line for high-risk code.** For your most sensitive systems, consider GCC's approach: reject AI-derived contributions past a defined size threshold outright, rather than trying to review your way to safety. [4]

Six clauses, each already stress-tested by a project with more contributors and more legal exposure than most enterprises will ever face. Teams building this out from scratch may find it faster to start from the governance rules already laid out in [Securing the Code Your AI Wrote](https://goremy.ai/blog/articles/ai-generated-code-security-risks-5-governance-rules), then layer the disclosure-tag mechanics on top. And if the goal is giving employees a sanctioned way to build internal tools instead of quietly running unsanctioned AI code through personal accounts, a platform like [Remy](https://goremy.ai) gives IT a governed alternative to point shadow builders toward, rather than just a policy to hand them.

## What does it cost to skip this?

The IBM numbers aren't subtle. 97% of breached organizations with an AI-related incident lacked proper AI access controls. [5] Of organizations that do have an AI governance policy, only 34% actually audit for unsanctioned AI use. [5] Having a policy document and having a functioning one are different things, and the gap between them is where breaches happen.

**Breaches with an AI-related incident lacking proper access controls**

|  | Breached organizations |
| --- | --- |
| Lacked proper AI access controls | 97% |
| Had proper AI access controls | 3% |

_Based on organizations reporting a breach of AI models or applications._

Debian, the Linux kernel, and GCC didn't write these policies because they were bored. They wrote them because ungoverned AI contributions had already caused real problems, disclosed and undisclosed. Your engineering org has the same exposure, just with less public scrutiny and, so far, less pressure to fix it. The blueprint is sitting there, published, argued-over, and free. Adopting it is cheaper than writing your own from scratch, and considerably cheaper than the breach you'll eventually have without one.

## Frequently Asked Questions

### What should an internal AI-generated code policy actually require?

At minimum: a disclosure tag identifying AI assistance on commits, a named human accountable for reviewing and certifying each contribution, a check that your AI tool's terms don't conflict with licensing or confidentiality obligations, and mandatory review before bulk AI-generated changes ship.

### Should companies ban AI-generated code outright?

Most major open-source projects rejected outright bans as impractical. The Linux kernel treats AI as a tool and holds the human contributor accountable instead. GCC is the exception, rejecting legally significant AI-derived contributions above a line-count threshold, which is a reasonable model for your highest-risk codebases only.

### What is the Assisted-by tag and should companies use something like it?

It's the Linux kernel's required commit trailer disclosing which AI agent and model version helped write a contribution, paired with a human Signed-off-by tag that carries legal accountability. Companies can adapt the same pattern internally to track where AI assistance touched production code.

### How common is shadow AI risk inside companies right now?

IBM's 2025 breach report found 63% of breached organizations lack a finished AI governance policy, only 37% have any way to manage or detect shadow AI, and one in five breached organizations traced the incident to shadow AI use.

### Does an AI code policy need legal review before rollout?

Yes, at least for the licensing and tooling-terms clause. Confirming that your AI coding assistant's terms of service don't create IP or confidentiality conflicts is a legal question, and it's one Debian's and the Linux Foundation's policies both address directly.

## Sources

1. [General Resolution: LLM usage in Debian](https://www.debian.org/vote/2026/vote_002) (Debian)
2. [AI Coding Assistants](https://docs.kernel.org/process/coding-assistants.html) (The Linux Kernel documentation)
3. [Linux lays down the law on AI-generated code, says yes to Copilot, no to AI slop, and humans take the fall for mistakes](https://www.tomshardware.com/software/linux/linux-lays-down-the-law-on-ai-generated-code-yes-to-copilot-no-to-ai-slop-and-humans-take-the-fall-for-mistakes-after-months-of-fierce-debate-torvalds-and-maintainers-come-to-an-agreement) (Tom's Hardware)
4. [GCC steering committee announces AI policy](https://lwn.net/Articles/1086041/) (LWN.net)
5. [IBM Report: 13% Of Organizations Reported Breaches Of AI Models Or Applications, 97% Of Which Reported Lacking Proper AI Access Controls](https://newsroom.ibm.com/2025-07-30-ibm-report-13-of-organizations-reported-breaches-of-ai-models-or-applications,-97-of-which-reported-lacking-proper-ai-access-controls) (IBM Newsroom)
6. [Guidance Regarding Use of Generative AI Tools for Open Source Software Development](https://www.linuxfoundation.org/legal/generative-ai) (Linux Foundation)