In the realm of cyberspace, both state and non-state actors have capitalized on its extensive interconnectedness to engage in a wide range of activities, some of which may involve actions of malicious nature. Cybercriminals are now adopting the tactics, techniques, and procedures (TTPs) once used only by state actors. To counter these threats, organizations must equip themselves with the tools that best meet their specific needs and requirements.
Cyberattacks have increased over the past few years. In light of this, some vendors have realized that traditional cybersecurity approaches and tools have proven inadequate in keeping up with the rapid changes in the threat landscape. As a result, to remain secure and compliant, organizations must actively seek new ways to assess and respond to cyber threats, while empowering security operations center (SOC) analysts with the right tools.
Over the last few years, generative AI has emerged as a powerful and useful tool. It has presented significant advancements in industries such as art, finance, education, healthcare, government, marketing, software development, and more. Generative AI focuses on the creation of content, be it text, images, videos, or other forms, using advanced algorithms and models. It creates original content or outputs similar to those created by humans.
More recently, the use of Large Language Models (LLM) has been the focus of public interest. Trained on enormous datasets from various sources, LLMs can generate new content and texts in multiple languages. They enable the creation of chatbots that are potentially indistinguishable from humans when combined with natural language processing (NLP) technology.
For security analysts, generative AI offers a remarkable leap forward in the efficiency and effectiveness of their work. It means being able to automate the most repetitive parts of their job, focusing on the more creative and strategic dimensions of their role, such as planning new defense strategies, identifying emerging threats, and formulating proactive mitigation plans.
The potential use of intelligent chatbots in cybersecurity extends beyond mere task automation. Security analysts can use chatbots to generate alerts and perform tasks like threat modeling, incident analysis, and even preventing future attacks just by interacting with an AI assistant. While general-purpose LLMs possess an exceptional capacity to understand and generate human-like text, they lack the specialized training necessary for mission-critical roles in cybersecurity.
By harnessing the potential of generative AI, however, human analysts can broaden their scope within cybersecurity practices, cultivating new knowledge and developing new skills, such as the art of prompt engineering. Prompt engineering is a relatively new and useful discipline. It allows you to develop and optimize prompts to use LLMs efficiently. By developing the skills needed to engineer AI prompts, analysts can leverage AI tools to efficiently navigate the complexities of modern cybersecurity while staying ahead of evolving threats.
Implementing automated solutions and cultivating a cybersecurity culture simultaneously can help your organization stay safe from cyberattacks. The human factor continues to be an important element CISOs need to consider in order to protect their organizations from cyber-attacks. We often hear this: “Humans are the weakest link in cybersecurity.” This characterization of human nature is deeply ingrained in the industry. As a result, it prevents us from talking about how to better involve people in cybersecurity processes.
Although the human factor continues to be a major problem in cybersecurity, it is essential to develop new skills and implement the right tools. Instead of viewing generative AI as a potential replacement, we could see it as a force multiplier. This approach echoes a broader trend in AI development, where the objective is not to supplant human endeavors but to amplify them.
Nevertheless, with great progress comes great responsibility. While generative AI offers new possibilities, it also raises new questions. Generative AI presents significant social, legal, and economic challenges. It is therefore essential to balance the development of generative AI with proper research, responsible deployment, and ethical considerations to ensure that its advantages can be harnessed while minimizing its potential disadvantages.