Latest Dynatrace
During the Software Development Lifecycle (SDLC), multiple tools scan various artifacts as they progress through the development stages. An artifact like a container image reaches the deployment stage and eventually represents your running applications. At this point, you want to be sure the artifacts went through the proper security scanning procedures and didn't skip any essential validation.
Gaining complete visibility of the validation cycle isn't easy, as the scanning products used by different teams silo.
In this context
Dynatrace serves as a security platform that:
Aggregates the security scans for the deployed and running artifacts
Gives you complete visibility into the security validations those artifacts went through before reaching your production environment
Allows you to discover gaps in your security procedures and remediate them before they become a real risk
Our dashboard sample allows you to quickly visualize security scan events across the products and tools. It can also be a good foundation for tailoring further visual customization to meet your organization's posture analysis and reporting requirements.
Security architects and managers responsible for keeping the security scan procedures aligned with the security standards.
Your organization uses multiple container image registries, such as
You want a security coverage report of the container images to determine which repositories undergo the proper scan procedures and which don't.
Our solution allows you to analyze which repositories and images have been scanned and, thus, identify those that haven't been scanned.
Scan events are generated in addition to the vulnerability-finding events when you set up automatic ingestion with AWS CloudFormation and also for cases when no vulnerabilities have been found during a scan.
Download our sample dashboard from GitHub.
Open Dashboards, select Upload, then select the downloaded file.
Example result:
Open Notebooks to query security scan events, using the data format in Semantic Dictionary.
For a better understanding of how to build your queries, see DQL query examples for ingested events.
Example analysis: