Abstract
Currently, a gap persists between artificial intelligence (AI) research and its widespread market application, particularly in the high-risk field of AI security technology, which remains a long-standing challenge. Despite substantial investment in AI research, a significant portion of promising innovations have failed to achieve commercialization. This study aims to bridge this gap by constructing an investment-driven framework for the commercialization of AI security technologies. Based on innovation management theory and drawing on multi-case analyses of AI security companies, this research proposes a structured model that integrates technology maturity assessment, investment phase segmentation, ecosystem coordination, and market validation mechanisms. The framework emphasizes the mediating role of strategic investment, which acts not only as a source of funding but also as a catalyst for bridging technological, organizational, and market uncertainties. The findings indicate that successful commercialization depends on a cyclical feedback loop between research outcomes, investor capabilities, and market signals. This research contributes to both theory and practice by providing investors, policymakers, and technology transfer professionals with replicable analytical tools.
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