Discrimination in OSINT is defined as:

Discrimination in OSINT is defined as:

• A. Secondary raw data filtered by analysis; can be agglomerated with editing
• B. Primary raw data collected from field sources
• C. Raw images before processing
• D. Final validated intelligence disseminated

Answer: (A)

Discrimination in OSINT is about sifting through information to identify what matters and turning raw material into usable insight. In practice, this means taking data that comes from open sources (often produced by others, i.e., secondary data), applying analysis to filter for relevance and credibility, and then editing or aggregating those pieces with other data to form a focused, actionable set of information. This step sits between collection and dissemination, ensuring only pertinent material is carried forward. It’s not describing collecting data firsthand (primary data), nor unprocessed images that require processing, nor the final, vetted intelligence that gets distributed.

Discrimination in OSINT is about sifting through information to identify what matters and turning raw material into usable insight. In practice, this means taking data that comes from open sources (often produced by others, i.e., secondary data), applying analysis to filter for relevance and credibility, and then editing or aggregating those pieces with other data to form a focused, actionable set of information. This step sits between collection and dissemination, ensuring only pertinent material is carried forward. It’s not describing collecting data firsthand (primary data), nor unprocessed images that require processing, nor the final, vetted intelligence that gets distributed.