One of the biggest cybersecurity risks involves vulnerabilities in the application layer. After all, the best firewall is useless if the web application itself is vulnerable. Many companies have worked to mitigate these risks by investing in their AppSec programs. According to a recent (commissioned by Synopsys), 71% of companies surveyed now use AppSec tools for more than half of their software projects. Notably, over two-thirds of companies already use 11 or more automated application security testing (AST) tools, such as SAST, DAST, IAST, fuzz testing and container scanning solutions.
This is due, at least in part, to the fact that tool manufacturers have now made their products “DevOps-ready” and support suitable integrations with CI/CD pipelines. This makes it tempting to simply let AppSec scanners run in the pipelines, but that can introduce other problems.
Problems with AppSec in CI/CD Pipelines
Too many results: Developers can be inundated with findings, yet only a small percentage are likely to pose such a high risk that they need to be fixed immediately. But the prioritization guidelines are often formulated in separate documents and are ambiguous.
Development pipelines are slowed down: Build pipelines often run at frequent intervals; every second to every minute. Scans with AppSec tools may take several minutes or even hours.
Manual AppSec activities are left out: Not all AppSec activities can be automated, such as architecture risk analyses, threat models and penetration tests. Nevertheless, these are an essential part of the AppSec strategy.
Intelligent pipelines (i.e., intelligent, purpose-optimized automation and orchestration of the various AppSec tools and activities) are ideal for overcoming this challenge. Combined with the consolidation of scan results, a new category of solutions has emerged here, which Gartner dubbed application security orchestration and correlation, or ASOC for short, in 2019.
How Pipelines Become Intelligent
The “intelligence” lies in deciding which tools need to run at what time and what to do based on the results. So instead of scanning the entire codebase with AppSec tools at every commit, it dynamically decides which scanner needs to run and to what extent. This decision can take into account various parameters such as the scope of the actual code change, the risk profile of the application or the development stage of the software.
The risk profile of the application should also be considered. Web applications that are accessible from the internet and process sensitive data pose a greater security risk than an internal tool for generating documentation. Such risk profiles usually emerge from prior architectural risk analyses and threat models.
Furthermore, the scope of AppSec testing should be appropriate to the development stage of the application. Individual commits of a feature branch should be checked mainly by static code analysis for passwords and API tokens contained in the code and compliance with coding guidelines, such as SEI CERT, to support rapid development. Later on, during the merge request into the main branch, more extensive scans should be added, including deeper data flow analyses, which then detect cross-site scripting or SQL injection attacks. A longer runtime can be accepted here since such merge requests usually have to be approved according to the dual control principle.