The LAZARUS consortium is glad to organise this year's workshop "DevSecOps Research and Opportunities 2024" taking place on July 12, 2024 at Vienna (Austria). The event, co-located with the "IEEE European Symposium on Security and Privacy", aims to attract innovative, high-quality research on secure software development (DevSecOps) practices.
Scope and topics
Security should not be treated as an add-on to software products; rather, it should be deeply integrated within the whole Software Development Life-Cycle (SDLC). The need for this integration and the design of suitable methodologies to make agile software development secure are paving their way in the security community. In this context, we often refer to DevSecOps or SecDevOps when discussing security integration in agile software production. Recently, players such as NIST, Google, OWASP, and the Cloud Security Alliance proposed their frameworks for secure software development. However, this provides only an initial step towards tackling the challenges related to the security of the many and iterated steps of SDLC.
Machine learning and AI can play a crucial role in DevSecOps as they can be used to analyse large amounts of data, including network traffic and system logs, to identify potential security threats, monitor system behaviour and identify anomalies that may indicate a security breach. By using machine learning in DevSecOps, organisations can more effectively detect and respond to security threats and improve their overall security posture. Moreover, they can be used in an automated way to interweave security in existing DevOps pipelines.
With this workshop, we aim to attract novel contributions to the secure SDLC to foster the creation of more conscious, robust, resilient, and advanced methodologies to prevent security issues at the different stages of the development pipeline. Topics of interest include but are not limited to:
- Methodological approaches to agile secure software development
- Security testing integration in the software supply chain
- Static and dynamic software bill of materials
- Secure software development via cloud testing
- Security as a service
- Machine learning approaches to speed up security testing
- Maturity models for secure software development
- Declinations of DevSecOps in different fields
- Integration of incident and response team operations
- Artificial intelligence for software security analysis
- Tracking and handling updates along the software supply chains
- AI support to Secure Software Development
- Automated vulnerability detection
- AI & ML in fuzzing
- Automated application of software patches
- Strategies for meeting regulatory compliance and addressing security challenges in DevSecOps.