Analyze GitHub Security Alerts using GitHub Copilot's Ask mode

  • 8 minutes

Understanding why a secret scanning alert was triggered and how to remediate it effectively requires careful analysis. GitHub Copilot's Ask mode provides an interactive way to analyze exposed credentials, understand their security implications, and plan appropriate remediation strategies.

What is GitHub Copilot Ask mode?

GitHub Copilot's Ask mode is a conversational chat interface integrated into Visual Studio Code. You ask questions about your code, and Copilot responds using the context you provide. For secret scanning alerts, Ask mode functions as a security consultant who can explain why credentials are exposed, assess risks, and suggest remediation approaches.

Ask mode is ideal for:

  • Understanding why specific code triggered secret scanning alerts.
  • Analyzing the security implications of exposed credentials.
  • Exploring secure alternatives for managing secrets.
  • Planning remediation strategies before making code changes.
  • Learning security best practices in context.

Note

Ask mode doesn't modify your code files. It provides analysis, explanations, and suggestions that inform your remediation decisions.

Effective prompting strategies

Successful analysis with Ask mode depends on clear, specific prompts that provide context and focus on actionable insights. GitHub recommends using natural language that references specific code selections and includes relevant context about your environment.

Understanding the exposure

Start by confirming what was detected and why it's problematic. Select the code containing the secret and ask direct questions.

The following prompts help clarify the nature of the exposure:

  • "Explain why the selected code triggered a secret scanning alert."
  • "What type of credential is used in the selected code, and what service does it access?"
  • "Analyze the selected database connection string and identify all exposed credentials."
  • "Does the selection contain a real API key or could it be a false positive?"

GitHub Copilot analyzes the selected code and explains:

  • The specific pattern that triggered the alert (for example, "matches Stripe's API key format starting with 'sk_live_'").
  • The type of credential and its characteristics (for example, "hard-coded string literal" or "matches known service pattern").
  • The service or system that the credential accesses.
  • The visibility of the exposure (who can see it).

This confirmation helps you understand whether you're dealing with a genuine secret or a false positive.

Assessing the risk

It's important to understand the potential impact and how to prioritize remediation. Include context about your repository visibility and environment.

The following prompts help evaluate the severity of the exposure:

  • "What could an attacker do with the selected exposed API key?"
  • "What's the risk level of exposing this database connection string in a public repository?"
  • "What sensitive data or systems are at risk due to this secret?"
  • "How severe is this exposure compared to other credential types?"

GitHub Copilot analyzes the credential type and explains:

  • Specific actions an attacker could perform (financial transactions, data access, system modifications).
  • Categories of risk (financial impact, data breaches, business disruption, compliance violations).
  • Urgency level based on credential type and repository visibility.
  • Immediate actions required (revoke credential, review logs, rotate secrets).
  • Potential compliance implications, such as Payment Card Industry Data Security Standard (PCI DSS).

This risk assessment helps you prioritize remediation and communicate urgency to stakeholders.

Understanding the context

Analyze how the secret is used to ensure remediation preserves functionality. Use @workspace to search across files.

The following prompts help explore usage context:

  • "How is the selected API key used in the application?"
  • "What functions or classes depend on the selected database connection string?"
  • "@workspace Find all code paths that reference this credential."
  • "What breaks if I remove this hard-coded secret?"

GitHub Copilot analyzes the workspace and identifies:

  • All files and locations where the secret is referenced.
  • Methods, classes, or services that depend on the credential.
  • How the credential is initialized and consumed.
  • Critical functionality that relies on the secret.
  • Dependencies between components that use the credential.
  • Recommendations for updating all usage points during remediation.

This analysis ensures your remediation plan addresses all dependencies and maintains application functionality.

Planning remediation

Develop a comprehensive fix strategy. Be specific about your technology stack and environment.

The following prompts help develop a comprehensive fix strategy:

  • "What's the recommended way to securely store this API key in a .NET application?"
  • "Create a step-by-step plan for remediating this exposed database credential."
  • "How do I migrate from hard-coded secrets to Azure Key Vault?"
  • "Show me the best practice for managing this credential type in production."

GitHub Copilot recommends secure storage solutions and explains:

  • Appropriate secret management approaches for your stack (Azure Key Vault, User Secrets, environment variables).
  • Priority ranking of options (production vs. development environments).
  • Step-by-step implementation guidance.
  • Code examples showing secure configuration access patterns.
  • Platform-specific integration details (dependency injection, configuration builders).
  • Testing considerations before deployment.

This guidance provides a clear roadmap for implementing secure secret management.

Identify similar problems elsewhere using workspace-wide analysis.

The following prompts help identify similar problems elsewhere in the workspace:

  • "@workspace Search for other hard-coded API keys or connection strings."
  • "Are there other credentials in configuration files that need remediation?"
  • "@workspace Find files that contain patterns similar to the selected secret."
  • "What other security concerns exist in files that handle external service integration?"

GitHub Copilot searches the workspace and identifies:

  • Other files containing similar credential patterns.
  • Configuration files that might contain hard-coded secrets.
  • Related security concerns in authentication or integration code.
  • Consistent patterns suggesting systematic issues.
  • Recommendations for workspace-wide remediation.

This comprehensive scan ensures you address all instances of the problem, not just the one that triggered the alert.

Providing effective context

The quality of GitHub Copilot's analysis depends on the context you provide.

Add relevant files and code

Providing comprehensive context helps Copilot deliver more accurate analysis:

  • Select the specific code containing the exposed secret before asking questions.
  • Use the Add Context button in the chat interface to include:
    • The file containing the secret.
    • Configuration files (appsettings.json, .env.example).
    • Files that use the secret (service classes, controllers).
  • Reference the alert details from GitHub's Security tab (secret type, file path, line number).

Write clear problem descriptions

When asking about a secret scanning alert, include key details from the GitHub alert to provide complete context.

You can use the following structure as a template for your prompts:

I have a GitHub secret scanning alert for a [SECRET TYPE] in [FILE PATH] line [NUMBER]. 
This is a [public/private] repository. I need to understand:
1. [Specific question about access/impact]
2. [Specific question about rotation]
3. [Specific question about secure storage]

Here's an example that uses the template:

I have a GitHub secret scanning alert for a Stripe API key in src/PaymentProcessor.cs line 15. 
This is a public repository. I need to understand:
1. What access this key provides
2. How to safely rotate it
3. How to implement secure storage without breaking payment processing

Writing clear problem descriptions with specific details helps GitHub Copilot to:

  • Understand the exact credential type and location.
  • Assess risk based on repository visibility.
  • Provide targeted remediation guidance for your specific scenario.
  • Address multiple concerns in a single, comprehensive response.

This structured approach follows GitHub's recommendation to provide context upfront rather than through multiple back-and-forth exchanges.

Iterative analysis workflow

GitHub Copilot's Ask mode is most effective when prompts support an iterative approach to analyzing issues and formulating a remediation strategy.

Follow this systematic approach when analyzing secret scanning alerts:

  1. Review the alert in GitHub's Security tab and note the secret type, file path, and line number.
  2. Open the repository in Visual Studio Code and navigate to the identified file.
  3. Select the code containing the exposed secret and surrounding context.
  4. Apply the prompting strategies from earlier sections in sequence: understanding the exposure, assessing the risk, understanding the context, planning remediation, and finding related issues.
  5. Document your findings and remediation plan as you work through the analysis.

Best practices for GitHub Copilot's Ask mode

Following best practice guidance can help maximize the effectiveness of your analysis sessions.

Start broad, then focus

Begin with general questions, then progressively drill down into specifics.

Consider a scenario that involves analyzing an exposed API key in a selected code snippet:

  1. "What does the selected code do and why is it a problem?"
  2. "What are the specific risks associated with the selected code?"
  3. "How is the API key used in the application?"
  4. "Can you outline a remediation plan?"
  5. "Are there any related issues within the codebase?"

Document your findings

As you analyze, document key points:

  • Credential type and purpose.
  • Services or data it accesses.
  • Usage locations in the codebase.
  • Remediation plan steps.
  • Related issues discovered.

Ask follow-up questions

Don't hesitate to ask for clarification:

  • "Can you explain that in more detail?"
  • "What are the pros and cons of this approach?"
  • "What testing should I do after implementing this change?"

Iterate your understanding

If an answer isn't clear, rephrase your question or provide more context:

  • Add more files to the chat context.
  • Be more specific about your environment.
  • Break complex questions into smaller queries.

Considerations for different secret types

Different secrets require specific analysis approaches:

API keys and tokens

When analyzing API key exposures:

  • Identify the service provider and access scope.
  • Determine if it's production or test environment.
  • Check if the service provider was notified (partner validation).
  • Understand permissions the key grants.

Database connection strings

For connection string exposures:

  • Identify all credentials in the string (username, password, server).
  • Determine production vs. development database.
  • Assess what data the database contains.
  • Check if the database is publicly accessible.

Private keys and certificates

For private key exposures:

  • Determine usage (encryption, signing, SSH).
  • Identify systems or data protected by the key.
  • Assess rotation impact.
  • Understand replacement requirements.

OAuth credentials

For OAuth secret exposures:

  • Identify what user data the credentials access.
  • Determine token lifespan (long-lived vs. short-lived).
  • Check for refresh token access.
  • Assess scope of granted permissions.

Understanding these differences helps you ask more targeted analysis questions and develop appropriate remediation strategies.