Synthesizing the raw intelligence into a usable state.
The collection stage of the intelligence process typically yields large amounts of unfiltered data, which requires organization. Substantial intelligence resources are devoted to the synthesis of this data into a form that intelligence analysts can use. Information filtering techniques include:
- Exploiting imagery
- Decoding messages and translating broadcasts
- Reducing telemetry to meaningful measures
- Preparing information for computer processing, storage, and retrieval
- Placing human-source reports into a form and context to make them more understandable
Exploitation seeks to determine whether the information is what it purports to be and what its value is to the IC. Exploitation is also sometimes referred to as analysis. One of the most significant challenges associated with using OSINT products is the sheer volume of information that is publicly available and the degrees of reliability inherent in that information. Thus, a great deal of time in analyzing OSINT must be spent on separating the reliable, “good” intelligence from the “bad.” Analysts must be able to “gather, judge, and sort information, know and handle limitations, and understand different users, needs, tasks, information mix, organization, institutions, and the law.” The finished product should provide analytical conclusions guided by the available sources.
We break exploitation down into three phases: authenticating, evaluating credibility, and contextualizing.
Authenticating seeks to verify whether the information is what it says it is. Authentication may need to occur concurrently with data-aggregation functions to ensure that a data sample or composite is not wrongly skewed.
Evaluating credibility like authentication, is fairly straightforward for traditional media content and gray literature but extremely difficult for social media content. A credibility measure seeks to determine whether the information is trustworthy—that is, whether it was provided without intent to deny or deceive and whether its source has plausible access to it.
Contextualizing allows the open-source analyst to relay subject-matter expertise to the ultimate consumer. This may involve comments about the source that provide additional information, such as information relevant to credibility. Contextualizing could also involve compiling multiple items of OSIF from any deliverable into a product that provides a more comprehensive picture of an issue.