AI coding agents are shipping code to your repositories right now. GitHub Copilot, Claude Code, Cursor – they’re opening pull requests, committing dependency changes, and refactoring production services. Ask most security teams what percentage of their codebase was written by an agent, and the honest answer is a shrug.
That’s a problem, because AI-generated code isn’t just more code. It’s code produced at a velocity and volume no AppSec program was designed for, by authors that never sat through your secure coding training. When an agent introduces a hardcoded secret or a vulnerable dependency, it looks exactly like every other commit in your queue. You can’t measure or attribute the risk. You can’t prioritize it. You can’t even see it.
Now you can.
AI agent commits, identified and attributed automatically
Cycode now identifies code committed by AI coding agents and links every agent commit to the security violations it introduced. Commits from GitHub Copilot, Claude Code, and Cursor are automatically identified, attributed to the specific agent that made them, and connected to any secrets, SAST, SCA, or IaC violations they carry.
The result: a clear answer to two questions every security leader is being asked right now. How much of our code is written by AI agents? And is that code introducing risk?
Three places this shows up in the platform
ADLC Agent commits dashboard – Your command center for agent-introduced risk. It aggregates agent commit volume over time and the violations tied to it, breaking down violations by severity, commits by code agent, and the repositories carrying the most AI-introduced risk. In one view you see total agent commits, how many carry violations, which agents are most active, and which repos deserve attention first.
Agent commits inventory – Every detected agent commit, in one searchable list. Filter by source, organization, repository, user, code agent, and whether the commit has violations. Each entry shows the commit message, the agent that authored it, and the violations it introduced by risk level. Drill into any commit for full detail: the agent, the user, the commit ID, and every open violation tied to it.
Violations view, now with agent context – A new AI Agent filter lets you slice your entire violations backlog down to agent-introduced findings. Each violation’s detail card displays an AI Agent Commit badge when an agent introduced it, so agent-introduced risk gets triaged alongside everything else – same workflows, same policies, new dimension of context.
Why attribution changes the game
Visibility into agent commits isn’t a vanity metric. It’s the foundation for governing AI in your development lifecycle.
With attribution in place, you can benchmark whether agent-written code is riskier than human-written code in your environment, or even against the different coding agents, and by how much. You can spot which repositories are seeing the heaviest agent activity and tighten guardrails where it matters. You can bring hard numbers to the conversations your CISO is already having about AI adoption, instead of estimates and anecdotes.
And because this runs on the same detection stack as everything else in Cycode – native secrets, SAST, SCA, and IaC scanning connected through one platform – agent-introduced violations come with the same context and accuracy you rely on for the rest of your code. No bolt-on tooling. No separate queue.
The AI development lifecycle needs AI-aware security
Agents are becoming first-class contributors to your codebase. Securing the AI development lifecycle starts with knowing what they’re doing in it. This release makes agent activity visible, measurable, and actionable – directly inside the platform your team already uses to secure everything else.
AI Agent Commit visibility is currently in early access for customers. Want to see how much of your code AI agents are writing – and whether it’s introducing risk? Book a demo and we’ll show you.
Agents are writing your code. Now you’re watching.
