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Achieving Peak OSINT Analytics Efficiency with Artificial Intelligence

April 12, 2022

Open source intelligence (OSINT) plays a vital role in all types of investigations, both in the private and public sectors. And as society continues to go more digital, open source data is being used more frequently to piece together key components of increasingly complex cases.

The rate at which data is created is growing exponentially, and while this can certainly aid investigators, reviewing the sheer amount of data available can also slow down their operations. In fact, as the creation of data continues to pick up the pace, it’s more important now than ever for analysts to find ways to efficiently identify, organize, and analyze the information available to them.

In a post-Covid world, there is renewed emphasis on finding a thorough yet quick approach to open source intelligence analysis. Finding the elusive equilibrium between these two would allow investigators to more effectively prioritize the open source data they find and glean more actionable insights from it.

But first, it’s important to understand what exactly open source data is.

What Is Open Source Data?

The open source definition as it relates to data is any information that can be accessed publicly, such as news and magazine articles, personal videos and photos on media platforms, and even weather data in different regions. Gathering open source intelligence is a crucial aspect of modern investigations because it can create a more comprehensive understanding of a situation.

In fact, investigators can often obtain clear answers to vital questions solely by conducting an open source analysis. However, problems often arise when it comes to prioritizing useful information in the vast sea of available data, organizing that information into a digestible format, and taking the time to draw accurate conclusions from it. That’s why a solid strategy is vital when conducting open source reconnaissance.

Understanding Data Analysis

A wide variety of open source intelligence methods are used by analysts to gather the wide scope of data required to make informed decisions. However, deploying these methods inefficiently, e.g. taking too long to put together an analysis, can cause delays in the information gathering process and negatively impact the results of an investigation.

The good news is, OSINT analysis involves collecting the data that is already freely available on the web. There’s no need to take additional steps to access it, and the information is public and easy to find. This type of collection serves as a way for investigators to get an overview of what information is available in a non-intrusive way. Accessing this information also doesn’t leave a trail.

The not-so-good news is, extremely quick and targeted action is needed in order for investigators to gather the maximum amount of open source data with the minimum amount of effort and time spent.

How Artificial Intelligence Can Support Open Source Intelligence Analysis

The intelligence industry has kept pace with the proliferation of open source data by developing open source intelligence tools that use artificial intelligence to assist investigators in data analysis.

Open source intelligence software makes such analysis more efficient by automatically scanning the open, deep, and dark webs for relevant information. This alleviates the burden on analysts to manually identify the information themselves and often protects them from threats posed by incorrectly or insecurely accessing it.

In addition, artificial intelligence, along with machine learning algorithms, efficiently organizes and prioritizes the data in one central location that allows analysts and their teams to easily access and review it. As a result, investigators can focus their time and efforts on pursuing information that requires more active methods, such as metadata and other system-specific intelligence.

Prioritizing which open source intelligence sources to access by using artificial intelligence minimizes analysts’ footprints, reduces threats from insecurely accessing information, and produces more actionable results.

However, to achieve this, it’s important to use an open source technology that sufficiently meets analysts’ needs and uses multiple factors in its analysis.

Using Cobwebs to Enhance Open Source Analytics

The Cobwebs WEBINT platform significantly enhances open source analytics by incorporating artificial intelligence and machine learning algorithms into data analysis. It scans all layers of the web to safely and efficiently extract relevant information. The platform then centralizes it, empowering investigators to conduct a more thorough and comprehensive analysis.

Cobwebs aids investigators in overcoming the challenges posed by large amounts of unstructured data, limited or restricted access to sources, and an evolving web ecosystem. The user-friendly search engine also generates intelligent insights and supports investigators by providing unmatched situational awareness.

By using Cobwebs’ web intelligence platform, investigators can quickly uncover hidden data and complete an entire investigation within minutes.

Stephen Lerner

Stephen is an intelligence team leader at Cobwebs Technologies. He trains clients around the world on the use of Cobweb’s proprietary open-source intelligence platforms, and on the field of OSINT and WEBINT in general. In particular, he enjoys researching the psychology of intelligence analysis.



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