<![CDATA[Background. The North Natuna Sea faces overlapping maritime security challenges involving contested activities, fragmented surveillance information, and emerging hybrid threats. These conditions demand timely intelligence and coordinated public policy without compromising legal accountability. Purpose. This study aims to examine how big data analytics and artificial intelligence can support maritime intelligence integration and inform responsive policies for hybrid threat mitigation. Method. A mixed-methods research framework is proposed, combining policy document analysis, stakeholder interviews, and quantitative evaluation of anonymized maritime surveillance indicators. The analytical approach integrates data quality assessment, predictive modeling, and thematic analysis to evaluate institutional coordination, decision support, and governance safeguards. Results. The proposed framework identifies potential benefits in detecting anomalous maritime patterns, improving information sharing, and prioritizing policy responses. However, its anticipated constraints include data fragmentation, algorithmic uncertainty, institutional interoperability, and risks to transparency and privacy. (Note: These propositions remain unverified until empirical analysis is completed). Conclusion. The study concludes that AI-enabled maritime intelligence should complement, rather than replace, human judgment and established legal procedures. Effective adoption requires interoperable data standards, accountable oversight, rigorous model validation, and sustained interagency collaboration. The framework offers a testable basis for strengthening evidence-informed maritime governance while avoiding unsupported claims about operational effectiveness or threat reduction in the North Natuna Sea.]]>
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