Introduction
The world of Open Source Intelligence (OSINT) has undergone a profound transformation since the beginning of the 21st century and the affirmation of the Internet. Over fifteen years ago, when I started studying and applying OSINT, the geopolitical and technological environments were significantly different.
At that time, analysts, particularly those based in countries such as Italy (as me), had limited resources, therefore to learn more about OSINT they often relied on foundational works like The Psychology of Intelligence Analysis and the writings of Robert Steele, who emphasised the strategic significance of OSINT in different fields. Additionally, we could count on NATO publications related to OSINT and sources and documents released by the Western defence institutions.
Today, the situation is consistently different. A basic online search for “Open Source Intelligence” on the Internet produces numerous results, including blog posts, reports, training resources and specialised platforms. Yet, this abundance of material also brings confusion and can convince the readers that today’s OSINT is more a matter of technology and computer degrees. By contrast, the fundamental purpose of OSINT, however, remains unchanged: to provide actionable intelligence that supports decision-making and strategic development by using publicly available information (PAI) instead of classified data.
During the last years, the overlap of OSINT and cybersecurity has increased. OSINT analysts now play a critical role in supporting cyber threat intelligence, red team, blue team, SOC activities, and broader information security operations. Therefore, looking at job positions and training curricula, it seems that the modern OSINT analyst is a mix between a computer expert and an AI platform practitioner.
The rise of artificial intelligence (AI), automation, and specialised analytical platforms has transformed methodologies, thereby accelerating data collection and processing. Further, these advancements have also introduced a significant risk: the erosion of critical thinking and analytical discipline.
OSINT, AI and “Old School Teachings”
Initially, when I started working in this field, the absence of AI tools pushed the analysts to find manually the right sources and apply structured analytical techniques (SATs) to transform data and information into solid intelligence.
Language proficiency was indispensable; analysts often studied Arabic, Russian, Farsi, Pashto, Chinese or other languages to interpret primary sources. Reading, contextualising, and synthesising large volumes of information demanded time, patience, and intellectual rigour but created also a solid expertise and analytical judgement. Although data mining and text mining tools were available, government organisations and big businesses mostly used them because the software performing these activities was expensive. At that time, the emphasis rested on methodology, critical thinking, and the human capacity to filter out rumours and noises from useful information.
However, current analysts work in a time with more information than ever before and large access to different technologies and software. Even without subscription-based tools, AI can help refine Boolean search strategies, automate data extraction, and accelerate work activities. Yet, speed does not equate to quality.
The key problem with modern OSINT is not gathering data, but interpreting, analysing, and putting it into context. The intelligence cycle remains the same (direction, collection, processing, analysis, dissemination, and feedback), but the human element has become increasingly overshadowed by technological convenience.
A recurring issue within the modern OSINT community is the overemphasis on tools rather than thinking. Some professionals and companies concentrate on collecting large datasets, often without adequately focusing on the analytical process needed to transform raw data into valuable intelligence.
As The Psychology of Intelligence Analysis reminds us, analysts might possess sufficient information but fail to exploit it effectively. This remains true today. While artificial intelligence can aid in data processing, it does not possess human judgment, intuition, or contextual understanding, all of which are essential in generating credible intelligence assessments.
Reflecting on my early career, I am grateful for the mentors and colleagues who challenged my assumptions and demanded intellectual discipline. Since the beginning, my OSINT activities have expanded from national security to include geopolitical consulting and business intelligence, which requires expertise in regional dynamics and interpreting economic data, especially from the post-Soviet space and the Middle East. This task needed language proficiency and source validation in a setting with limited English coverage. Information scarcity imposed constraints, but it also fostered precision and adaptability.
OSINT and the Challenge for the New Generation of Analysts
Today, while information is abundant, the need for education, training, and critical reasoning has never been greater. Analysts should be able to perform investigations with restricted resources and minimal technological dependence. It is significantly more beneficial to employ a well-organised analytical approach, follow the intelligence cycle, and possess solid analytical skills than to have a large amount of raw data or various tools.
The new generation of analysts faces a unique challenge: many are entering the profession already accustomed to advanced platforms and AI systems. However, it remains essential to assess their performance without these aids, particularly during operations overseas or in adverse conditions characterised by limited technological access.
This is a central lesson I emphasise in SpecialEurasia training courses and my activity as a professor and instructor in OSINT and Web Intelligence (Webint). The capacity to think critically, analyse effectively, and operate independently of technological aids thanks to a solid investigation and analytical framework defines the true competence of an intelligence analyst.
Open Source Intelligence remains essential in the current AI landscape. However, its enduring value does not lie solely in the volume of information collected, but in the analyst’s ability to transform raw data and information into actionable and solid intelligence. Technology can enhance intelligence work, and AI or advanced software should not scare us, but they cannot substitute the human mind, which provides context to data and transforms it into strategic understanding.
