Generative AI, a technology that is gaining significant attention in the cybersecurity industry, has yet to convince cybersecurity professionals of its implementation potential. While it has been a buzzword among vendors and in the media, many chief information security officers (CISOs) remain skeptical and have no immediate plans to adopt generative AI into their cybersecurity strategies.
The skepticism exhibited by cybersecurity professionals is not unfounded. In the past, the industry has witnessed numerous “silver bullet” solutions that promised to revolutionize cybersecurity practices. These included declarations like “IDS is dead, IPS is the new standard” in the early 2000s, the push for network access control (NAC) in 2006, and the buzz around user and entity behavior analysis (UEBA) in 2015-2016. Each of these solutions failed to deliver on their promises, leading to a general sense of caution among cybersecurity experts.
Additionally, generative AI is still in its infancy, with most products and applications in the beta phase. This further contributes to the hesitation among CISOs, who prefer to take a wait-and-see approach before incorporating any new technology into their cybersecurity frameworks. However, despite this cautious stance, there are CISOs who are actively exploring use cases where generative AI can potentially improve their cybersecurity operations.
One such use case for generative AI lies in the realm of threat intelligence. As organizations increasingly recognize the need for robust threat intelligence programs, they also acknowledge the challenges associated with establishing and managing these programs effectively. Research from ESG indicates that a significant number of enterprise organizations struggle to navigate through the noise of available cyber threat intelligence (CTI) and find relevant information. Additionally, a majority of firms admit to lacking the appropriate staff size or skills required to develop a comprehensive CTI program. These challenges often result in CTI programs being viewed as mere academic exercises that fail to deliver actionable insights for risk mitigation.
Generative AI can help address these challenges by improving the efficacy and efficiency of threat intelligence programs. By leveraging the power of generative AI, organizations can better analyze and interpret vast amounts of CTI data, ultimately enabling them to extract relevant insights from the noise. The technology’s ability to automate and streamline the process of sifting through threat intelligence reports can significantly enhance the value derived from these reports and guide better risk mitigation decisions.
ESG research posed a question to 380 cybersecurity professionals regarding their top challenges in implementing successful threat intelligence programs. Some of the key challenges identified include information overload, resource constraints, and the lack of skilled personnel. Generative AI presents an opportunity to mitigate these challenges by automating the process of analyzing threat intelligence, reducing the burden on human analysts, and enabling organizations to make informed decisions based on actionable insights.
In conclusion, while skepticism persists, there is a growing recognition among cybersecurity professionals that generative AI has the potential to transform the effectiveness of threat intelligence programs. With the ability to alleviate the challenges associated with CTI, generative AI offers organizations a promising solution to improve their cybersecurity posture. As the technology continues to evolve and mature, it is essential for CISOs to remain informed and evaluate its potential impact on their organizations’ cybersecurity strategies.