INTELLIGENCE SENSOR IN PMAM: OPERATIONALIZING THE SOLDIER AS A VECTOR FOR SYSTEMATIC DATA COLLECTION
DOI:
https://doi.org/10.56238/sevened2026.008-143Keywords:
Intelligence Sensor, SIPOM, Data Collection, Military Police, Public Security Intelligence, AmazonAbstract
The uniformed patrol officer represents an underutilized potential as a source of structured intelligence. This article analyzes the operationalization of the intelligence sensor in the Military Police of Amazonas (PMAM), investigating how the systematic transformation of the soldier into a data collection vector through operational intelligence techniques (OMD, surveillance, reconnaissance) expands the knowledge production capacity of the Intelligence System (SIPOM). Methodologically, qualitative descriptive research was adopted based on documentary analysis of RI-SIPOM (2021), DNISP (2015), constitutional jurisprudence (STF ADPF 635), and specialized literature in public security intelligence, compared with theoretical frameworks of Mission Command (Visacro, 2018; Alves, 2021) and military organizational innovation (Shultz, 2016). Results demonstrate that formal accreditation via Administrative Recruitment Process (PRA), integration with structured technical channels, and progressive training (16h + 32h + 8h/year) transform the soldier into a qualified sensor responsible for 40-60% of operational data collection in the Amazon environment. It was identified that institutional feedback mechanisms increase sensor motivation by 85%, while compliance with constitutional guarantees (ADPF 635, CNJ 2024) ensures legal operations. In the dispersed Amazonian context of vast territory (1,559,146 km²), the sensor model represents critical economy of means, reducing collection-analysis time from 5-7 days to 1-2 days. It is concluded that the structured institutionalization of the intelligence sensor, when accompanied by investment in training, procedurization, and integration with analytical systems, constitutes an institutional force multiplier capable of raising SIPOM effectiveness by 30-50%.
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