Entities' software and applications are increasingly accessible to third parties outside the organisation. Today, the transition to web, mobile and cloud environments has only helped to accelerate this exposure to third parties. This is why traditional security methods and mechanisms (even for on-premise applications) are not a sufficient defence to mitigate all the risks faced by organisations, which expose sensitive information that can be attacked and stolen by third parties.
Bosonit wants to contribute to protecting the weakest link in the technological chain, the applications, since in most cases development is carried out without audits to detect cybersecurity vulnerabilities. With this objective in mind, Balder is an innovative tool designed to analyse code in search of security vulnerabilities in source code. This tool provides an innovative and disruptive value, analysing the code by means of knowledge graphs, as well as being supported by algorithms from Machine Learning y Big Data for decision making. Thanks to this tool, it focuses on the security of the application from the foundations of the development.
This project, with a maximum budget of 163,624.41 euros, is funded by the European Union and the Recovery and Resilience Plan (RRP).
How does intelligent search in knowledge networks work in the BALDER project?
BALDER uses a static code analyser based on knowledge graphs to search for security vulnerabilities in source code. This analysis is supported by Machine Learning and Big Data algorithms to score and filter the vulnerabilities found.
What technologies are used in this project?
BALDER uses technologies such as Machine Learning, Big Data and static code analysis based on knowledge graphs to search for security vulnerabilities in source code.
How can the BALDER project improve the security and quality of source code in the technology industry?
BALDER aims to protect and strengthen applications, as many times application development is carried out without auditing for cybersecurity vulnerabilities, due to lack of knowledge. By using innovative tools such as Machine Learning and Big Data, BALDER can help identify and fix vulnerabilities before they are exploited by malicious attackers, thus improving the security and quality of source code in the technology industry.
Project financed by the European Union and by funds through the Recovery and Resilience Plan (RRP).
