April 22, 2025

The modern intelligence analyst simply cannot cope with the wealth of data at their disposal.

The sheer volume of available intelligence is overwhelming. Nowhere is this need clearer than in open-source intelligence (OSINT), where the darknet plays a critical role.

As Randall Nixon, Director of the Open-Source Enterprise at the CIA, warned: “It’s amazing what’s there…the next intelligence failure could easily be an OSINT failure, because there’s so much out there.”

The U.S. Office of the Director of National Intelligence (ODNI) has designated OSINT the “INT of first resort.” Recent global conflicts, including those in Ukraine and Gaza, have underscored OSINT’s critical role in modern intelligence.

Darknet Data is Critical OSINT

Cybercriminal marketplaces, encrypted messengers, forums and hacker sites serve as hubs for illicit transactions, where drugs, weapons, extreme politics, stolen credentials, malware, and hacking services are openly traded. These platforms operate much like traditional e-commerce sites, complete with vendor ratings, escrow services, and customer reviews. As a non-exclusionary ecosystem, its potential is infinite.

Darknet Data Challenges

Darknet data is a goldmine of intelligence. Unlike structured enterprise datasets, darknet data is chaotic, multilingual, and riddled with deception, requiring robust machine learning techniques to extract meaningful insights.

Darknet data is inherently messy, containing slang, obfuscation techniques, and multilingual text. Let alone short-lived and transient sites and pages. Additionally, much of the data is stored in an unstructured format, making it difficult to apply Natural Language Processing (NLP) and Large Language Models (LLMs) effectively. Many darknet sites also introduce deliberate noise—web pages filled with random or misleading content—to further obscure information.

Legal and Ethical Risks

Since the darknet is designed for anonymity, traditional privacy regulations don’t always apply in the same way they do for regulated social media. However, the ethical implications of darknet surveillance must still be considered, especially when handling sensitive intelligence and personally identifiable information (PII).

Illegal Content

Darknet data often includes information related to illegal activities, which can pose significant challenges for generative AI and Large Language Models (LLMs). Many models have built-in safeguards that restrict processing such content, making off-the-shelf AI solutions less viable for darknet analysis. Additionally, the more specific the input data, the harder it is to bypass these restrictions. For example, extracting insights from a full dataset structure is generally easier than pulling highly specific details, such as product names, which may trigger model safety mechanisms.

The Possibilities of AI

The goal of intelligent systems should be to enhance human capabilities, enabling people to focus on higher-value, strategic decision-making, and creative tasks rather than routine processing.

As darknet activity continues to expand, advanced big data analytics and AI-driven methods will be essential to making sense of this vast, high-risk ecosystem.

Quantum Computing increases computational power so that week-long analysis will take minutes, with unprecedented levels of accuracy. Recent leaps in quantum computing will ensure the processing of Darknet data is considerably easier.  

Human Behaviour Analysis in Anonymized Spaces

When no one is looking, how do people behave? The darknet provides a unique perspective on human behavior—a reflection of how individuals and groups act when they believe they are untraceable. Under the veil of assumed anonymity, forums and marketplaces reveal unfiltered reactions to the outside world. This creates an opportunity for social scientists, intelligence analysts, and behavioral researchers to study criminal psychology and radicalization patterns.

Graph Neural Networks (GNNs) are particularly effective for link prediction and clustering, helping identify connections that may not be obvious through traditional analysis for entity resolution.

Anomaly Detection and Trend Monitoring

Detecting anomalies in darknet activity is essential for identifying emerging threats. Analysts tracking illicit trades look for anomalous patterns in trade volume, pricing, and vendor behavior—indicators that may signal disruptions, law enforcement interventions, or the emergence of new criminal enterprises.

Predictive Analysis and Threat Forecasting

By analyzing historical data, organizations can predict the likelihood of future cyber threats, misinformation campaigns, and illicit trade patterns.

As Greg Ryckman, Deputy Director for Global Integration at the Defense Intelligence Agency (DIA), stated: “We need a professional cadre that does open-source collection for a living, not amateur.”

With the integration of AI-powered predictive models, darknet data can be used to simulate complex scenarios, sanitise PII and help organizations prepare for emerging risks—whether that be the spread of disinformation, shifts in ransomware tactics, or geopolitical cyber threats.

DarkOwl’s Contribution

DarkOwl is exploring the use of LLMs to identify additional personally identifiable information (PII) entities. By refining these models to detect structured elements within highly unstructured text, we are developing tools that can track cybercriminal activity and detect fraud at scale.

Beyond entity extraction, we are also applying topic modeling techniques to classify and label darknet content. By using Latent Dirichlet Allocation (LDA) and transformer-based models like BERT, we have successfully categorized subsets of forums, marketplaces, and chat data.  We plan to expand on this work to create unique digital fingerprints of these spaces. This will allow us to track shifting trends, identify when threat actors migrate from one marketplace to another, and detect the resurgence of illicit communities following law enforcement takedowns.

We have successfully applied Generative AI models to pull structured product details from specific darknet marketplaces. We plan to expand this work to allow us to monitor illicit trade trends, track specific vendors, and assess market shifts over time. As our AI models continue to structure and analyze darknet data, we gain deeper visibility into longitudinal trends.

We are exploring AI-driven summarization, NER, clustering, and topic modeling to filter out irrelevant noise and surface high-priority leaks. By applying AI-powered triage mechanisms, we can determine which breaches pose the greatest risk to organizations.

Curious to learn more? Contact us.