In the realm of cybersecurity, the combination of cyber threat intelligence (CTI) and artificial intelligence (AI) has become a potent weapon to protect digital assets and infrastructure. However, the effectiveness of AI is heavily reliant on the quality and breadth of the data it receives. Access to unique and underground sources is crucial in this regard.While CTI offers significant value to individuals and organizations, it also presents challenges, particularly in terms of the overwhelming amount of data it generates. This can lead to confusion and inefficiencies in threat exposure management efforts for security teams. Additionally, organizations vary in their security maturity levels, making it difficult to access and comprehend CTI data.To address these challenges, many cybersecurity companies, including threat intelligence providers, are introducing generative AI solutions to streamline and expedite the analysis of CTI data. However, a fundamental issue arises with some AI offerings—they often rely on limited, outdated, or inaccurate data, leading to unreliable outputs.In essence, the effectiveness of AI is only as good as the data it is fed. To derive meaningful business value from generative AI, the underlying data must be credible, current, and relevant. Moreover, the intelligence feeding the AI models must encompass a wide range of unique sources to ensure accuracy.This article serves as a guide to help organizations find the right combination of CTI and generative AI, ensuring that the threat intelligence received is accessible, understandable, and actionable regardless of their security maturity level. The forthcoming eBook, “From AI to IQ: Transforming Cyber Defense with Generative AI,” provides further insights into the benefits of AI and CTI.The significance of deep and dark web sources and the context of attack surfaces cannot be understated. Commercial and open-source AI solutions often lack access to these sources, resulting in inaccurate or incomplete responses when querying such intelligence. This limitation prevents organizations from obtaining accurate answers to critical questions related to ransomware susceptibility, resilience against phishing attacks, intellectual property protection, and identifying the most threatening threat actor groups.Cybersixgill addresses this limitation by providing access to deep and dark web sources, enabling generative AI capabilities to deliver instantaneous and accurate answers. By leveraging vast and unique repositories of CTI threat context and correlating it with organization-specific attack surface context, Cybersixgill IQ empowers organizations to manage their threat exposure effectively. Inquiries about the impact of specific vulnerabilities or the identification of vulnerable areas yield immediate, accurate, and easily understandable responses.Cybersixgill IQ goes beyond mere chat features offered by other generative AI solutions. It encompasses AI-driven automated analysis, on-the-fly generation of high-quality intelligence reports, and an AI analyst assistant that provides valuable insights in various contexts or activities. The solution intelligently interprets customer inquiries and delivers data and insights tailored to specific use cases, be it a concise threat overview for senior leadership or comprehensive forensic incident reports for detection and response teams.To address concerns about data privacy, Cybersixgill upholds strict measures to ensure customer data security and privacy. Data protection measures, including minimizing data transfer, sensitive data masking, and local processing, are implemented to safeguard user information. Privacy and security remain paramount in Cybersixgill’s implementation of generative AI solutions.Unlike other CTI vendors, Cybersixgill has a long-standing history of investing in AI and leveraging it for automated processing and enrichment of data. Machine learning and deep learning have been integral to their products, such as DVE Intelligence, which utilizes real-time natural language processing (NLP) to analyze underground chatter and predict the likelihood of exploiting specific CVEs.
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October 20, 2023
October 20, 2023