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Gareth Owenson, CTO, Searchlight Cyber
July 11, 2024
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HOW AI HELPS DECODE CYBERCRIMINAL STRATEGIES



With terms like “AI washing” making their way into mainstream business
consciousness, the hype surrounding AI is making it harder to differentiate
between the true applications and empty promises of the technology.



The quest for tangible business benefits is in full swing, and in cybersecurity
as much as any industry, it’s important to distinguish the deployment of AI
technologies simply for the sake of it, as opposed to identifying the use cases
where AI will make a real difference. Lauding AI as the solution to every
problem muddies the waters and could lead to missed opportunities.

In the field of threat intelligence, however, there are specific ways in which
AI tools are showing huge promise for cybersecurity teams, including in lifting
the lid on dark web threats. The dark web is a hugely complex landscape,
well-known for the promise of anonymity, and a domain where cybercriminals
organize and plan their attacks against organizations. There is a role for AI in
gathering data from the dark web, applying structure to it, and ultimately
turning it into intelligence that organizations can use to inform their security
strategy.


THE DARK WEB IS A PERFECT USE CASE FOR AI

The dark web represents a classic case of unstructured, disparate, and
difficult-to-analyze data. From forum discussions, marketplace listings, and
ransomware group communications, often scattered across various platforms and
languages – making sense of the dark web and navigating this vast, evolving
terrain can be daunting, even for experienced cyber analysts.

The biggest use case for AI is its ability to process, analyze, and interpret
natural language communication efficiently. AI algorithms can quickly identify
patterns, correlations, and anomalies within massive datasets, providing
cybersecurity professionals with actionable insights. This capability not only
enhances the speed and accuracy of threat detection but also enables a more
proactive and comprehensive approach to securing organizations against dark
web-originated threats. This is vital in an environment where the difference
between detecting a threat early in the cyber kill chain vs once the attacker
has achieved their objective can be hundreds of thousands of dollars.


THE ROLE OF AI IN OVERCOMING LANGUAGE BARRIERS

A great way to illustrate this use case is through language translation. The
dark web is a global space with cybercriminals operating in various languages
and using complex and dark-web-specific slang. Our data shows us that the top 10
languages used on the dark web are English, Russian, German, French, Spanish,
Bulgarian, Indonesian, Turkish, Italian, Dutch, and standard Chinese. After
English, Russian is the most used language on the dark web, accounting for 66
percent of non-English language content. 

But it’s often not textbook Russian. Just as English-speaking hackers have their
own slang terms, acronyms, and code words, so do their Russian counterparts.
Historically, this has created a challenge for gathering intelligence from the
dark web, because once security professionals capture a conversation between
potential adversaries, they must “decode” it.

Traditional translation tools, naturally, are not equipped to accurately
translate the slang used by Russian hackers. But, by training a model on the
slang terms used on the dark web, custom-built AI-powered translation tools can
help to break down this multilingual complexity and identify hidden threats. 

This AI-based approach also has the potential to improve efficiency of security
teams and the accuracy of intelligence by removing the manual and error-prone
process involved copying and pasting large quantities of content or searching
through dark web data with poorly translated terms. Advanced AI models, such as
transformers, can also produce a better understanding of the semantic meaning of
translations rather than merely translating word-for-word. By using context to
derive meaning, AI improves translation accuracy, allowing analysts to interpret
threats that might otherwise remain hidden.


UNDERSTANDING THE NATURE OF THE THREAT

Another potential use case of AI is in quickly identifying and alerting specific
threats relating to an organization, helping with the prioritization of
intelligence. One thing an AI could look for in data is intention – to assess
whether an actor is planning an attack, is asking for advice, is looking to buy
or to sell access or tooling. Each of these indicates a different level of risk
for the organization, which can inform security operations.

Take, for example, posts by initial access brokers, i.e., advertisements
cybercriminals post on the dark web to sell access to an organization’s network.
Monitoring for such posts is a time-consuming and manual task for a human
analyst, as it requires them to read through dark web forums day-in, day-out and
spot relevant posts through a lot of noise. But an AI model can be trained to
identify and extract key components of an initial access broker post as well as
identify the possible target, providing that company advanced warning and
allowing them to review their security protocols, heighten their alert status
and begin proactively hunting for signs of access.


ENHANCING THREAT INTELLIGENCE THROUGH AI

AI is not going to be a cure-all in cybersecurity, but there’s a role it can
play in areas where inefficiencies are created by vast amounts of unstructured
data. There is an ever growing source of threat feeds and data sources for
security teams to monitor, making extraction of relevant intelligence
increasingly difficult. AI can support security analysts by quickly and
efficiently finding the most serious threats. Time is critical in security and
there is a real power in making threat intelligence faster, more accurate, and
therefore more actionable. 

Moreover, as AI innovations make gathering intelligence easier and less
resource-intensive, there is a high likelihood that it will enable smaller
cybersecurity teams to undertake more sophisticated threat intelligence
activities such as actively monitoring the dark web for potential threats
against their organization. It could allow more companies to adopt a proactive
cybersecurity stance. As technological advancements continue, the integration of
AI in threat intelligence will become standard. Looking beyond the hype, AI
could prove to be a major catalyst in bringing proactive dark web monitoring
into the mainstream.





More about
 * artificial intelligence
 * cybercriminals
 * cybersecurity
 * dark web
 * opinion
 * Searchlight Cyber
 * threat
 * threat intelligence

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