SEMLA: Securing Enterprises via Machine-Learning-based Automation

The SEMLA project seeks to make the development of software systems more resilient, secure, and cost-effective. SEMLA leverages recent advancements in machine learning (ML) and artificial intelligence (AI) to automate critical yet common & time-consuming tasks in software development that often lead to catastrophic security vulnerabilities.

SEMLA aims to achieve the following three objectives:

• quickly learning about new vulnerabilities,
• enabling developers to generate secure code,
• realizing resilient infrastructure.

Expected effects and result

SEMLA will enable industry to:

• tackle security breaches,
• reallocate economic resources to the pursuit ofinnovation,
• improve productivity and time-to-market,
• grow faster, easier, & cheaper (e.g., startups and SMEs).

We aim to build a prototype that detect vulnerabilities and improve code capabilities aided by verification tools. Our secondary goal is to generate competences by distilling the insights of the project related to Large Language Models and code generation into two new courses established at KTH.

This project funded by Vinnova, Sweden’s innovation agency.