Shifting security left in the SDLC is the most efficient means of engineering secure applications. However, the velocity of development can make this a daunting task. Integrating security intelligence into dev pipelines optimizes the power of automation for agility, speed, innovation, and delivery to efficiently identify software risks, enforce policies, and remediate any vulnerabilities.
When deploying open source coding frameworks, you should account for the potential risks and trade-offs inherited from these components. In today’s environment, where most applications have numerous open source issues and most organizations have hundreds (if not thousands) of applications, auditing these issues is a huge bottleneck. Open source scanning tools put a spotlight on general risks associated with open source components. Now, with susceptibility analysis, developers and AppSec engineers can automatically understand whether a vulnerability has been actually invoked in your applications and whether attacker-controlled input reaches that function—saving thousands of hours of time.
When developers write software, they sometimes make mistakes. Left undetected, these mistakes can lead to unintentional vulnerabilities that potentially compromise that software or the data it processes. Developers can reduce unintentional code-level security vulnerabilities by leveraging secure coding standards; selecting the most appropriate (and safe) languages, frameworks, and libraries; ensuring their proper use (especially use of their security features); and using automated analysis tools for static code analysis. Enabling developers to find security bugs within their native IDE environment in real time or when they check in code minimizes the costs of non-secure coding or developer mistakes.
Sending data offshore for dev and test is common practice. However, most data loss occurs from non-production copies of live production system data. Production copies hold sensitive customer data. Many copies of the database are made in order to support testing in different test environments. Whether data privacy, ISO, PCI, or any other regulation, best practices for compliance require live personal data to be anonymized in test. What’s needed is effective data protection for meaningful test and analytics. Reduce risks and increase compliance by anonymizing test data while maintaining the format and the meaning of the original data using NIST-approved algorithms.
Applications are exploding in volume and development velocity, overwhelming AppSec teams and processes. A resilient application security testing program supports extensible scanning capacity, from 1 scan to 1+n. It's about having the burst capability that you can turn on when you hit a threshold. Scale the static (SAST) and dynamic (DAST) testing in your CI/CD processes to the hundreds or even thousands of scans required. Leverage on premises, on demand, or a hybrid of both to best suit your testing needs.