Stages of Open Source Intelligence

In order to provide useful intelligence to the target, open source intelligence follows a precise and precise approach. The CIA's "Intelligence Cycle" and "Intelligence Studies" describe this process in slightly different ways, but both have common collection, processing, analysis, production, and dissemination, with the former adding planning and direction as additional steps, the latter adds classification.

1. Collect data

Data is a critical asset in conducting any intelligence activity. Like any other intelligence method, open source intelligence relies heavily on data, which is extracted from publicly available sources. At this stage, the data source and the type of data to be collected must be identified. The data source can be a keyword, and the data type can be in the form of text, picture, video, etc. Using web intelligence monitoring systems such as Knowlesys Intelligence System, you can collect information from specified data sources (such as social media, search engines, online databases, dark web) through keywords.

2. Data processing

The processing step mainly involves validating and removing noise from the raw data received during the data collection phase to make it available for analysis. Filtering irrelevant data, translating text from one language to another, converting photo, audio and video files into useful data, and more are tasks performed during the processing stage. The large amount of data obtained from open sources makes it difficult to interpret and extract useful insights, requiring increased processing power, such as cloud storage and big data computing power.

3. Utilization of data

Also known as the analysis phase, the exploit is responsible for determining whether the material processed in the previous phase is what it claims to be, and its value to the intelligence community. The development phase consists of three steps such as authentication, credibility assessment, and contextualization. Verifying the authenticity and credibility of information is critical to developing trusted knowledge. Contextualization requires assembling several pieces of open-source information from any source into an output that provides a comprehensive understanding of a topic. The most common analysis methods are lexical analysis, semantic analysis, geospatial analysis, and social media analysis.

a. Lexical Analysis

Lexical analysis is a program that collects and analyzes large amounts of text from the Internet. Identifying frequently searched phrases on Google is a direct application of lexical analysis.

b. Semantic Analysis

Semantics is a subset of linguistics that studies the meaning of language. In the context of natural language processing, semantic analysis evaluates and reflects human language, analyzing words written in English and other natural languages with human-like interpretations.

c. Geospatial analysis

In environmental studies, geospatial analysis refers to the use of geographic data to discover important information about the environment that is referenced both geographically and temporally. The basic functions of geospatial analysis include the identification of environmental threats, the diffusion tracking of pollutants over time, the model research of environmental factors such as ocean temperature and acidification, and the correlation between different environmental characteristics and locations. Geospatial analysis refers in particular to data transformations that depend on geography. Geographic information systems, remote sensing, GPS, metadata, remote sensing, and georeferencing are some of the techniques used in this type of analysis. Geospatial analysis techniques are widely used in fields such as weather-related risks, urban planning and development, covert operations, and natural resource development. Data for geospatial analysis comes from a variety of sources, such as images uploaded to social media, mobile device data, and detailed GPS, the information stereotaxic sensors use to build meaningful intelligence.

d. Social media analysis

The practice of gathering the most basic information from people's social media platforms and drawing practical conclusions is called social media analysis. The information being analyzed comes from people's previous posts, conversations with their followers, early social media initiatives, and more. The purpose of social media analysis is to obtain valuable information on individual attitudes and preferences. Most users use social media to express their emotions such as happiness, anger, agreement, disagreement, and annoyance through text messages or posts. When individuals mention or talk about a business or product on social media, sentiment analysis methods can be used to determine the mood or emotion behind the phrases they use. The term used by individuals to express themselves about a scene, event, product, brand, company, or other subject A detailed analysis will provide public opinion on the subject under consideration. Organizations can use social media analytics to discover commonalities in consumer preferences and complaints, as well as talking about a person, business or event online, if they have the right tools.

4. Knowledge production-extraction

The final stage of open source intelligence is delivering meaningful intelligence reports to consumers. Since the report will be comprehensive and high priority, it can be shared directly with the judiciary, law enforcement agencies and other interested parties. Classification levels for open source intelligence products are also specified during the production phase. The details of collecting, analyzing and utilizing data may require a higher level of disaggregation. Allocation is an important part of the production phase. The most common way to share open source analysis is through formal reports. On the other hand, a product can be in the form of a verbal description or a visual representation. Systems such as Knowlesys Intelligence System provide data collection and graphing functions to make reviewing data easier.