Artificial-intelligence-based proactive cybersecurity for integration processes

Abstract

'Prevention is better than cure' aptly applies to the field of cybersecurity. However, state-of-the-art integration systems generally perform late-stage scanning that is reactive, rather than proactive. This can be particularly dangerous in a low-code environment, in which the integration processes may be constructed by novice users without expertise in mitigating cybersecurity threats. Accordingly, embodiments utilize artificial intelligence to proactively detect and resolve cybersecurity threats during the design stage of integration processes. For example, a generative model may be used with crowd-sourced integration configurations to generate synthetic integration data that are targeted towards specific cybersecurity threat endpoints. This synthetic integration data may be input to an integration process, during integration testing within a test environment, to detect and resolve cybersecurity threats, prior to deployment of the integration process to a production environment.