OSINT 3.0: Embracing the Generative AI Revolution
The era of Generative AI (GAI) is upon us, and for OSINT (Open-Source Intelligence) professionals, adaptation is no longer optional—it’s a matter of survival. I started my career as an intelligence analyst, transitioned into the human intelligence profession and have been learning, teaching and practicing OSINT since the release of Twitter in 2006. The rise of GAI marks the most significant shift in OSINT in almost two decades, Rivaling the transformative impact of the internet, social media, cloud computing, and fiber-optic technology. If social media ushered in OSINT 2.0, Generative AI has launched us into the era of OSINT 3.0. The traditional methods of searching and gathering publicly available information (PAI) is gradually being streamlined by agentic platforms like Deep Research on Gemini 2.0 and Perplexity AI, to name a few. While these AI applications are not perfect yet, their rapid improvement is undeniable. With advancements in AI hardware, such as companies like xAi Colossus supercomputer achieving 100,000 Nvidia Hopper GPUs that leverages the Spectrum-X ethernet platform, with plans to deploy a supercomputer in the near future powered by over one million GPUs, improvement is inevitable. If you’re an OSINT practitioner and haven’t heard of these platforms, it’s time to take notice.
Timing is critical in the world of OSINT. The first wave of information dominates attention, and even when better data emerges later, it struggles to shift the narrative. I’ve experienced this firsthand on the ground in various combat zones and as an OSINT professional working in command centers. To meet this challenge, we must create and distribute accurate, high-quality intelligence at unprecedented speeds. In the age of AI, relying solely on human effort is no longer enough to keep up with the demands of speed and scale.
Generative AI will have significant impact on the OSINT training, technology, and services industry. We must rethink OSINT—and we must do it quickly.
How GAI Will Disrupt OSINT Training
Generative AI is revolutionizing the foundational skills required for OSINT professionals. Traditional training programs focus primarily on manual data collection, analysis techniques, and critical thinking. However, the integration of GAI necessitates a shift toward new competencies that include:
GAI Literacy and Tool Proficiency:
OSINT professionals must become adept at using advanced GAI tools such as GPT-4, Perplexity AI, and Deep Research on Gemini 2.0. This includes understanding how to prompt these models effectively, interpret their outputs, and integrate them into intelligence workflows.
Data Science and Machine Learning Basics:
A foundational understanding of data science principles and machine learning algorithms is becoming essential. This knowledge allows professionals to fine-tune models, customize data processing pipelines, and ensure the accuracy and relevance of AI-generated insights.
Ethical and Responsible GAI Use:
Training must also cover the ethical implications of using AI, including data privacy, bias mitigation, and the responsible dissemination of intelligence. Understanding these aspects ensures that OSINT practitioners use AI tools in a manner that upholds integrity and trust.
Consider this: An OSINT training program that previously emphasized manual search techniques and data analysis now incorporates modules on using GAI tools for automated report generation, training sessions on integrating Retrieval-Augmented Generation (RAG) for enhanced data retrieval, and workshops addressing ethical considerations in deploying AI tools for intelligence operations. This comprehensive approach ensures that OSINT professionals remain proficient in traditional methods while also being equipped to effectively leverage cutting-edge AI technologies.
How GAI Will Disrupt OSINT Technology
The technology landscape within OSINT is shifting rapidly due to the capabilities of Generative AI. Traditional OSINT software, which relied on data scraping, keyword searches, and analytics, are being supplemented or replaced by GAI-powered platforms that offer more efficient and comprehensive data analysis. Here a few things I think are worth considering:
Automated Data Collection and Processing: GAI platforms can autonomously gather vast amounts of data from diverse sources, including social media, dark web forums, and public databases. They can process and categorize this information in real-time, significantly reducing the time and effort required for manual collection, giving the OSINT practitioner more time to analyze the data using their critical thinking skills.
Advanced Pattern Recognition and Predictive Analytics: GAI models excel at identifying complex patterns and trends within large datasets. This capability enables more accurate predictive analytics, helping OSINT professionals anticipate and respond to emerging threats or opportunities more effectively.
Natural Language Understanding and Generation: The ability of GAI to understand and generate human-like text allows for more nuanced analysis of multi-modal data. This includes sentiment analysis, topic modeling, and summarization of large documents, videos and images, enhancing the depth and quality of intelligence reports.
Imagine an OSINT platform powered by GAI that can automatically scan and analyze thousands of social media posts to identify emerging security threats. Instead of manually sifting or searching through the data, the AI can highlight relevant posts, summarize key points, and even predict potential escalation points based on detected patterns with minimal guidance from you. This automation not only accelerates the intelligence cycle but also allows analysts to focus on strategic decision-making rather than data processing.
How GAI Will Disrupt OSINT Services
Service offerings within the OSINT industry must adapt to incorporate GAI capabilities to meet evolving client expectations. Clients now demand faster, more accurate intelligence, which GAI can provide at a lower cost and faster speed than traditional methods. Here a few things to think about:
Enhanced Report Generation: GAI can automate the creation of detailed intelligence reports, incorporating real-time data analysis and comprehensive insights. This automation reduces turnaround times from days to hours, enabling clients to make informed decisions more swiftly.
Personalized Intelligence Solutions: GAI allows for the customization of intelligence products to meet specific client needs. By leveraging AI-driven analytics, service providers can offer tailored insights that address unique client requirements, enhancing the value and relevance of their offerings.
Scalability and Cost Efficiency: GAI-powered services can scale more easily to handle increasing data volumes without a proportional increase in costs. This scalability makes high-quality intelligence accessible to a broader range of clients, including smaller organizations that may have previously been unable to afford comprehensive OSINT services.
I can envision a situation in the very near future where an OSINT service provider uses GAI to offer real-time threat intelligence to cybersecurity teams. By leveraging AI to continuously monitor and analyze data from various sources and while training their special purpose LLM, the provider can deliver up-to-the-minute reports on emerging cyber threats. These reports are not only generated faster but also incorporate deeper analytical insights, allowing clients to proactively defend against potential attacks. Additionally, the service can be customized based on the client’s specific industry or threat landscape, providing highly relevant and actionable intelligence.
Why OSINT 3.0: The Significance of Advanced Computational Power in Large Language Models
The rise of large language models (LLMs) is reshaping the landscape of AI, driven in part by exponential increases in computational power across the industry. Recent advancements in hardware scalability, including the deployment of massive clusters of GPUs like Nvidia’s H100 and H200, have unlocked new possibilities for training and deploying increasingly sophisticated AI models. This surge in computational capacity is not just a technological milestone—it’s a foundational shift that is accelerating the evolution of artificial intelligence in profound ways.
With these advancements, LLMs can now handle vast datasets and execute complex computations at speeds previously unimaginable. Enhanced parallel processing capabilities enable simultaneous execution of multiple tasks, reducing latency and significantly increasing data throughput. This scalability allows for real-time analysis and model training, essential for fields like OSINT, where speed and precision are paramount.
Beyond performance, increased computational power is driving the development of more advanced algorithms. These deep learning models are now more complex, nuanced, and capable of delivering insights with greater accuracy. The ability to iterate and retrain multi-modal models at a rapid pace accelerates the innovation cycle, ensuring that AI applications stay ahead of emerging challenges and opportunities.
Moreover, this expansion in compute power is helping democratize access to high-powered AI. Economies of scale are lowering costs, making it feasible for even smaller organizations to leverage state-of-the-art models. This accessibility ensures that advanced AI capabilities are no longer confined to large corporations or well-funded institutions but are available to a broader range of intelligence teams.
As these large-scale infrastructures evolve, they also bring improvements in reliability and security. Redundancy measures safeguard operations against hardware failures, while advanced security protocols ensure that sensitive data is processed in secure environments. This robust infrastructure enhances trust and confidence in the deployment of AI for critical applications.
In short, the ongoing advancements in computational power are not merely technical achievements—they are catalysts for the continued transformation of AI and its integration into disciplines like OSINT. The opportunities are immense for those ready to embrace this new era of innovation.
Imagine an OSINT team tasked with monitoring global social media for emerging security threats. Traditionally, this would involve manually searching and sifting through vast amounts of data, a time-consuming and labor-intensive process. However, with xAI’s supercomputer clusters of H100 and H200 chips powering their GAI platforms, the team can leverage advanced AI models to automatically scan, analyze, and interpret millions of social media posts in real-time. The AI can identify patterns, detect anomalies, and generate comprehensive reports within minutes, providing the intelligence team with actionable insights far more quickly and accurately than manual methods ever could. This not only enhances the team's efficiency but also significantly improves the timeliness and reliability of the intelligence produced.
Final Thoughts: The Choice is Yours
The rise of GAI is not a challenge to fear—it’s an opportunity to seize. This technology won’t eliminate OSINT jobs; it will elevate them, allowing us to focus on strategy, creativity, and innovation. Back in 2023, I emphasized the importance of taking GAI seriously, and I’m glad I did. GAI has evolved at a neck-breaking speed over the past year—clusters have grown, compute power has increased, and advancements continue unabated. I took the time to learn the history of GAI, how large language models are built, trained, deployed, and fine-tuned. While GAI is still not perfect, new applications and increasing investment in the space mean that application-layer GAI enabled software is making it easier to enhance output quality.
We can now fine-tune portions of data collection quickly and review information faster than ever before. Our relationship with data is undergoing a significant shift. Soon, the days of merely searching for and organizing data will give way to asking questions, following up, and engaging with the data. This means our critical thinking and questioning skills will be more important than ever. Reasoning models like GPT-o1 elevate this relationship to a whole new level by sharing with you exactly how it thinks about your question. Retrieval-Augmented Generation (RAG) will become the intelligence community’s new secret weapon (more on that in a future article).
While some will resist change, clinging to traditional OSINT methods in defense of their legacy, I believe the future lies in doing both: safeguarding our core skills while harnessing the unparalleled capabilities of GAI. This new era of Generative AI won’t eliminate OSINT jobs; instead, it will open the door to new opportunities for those who are willing to embrace change. Tasks that once required the resources of firms like McKinsey & Company or Boston Consulting Group will soon be achievable by OSINT professionals in a fraction of the time and at a fraction of the cost. As a community, we must commit to learning new skills, taking risks, and embracing the uncertainty that comes with innovation. After all, isn’t that mindset what brought us here in the first place? I am determined to adapt, thrive, and help shape the future—are you?