ECCouncil 312-97 Exam With Confidence Using Practice Dumps

GitHub Code Scanning is a built-in security capability designed to automatically analyze source code for security vulnerabilities and coding errors. The primary and officially supported engine for GitHub Code Scanning isCodeQL. CodeQL works by converting source code into a database and running security queries to detect issues such as injection flaws, insecure coding patterns, and logic errors. William can enable CodeQL by configuring GitHub Actions with either the default or advanced CodeQL workflow. Once enabled, CodeQL scans are triggered on events such as code pushes and pull requests, and the results appear as code scanning alerts in the repository’s Security tab. Gauntlt is a security testing harness used mainly for infrastructure and application testing, GitMiner is used to discover sensitive data like secrets in repositories, and OWASP ZAP is a dynamic application security testing tool used against running applications. None of these tools configure GitHub’s native Code Scanning feature. Therefore, CodeQL is the correct tool to set up GitHub Code Scanning in the Code stage of a DevSecOps pipeline.

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In AWS-based DevSecOps pipelines, static analysis tools such as PHPStan commonly send their results to AWS services through event-driven processing. When PHPStan detects security issues, the results are typically parsed and processed by anAWS Lambda function, which can transform findings and forward them to AWS Security Hub. CloudFormation is used for infrastructure provisioning, AWS Config evaluates configuration compliance, and Elastic Beanstalk is an application deployment service—none of these are suited for parsing and relaying scan results. Lambda functions provide a scalable and serverless way to handle scan outputs automatically. This integration ensures that security findings are centralized, visible, and actionable, aligning with secure automation practices during the Code stage.

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Dynamic Application Security Testing (DAST) and fuzz testing require a running application in order to actively probe for vulnerabilities such as injection flaws, authentication bypasses, and improper input handling. These techniques are therefore performed after the application has been built and deployed to a testing environment, categorizing them astest-time checks. Commit-time and build-time checks rely primarily on static analysis and dependency scanning and do not exercise application behavior at runtime. Deploy-time checks focus on configuration validation rather than aggressive attack simulation. Test-time checks are specifically designed to uncover critical and high-severity vulnerabilities by mimicking real-world attack scenarios. Performing DAST and fuzz testing during this stage allows teams to detect exploitable flaws before production release, significantly strengthening application security.

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