The Attribution Problem Nobody's Talking About

You merged a pull request last week. Somewhere in that code, an AI tool helped generate a function. Your git log shows a neat little marker indicating collaboration happened. Problem solved, right?

Wrong. That marker tells you almost nothing useful about what the AI actually contributed, which lines it touched, or whether those lines pose any security risk. For solo developers and small teams, this gap between appearing to have accountability and actually having it matters more than you think.

As your codebase grows and you start asking uncomfortable questions later ("Did AI write this authentication logic?" or "Which version of which model generated this database query?"), a simple attribution tag becomes painfully inadequate.

Why Labels Aren't Evidence

Think of it this way: adding "Co-authored-by" to a commit message is like writing "inspected by QA" on a shipping box after the product already left the warehouse. The label arrives too late to capture what actually happened.

Here's what a basic attribution marker cannot tell you:

Which specific lines came from the AI versus manual edits
What prompt you actually used when requesting code
Which AI model generated the response
The exact timestamp when generation occurred
Whether AI-generated code touched sensitive areas like authentication or encryption
The raw response the model returned before you modified it
Temperature settings, token limits, or other model parameters

A git commit is a retrospective snapshot. By the time code reaches your repository, the detailed information about how that code was created has already vanished. The HTTP request headers are gone. The full model response was processed and discarded. The timing data evaporated in milliseconds.

Furthermore, attribution gets even trickier when you consider that false positives create noise. If your IDE marks AI authorship on code you wrote entirely by hand, or on files where AI features were disabled, the signal becomes unreliable. Developers stop trusting the markers.

The Real Security Question You Should Be Asking

Imagine this scenario: Six months from now, a security audit flags a potential vulnerability in production. Your auditor asks, "Was any part of this function AI-generated?" What evidence do you actually have?

A git trailer gives you a name and email address. It does not help you answer whether the vulnerable line came from an AI model, what that model was, or whether the same vulnerability might exist in other code generated with identical settings.

For indie developers and small teams, this creates a practical problem: liability without visibility. If something goes wrong, you cannot definitively trace the provenance of the code. You also cannot prove that you followed responsible practices when using AI tools.

Consider whether your current setup would satisfy:

A compliance officer asking about data handling in AI-assisted code
A lawyer during a code review after a security incident
Your own team when debugging an issue six months later
A client who needs to understand what percentage of their codebase involved AI assistance

Most developers would fail these tests with only git metadata available.

What Real Provenance Actually Looks Like

Instead of waiting until code reaches the repository, provenance capture records evidence at the moment AI code enters your editor. The system creates a detailed record that includes timestamps, model information, prompt details, and correlation data.

This approach captures several critical pieces of information:

Precise timing when the text appeared in your buffer
The complete prompt you sent to the model
The raw response the model returned
Model name and configuration parameters
Request and response identifiers for tracing
Explicit declarations of what data is missing or unavailable
Similarity scoring between request and insertion
File context that was available during generation

Additionally, the system explicitly documents gaps in captured data. A record that says "prompt-body: missing" is categorically more useful than no record at all. You know exactly what you don't know.

Compare this to a git trailer which has no granularity whatsoever. It either exists or it does not. There is no middle ground, no nuance, no declaration of what information was actually captured.

Building Better Accountability Into Your Workflow

If you are an indie developer or small team lead, you have a choice to make about how seriously you treat AI-assisted code. Currently, most tools default to minimal tracking.

Here are practical steps you can take today:

Disable automatic attribution until you have a system that captures actual provenance data
Document your AI usage manually in commit bodies or separate audit logs
Flag AI-assisted code in code reviews so it gets extra scrutiny from teammates
Create a policy for which tools and models you allow in your codebase
Keep records of prompts separately in a searchable format outside of git

For teams that need stronger guardrails, consider implementing a logging layer that captures AI interactions before code reaches your editor. You can build a simple proxy that logs requests and responses to a database or file system.

Here is a minimal example of what structured logging might look like:

{
  "timestamp": "2026-05-15T14:32:18.247Z",
  "event_type": "ai_code_insertion",
  "tool": "copilot",
  "model": "gpt-4",
  "file": "src/auth.js",
  "prompt": "Create a function to validate JWT tokens",
  "lines_inserted": 12,
  "contains_sensitive_keywords": ["password", "token", "secret"],
  "developer_id": "user@example.com",
  "diff_hash": "sha256:abc123..."
}

This simple structure gives you searchability later. You can query "show me all AI-generated code in authentication files" or "which models generated code containing hardcoded credentials."

Additionally, check out digital footprint audit tool concepts if you want to understand how to systematically catalog what data about your code exists in various systems.

The Broader Implication for Small Teams

This audit trail gap highlights a bigger issue: AI tooling is advancing faster than governance frameworks. Your IDE vendor ships features optimized for speed and convenience. Security and compliance catch up later.

For solo developers, this means you cannot rely on default settings. However, you also cannot implement enterprise-grade compliance infrastructure alone.

The practical sweet spot is capturing enough information to answer basic questions later without implementing a full security operations center. Structured logging of AI interactions is that middle ground.

Furthermore, as you scale from solo developer to small team, your AI governance needs will compound. Code reviews become harder when you cannot identify which lines an AI tool contributed. Onboarding becomes confusing when new team members cannot understand why certain code exists. Debugging becomes slower when you cannot correlate code changes to the prompts that generated them.

Consider reviewing OWASP security guidelines to understand which code areas demand the most scrutiny, then pay special attention to those areas when AI generates solutions for them.

What to Do Right Now

Start by auditing your current setup. Check whether your IDE is currently adding attribution markers automatically. If yes, verify that those markers are accurate.

Next, decide on a policy: Do you want to allow AI-assisted code in security-sensitive files? Do you want to require explicit approval before any AI tool runs? Do you want to maintain separate records of AI interactions?

Document that policy and share it with anyone who works on your codebase. Use top productivity tools to track when AI tools are being used so you maintain awareness of your own workflow.

Finally, keep watching how this space evolves. Tools are beginning to implement better provenance capture. Standards are emerging. However, today, the default behavior of most IDEs leaves you vulnerable to gaps in accountability.

The tools you use should help you understand what your code is made of. Right now, most of them do not. That gap is worth closing before it becomes a problem.