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Open-source information is publicly accessible material that can be lawfully acquired through requests, purchases, or observations. When this information is gathered, analyzed, and disseminated to a specific audience, open-source information transforms into open-source intelligence (OSINT). While conventional wisdom previously limited OSINT’s value to complementing classified intelligence, the ongoing technological revolution has significantly expanded the scope and applicability of OSINT. From aiding the intelligence community to forecasting market risks, OSINT now empowers a wide range of public and private sector actors.
The OSINT Cycle
Open-source information becomes OSINT after data goes through the intelligence cycle, which encompasses four essential components: collection, processing, analysis, and production.
The initial phase of the OSINT intelligence cycle pertains to data collection from open sources. OSINT encompasses four main categories: widely available data, targeted commercial data, individual experts, and grey literature.
- Widely available data includes information disseminated by newspapers, radio, television networks, and academic papers, among other publicly accessible sources. This category also includes government records, press releases, and public survey data.
- Targeted commercial data includes resources such as satellite imagery, customized datasets, market surveys, and harvested metadata.
- Individual experts refer to subject matter specialists who provide expertise relevant to the specific information needs of an OSINT analyst.
- Grey literature takes a more nuanced approach, including data published outside conventional channels, such as policy literature, white papers, speeches, and unclassified government documents.
Data collection methods can be active or passive based on the level of interaction with the information sources.
Processing and Analysis
The next stage of the OSINT cycle requires the collected data to undergo processing and analysis before being disseminated as the final product. This stage involves refining the gathered data by removing unwanted elements and translating and aggregating crucial information to enhance its usability for further manipulation.
The analysis stage of the OSINT cycle exploits the processed data to separate “good” and “bad” intelligence with the aim of increasing the reliability of the gathered intelligence. This is the most critical stage of the OSINT cycle because it includes verifying the authenticity of the processed intelligence. The outcome of this process will be added to the final product; hence it is important for the processed information to be verified, evaluated, and contextualized.
Once the raw data is collected, processed, and analyzed, it is ready for the final stage of the OSINT cycle, production. The primary objective of the final intelligence product is to offer analytical conclusions to a client, and its format may vary in accordance with the client’s specific requirements, ranging from visual briefings to written reports. In instances where OSINT findings relate to classified intelligence needs, these products may be classified by the IC.
Why AI Automation in OSINT Analysis?
In recent years, the applications of OSINT have expanded and diversified significantly. OSINT complements traditional intelligence methods like Human Intelligence (HUMINT) and Signal Intelligence (SIGINT) while simultaneously broadening the net utility of an organization. Although SIGINT, HUMINT, and other traditional intelligence-gathering methods involve high costs and serious risks to human lives, OSINT provides a far more cost-effective and safer solution.
AI automation in OSINT analysis harnesses tools such as data mining, satellite imagery analysis, and geodata identification, both freely available and commercially accessible. It enhances the timeliness of intelligence processes, allowing analysts and organizations to stay ahead of emerging technological trends and current events.
In recent times, governments have recognized the extensive potential of AI automation in OSINT analysis and integrated various applications into crisis management. For example, in the ongoing Russo-Ukrainian conflict, AI automation in OSINT analysis has influenced modern warfare by involving civilians in the targeting process, leveraging the widespread use of smartphones to extract intelligence from social media, and through partnering with private companies to use low-orbital satellites for tracking Russian movements across the frontline.
AI automation in OSINT analysis has also further expanded the application of the tradecraft by allowing private companies to gather valuable insights into a target market. By analyzing data from market surveys, customer rankings, risk assessments, and K-10 forms, businesses can fine-tune their strategies to meet specific market demands and gain a competitive edge in their respective industries.
AI automation in OSINT analysis also facilitates the exploration of nontraditional subjects, including implications of climate change, energy usage, environmental security, and issues related to organized crime.
Open Questions about Open Sources
OSINT applications have expanded beyond traditional intelligence sectors to encompass an array of fields, including journalism, business, and academia. As technology continues its rapid evolution, AI automation in OSINT analysis is poised to become even more influential. Between the proliferation of data in our digital age and the integration of emerging technologies, particularly Artificial Intelligence (AI), there are critical questions about the ethical use of OSINT and the need for robust privacy safeguards.
With increased access to information, the potential for misuse is a growing international concern. Striking a balance between open-source transparency and protecting individuals’ rights is already a challenge that OSINT practitioners and policymakers must address.
As AI becomes increasingly intertwined with OSINT, ensuring the accuracy, credibility, and accountability of automated data collection and analysis processes will be imperative. AI automation in OSINT analysis has the potential to revolutionize how we manipulate information, but it must be done ethically and responsibly to safeguard privacy and maintain public trust.