The basis of all data-recording, sharing, and retrieval, regardless of the type of data – from computer code to war stories told by grandparents to public accounting records – is language. This is no different for OSINT data. Practitioners collect and evaluate human-generated, open-source content, based on human language with tools that are coded with computer languages, using structured queries.
What this leads to is a basic misunderstanding between human and computer languages. It’s all about context and slang terms.
Open-Source Intelligence (OSINT), false positives occur when an alert is generated from a keyword or phrase being identified, but the context of the keyword is not the context intended. “We’re going to kill you” vs “This restaurant kills” as a simple example.
WHAT YOU’LL LEARN:
- The financial cost of false positives
- Techniques to minimize and reduce the impact of false positives on your team.
- How Natural Language Processing and Machine Learning are evolving to create a human-like, contextual understanding of language to reduce false positives.