Minimize False Positives in Your OSINT Investigations

What are False Positives in OSINT?

In the context of 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. As powerful as computers have become, most programs remain unable to understand the nuances of human language and the varied context within our communications.

Given that intelligence from the Surface, Deep & Dark Web is written by people and not code – determining context can be a tricky task that traditionally requires human analysis, as computers tend to match the exact keyword to exact keyword.

Without the context that NLP brings, you’ll drown in alert noise

What does this mean for Corporate & Information Security Teams?

False positives in any form are a time-sink. In the world of OSINT, it means sifting through a lot of content that is irrelevant to what you are looking for, solely because there was a 1:1 match to a keyword.

Beyond the negative impact on efficiency and morale, is the direct cost of valid threat alerts being missed due to teams pursuing false positives.
One missed alert indicating an insider threat, a threat to an executive, or sensitive data being sold on a Dark Web marketplace could lead to a significant loss in revenue, brand damage, and even the safety of staff.

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  • The financial cost of false positives to Corporate & Information Security teams
  • 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