AI Software Delivery Control gives AppSec, platform, and security leadership teams a way to answer a practical question: which AI-assisted workflows can change real systems, what authority do they use, which systems can they affect, and what approval or evidence exists?
Why this exists now
AI coding tools are no longer only writing suggestions in an editor. Vendor documentation now describes agents that can work on branches, create pull requests, run tests, use tools, and operate under permission systems. That changes the security job from “is this code good?” to “which action path did this create, and can we prove it later?”
The first enterprise control problem is not broad AI governance. It is software-delivery authority: repos, workflow files, PR-linked provenance, CI jobs, package scripts, MCP tool configs, credentials, cloud commands, and release paths.
The control model
A useful control model starts with the action path, not the model name. The same AI-assisted change can be low-risk in a docs repo and high-risk in a workflow that can publish packages or trigger a production-adjacent deployment.
Visibility
Find AI-assisted delivery paths in repos, workflow files, MCP configs, scripts, and credential references.
Authority
Map the token, identity, role, OAuth grant, or inherited permission used by the workflow.
Decision
Classify each path as allowed, approval-required, or blocked based on reachable action and target.
Evidence
Keep an evidence pack covering actor, owner, action, target, approval decision, timestamp, validation, and outcome.
The action-control graph behind the category
The Agent Action BOM is the first artifact. The action-control graph is the system object behind it: a connected view of owner, workflow, task, tool, credential, target, approval rule, policy decision, and evidence.
This matters because AI-assisted delivery authority is fragmented across tools. A repo permission, CI secret, MCP tool, package script, and release job may each look manageable alone. The risk becomes clear when they form a path to consequential change.
What it is not
AI Software Delivery Control does not replace secure code review, SAST, secret scanning, IAM, PAM, NHI management, CI/CD policy, or runtime agent gateways. Those are still useful control points.
The gap is between them: the software-delivery action path that ties an AI-assisted workflow to credentials, tools, code changes, CI/CD, cloud systems, approval, and evidence.
Where to start
- Pick one engineering team already using AI coding tools.
- Scan two to three repos or workflows that are close to CI/CD, credentials, cloud, or release paths.
- Map owner, workflow, credential, reachable action, target, approval rule, policy decision, and evidence.
- Set a first policy boundary: allowed, approval-required, or blocked.
- Repeat when a new agent, MCP tool, workflow, or credential pattern appears.
Who should care
- AppSec: find AI-assisted change paths before they reach production.
- Platform and DevOps: keep delivery moving without invisible CI/CD and credential risk.
- Security leadership: answer customer, auditor, and incident questions with evidence.
- Engineering leadership: adopt AI coding tools without turning security into a late blocker.
Source notes
- GitHub documents Copilot cloud agent as an autonomous software development agent that can make code changes on a branch and create pull requests. GitHub Docs
- Claude Code documents permission modes, allow/ask/deny rules, and hooks that can evaluate tool calls before execution. Claude Code Docs
- MCP security guidance covers consent, token passthrough, local server compromise, and scope minimization concerns for tool-connected systems. MCP Security Best Practices
Turn the category into a first assessment.
Clyra starts with a private assessment, action-control graph, Agent Action BOM, and evidence pack for two to three repos or workflows.
Request assessment