Open Source Intelligence (OSINT)

Open-source intelligence (OSINT) is data collected from publicly available sources to be used in an intelligence context. In the intelligence community, the term “open” refers to overt, publicly available sources (as opposed to covert or clandestine sources). It is not related to open-source software or collective intelligence.

OSINT under one name or another has been around for hundreds of years. With the advent of instant communications and rapid information transfer, a great deal of actionable and predictive intelligence can now be obtained from public, unclassified sources.

LP3 is in a unique position to provide OSINT as a Service by developing relationships with OSINT aggregators of deep and vast content types. No single provider can give the comprehensive deep dive into OSINT that LP3 can provide.

Open Source Intelligence as a Service (OaaS)

A comprehensive intelligence and security offering consisting of:

• Multiple global sources; public and commercial data
• Fully fused searches and alerts by topic, person, location and associated networks
• Social media sentiment
• Facial recognition
• Object Recognition
• Deep and Dark Web
• Global mobile device data
• Pattern-of-Life analysis
• Financial Crime & Terrorism Connections
• Global imagery 2x/Day
• Supply Chain Risk Management & Intelligence
• Take Down as a Service

Analytical Services

• Lexical Analysis – he ability to simultaneously aggregate large bodies of text from all over the world at any given time of day from multiple sources across an array of languages, cultures, and nationalities.
• Keyness Analysis – measure of how often a word occurs in a given sentence or piece of writing. creating a vivid picture of a speaker or writer based on the words he or she uses.
• Frequency Analysis – ability to either distinguish one corpus from another based on the occurrence of keywords in each body or compare a sample corpus to a large or larger corpus
• Clusters – A cluster is a sequence of two or more words which may not be a grammatical or meaningful unit in and of itself, but which can be included in a keyword analysis.
• Collocation – Collocation is the probability that any two of the words identified in a keyness analysis frequently occur together, typically within five words on either side of the word identified for investigation (also referred to as the “node”)
• Sentiment Analysis – Sentiment analysis identifies terms or entities about which a person has “an overall majority opinion which is not serent class.”
• Stance Analysis – While sentiment analysis shows how language can differentiate viewpoints between individuals or groups, stance analysis uses language preferences to indicate an individual’s underlying values or an expression of attitude toward a given concept.
• Natural Language Processing – translating, interpreting and processing large bodies of text in other languages.
• Machine Learning – All the lexical-analysis processes and terms described above, from calculating keyness to detecting collocations to translating materials and providing sentiment analysis, are made more efficient through machine learning