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It is crucial to implement innovative artificial intelligence (AI)-powered network intrusion detection systems (NIDSes) to protect enterprise networks from cyberattacks, which have recently become more diverse and sophisticated. High-quality labeled training datasets are required to train AI-powered NIDSes; such datasets are globally scarce, and generating new training datasets is considered cumbersome. In this study, we investigate the possibility of an approach that integrates the strengths of existing security appliances to generate labeled training datasets that can be leveraged to develop brand-new AI-powered cybersecurity solutions. We begin by locating communication flows that the deployed NIDSes detect as suspicious, investigating their causal factors, and assigning appropriate labels in a universal format. Then, we output the packet data in the identified communication flows and the corresponding alert …