Security teams often know that a security feature is enabled somewhere in an organization, but that is not the same as knowing whether it is actually enabled across the repositories that matter.

That distinction becomes important with AI Scan for pull requests. An organization can have a policy in place while individual repositories have different effective states because of configuration, prerequisites, or repository-level settings. Until recently, checking that status across a large organization required more manual investigation.

GitHub has now added AI Scan enablement status to the Security Overview coverage view. Organization and enterprise administrators can see how many repositories have AI Scan enabled or not enabled, inspect the effective status of individual repositories, filter the coverage view, and include the status in CSV exports.

The change may look like a small dashboard improvement, but it addresses a practical security-management problem: knowing whether a security control is actually deployed is part of managing that control.

What Is GitHub AI Scan for Pull Requests?

AI Scan for pull requests is a GitHub code-scanning capability designed to identify security issues in pull-request changes using AI-based analysis.

The important operational detail is that enabling an AI security feature is not the same as receiving useful coverage everywhere. Repository eligibility, organization configuration, enterprise policy, prerequisites, and repository-level choices can all affect whether the feature is effectively enabled.

That is why a simple organization-level setting is not enough for security teams managing many repositories.

Consider an organization with:

Engineering
├── payments-api
├── customer-portal
├── mobile-backend
├── internal-tools
└── data-platform

A security administrator may want to answer a very simple question:

Which of these repositories actually have AI Scan for pull requests enabled?

That is the problem the new Security Overview coverage information helps solve.

What Changed in Security Overview?

GitHub has added AI Scan for pull requests to the coverage view in Security Overview.

The summary now shows counts for repositories where the feature is:

Enabled
Not enabled

The repository-level rows also show the effective AI Scan enablement status. GitHub additionally provides filters for:

code-scanning-ai-scan-pr-scan:enabled
code-scanning-ai-scan-pr-scan:not-enabled

The coverage data can also be exported as CSV, with a dedicated column for the AI Scan status.

That makes the feature useful beyond simply looking at a dashboard. Security teams can use the exported information for internal reporting, adoption tracking, or further analysis.

Why "Enabled" Does Not Mean a Simple Repository Setting

One detail is easy to miss.

GitHub describes the displayed enabled state as the effective enablement after enterprise policy, organization configuration, prerequisites, and repository opt-out settings have been considered.

That is a better model than treating the repository setting in isolation.

For example, suppose a repository appears to have AI Scan configured, but an enterprise-level policy prevents the feature from operating. A repository-level inspection alone could give an administrator the wrong impression.

The effective state answers a more useful question:

Can AI Scan actually operate for this repository?

rather than simply:

Does this repository have a setting configured?

That distinction is important when security controls are managed at multiple organizational levels.

"Not Enabled" Does Not Tell You Why

There is another important limitation.

A repository shown as not enabled can include repositories that are not eligible for AI Scan. Security Overview does not distinguish the specific reason for the not enabled state.

That means administrators should not interpret the coverage table as a complete remediation report.

For example:

Repository A → not enabled
Repository B → not enabled
Repository C → not enabled

The three repositories may have completely different underlying situations.

One might have the feature intentionally disabled. Another might not satisfy the required prerequisites. A third might simply be ineligible.

The dashboard tells you where coverage is missing, but additional investigation may still be necessary to determine why.

That is a useful distinction when building internal security processes around this data.

How Security Teams Can Use the Coverage View

A practical workflow starts with the organization or enterprise Security Overview and its Coverage view.

The general process is:

  1. Open the organization's security and quality area.

  2. Open the Security Overview.

  3. Navigate to the Coverage view.

  4. Review the AI Scan for pull requests summary.

  5. Filter repositories using the AI Scan status.

  6. Investigate repositories reported as not enabled.

  7. Export the coverage information when an external report or further analysis is required.

GitHub's documentation describes the Coverage view as a way to assess adoption of security features across repositories. For AI Scan, the summary and repository list now include the feature's effective enablement status.

The important part is that this turns AI Scan from something an administrator enables into something that can also be measured across the organization.

Filtering Matters More Than It First Appears

The new filters are particularly useful in organizations with a large number of repositories.

Instead of manually inspecting every repository, administrators can narrow the view to repositories where AI Scan is enabled or not enabled.

For example:

code-scanning-ai-scan-pr-scan:not-enabled

can be used to focus on repositories that require attention.

That changes the workflow from:

Inspect every repository
        ↓
Determine AI Scan status
        ↓
Record missing repositories

to:

Filter for missing coverage
        ↓
Review affected repositories
        ↓
Determine why they are not enabled
        ↓
Take corrective action where appropriate

For large organizations, that difference matters. Security administration is often less about enabling a feature once and more about continuously identifying gaps as repositories are created, moved, or reconfigured.

CSV Export Makes the Feature More Useful for Governance

The new coverage information is also included in CSV exports through a dedicated column for Code Scanning AI Scan for pull requests.

That opens up several practical uses.

A security team could import the export into an internal reporting system and track repository coverage over time. Engineering leadership could use it to identify teams with repositories that have not adopted the feature. Security operations could combine the data with other repository metadata for internal assessments.

The important point is that the dashboard does not have to be the final destination for the information.

For example, a simple internal report could eventually look like:

Repository

Team

AI Scan Status

Follow-up

payments-api

Payments

Enabled

None

customer-portal

Web

Not enabled

Investigate

data-platform

Data

Enabled

None

internal-tools

Platform

Not enabled

Check eligibility

The exact reporting process will depend on the organization's security tooling, but the CSV field makes the data easier to move into those workflows.

AI Scan APIs Already Enable Programmatic Management

The Security Overview change is particularly interesting when combined with GitHub's existing AI Scan APIs.

GitHub introduced REST API endpoints for managing AI Scan for pull requests at both the organization and repository levels in public preview. The APIs can read and update whether AI Scan is enabled for supported organizations and repositories.

That creates a useful separation between two jobs:

API
 ↓
Configure AI Scan

Security Overview
 ↓
Measure effective coverage

A security platform could potentially use the API to manage configuration while using coverage information to determine where adoption gaps remain.

However, automation should not simply turn the feature on everywhere without considering eligibility, repository ownership, security requirements, and organizational policy.

Common Mistakes

Assuming Organization Configuration Means Full Coverage

An administrator may enable a security feature at the organization level and assume every repository is covered.

That assumption can be wrong when repository-level configuration, prerequisites, enterprise policy, or eligibility affect effective enablement.

The correction is simple: treat organization configuration and repository coverage as separate questions.

Treating "Not Enabled" as a Failure

A not enabled status should be investigated, not automatically treated as a configuration error.

Some repositories may not be eligible for the feature, while others may have been intentionally excluded.

The coverage view identifies a gap. It does not necessarily identify a defect.

Measuring Adoption Without Checking Repository Context

A raw percentage such as:

AI Scan enabled: 82%

may look useful, but the number alone does not tell you whether the remaining 18% represents important production repositories, experimental projects, archived work, or repositories that cannot use the feature.

Security coverage metrics are much more useful when combined with repository ownership, criticality, and business context.

Troubleshooting AI Scan Coverage

When a repository appears as not enabled, start by checking the configuration hierarchy rather than immediately changing repository settings.

Look at:

  1. Enterprise-level policy.

  2. Organization-level configuration.

  3. Repository-level AI Scan settings.

  4. Required prerequisites.

  5. Whether the repository is eligible for the feature.

  6. Whether the repository has intentionally opted out.

Security Overview itself does not distinguish every reason behind not enabled, so administrators may need to inspect the repository and organizational configuration separately.

This is also where permissions matter. Security Overview only exposes information according to the viewer's access to the organization, repositories, and security data.

If two administrators see different repository coverage, permissions and repository visibility should therefore be among the first things to check.

Production and Governance Considerations

AI-assisted security scanning should be treated as another layer of application security, not as a replacement for existing security controls.

A repository being covered by AI Scan does not mean the application is secure. Security Overview itself warns that the absence of alerts does not prove that vulnerabilities or code errors do not exist, particularly when a security feature is not enabled for a repository.

The useful role of the new coverage information is therefore operational visibility.

It can help answer questions such as:

Those are governance questions rather than vulnerability-detection questions, and that distinction keeps the feature's role clear.

Advantages and Disadvantages

Advantages

Organization-wide visibility: Security administrators no longer have to rely only on individual repository settings to understand AI Scan adoption. The coverage view provides a consolidated picture across repositories.

Effective status rather than configuration alone: The displayed state accounts for the configuration layers that determine whether AI Scan is actually enabled for the repository.

Useful filtering: Administrators can focus directly on repositories with or without AI Scan enabled instead of manually scanning the entire repository list.

Exportable data: Including the status in CSV exports makes it easier to integrate coverage information into internal security and governance processes.

Disadvantages

Limited explanation for missing coverage: A not enabled result does not identify the exact reason. Additional investigation is still required.

Coverage is not the same as security: Enabling AI Scan does not guarantee that an application has no security vulnerabilities.

Administrative complexity remains: Enterprise policies, organization settings, repository configuration, prerequisites, and eligibility can still make troubleshooting more involved.

Summary

GitHub's addition of AI Scan for pull requests to Security Overview addresses a practical problem that often gets overlooked when security features are introduced: adoption needs to be measurable.

Organization and enterprise administrators can now see enabled and not-enabled repository counts, inspect effective repository status, filter the coverage view, and export AI Scan status as part of their security data.

The most useful part of the change is not the dashboard itself. It is the shift from asking whether an organization has enabled an AI security capability to asking whether the repositories that matter are actually covered.

For teams managing GitHub security at scale, that is a much more useful question.