LLMs Raise Efficiency, Productivity of Cybersecurity Teams

Security professionals say adding large language model (LLM) and generative artificial intelligence (GenAI) capabilities to security programs improves efficiency in threat detection and increases productivity of analysts, according to Dark Reading's latest research on enterprise security. Efficiency and effectiveness were recurring themes.

In Dark Reading's Artificial Intelligence and Machine Learning in Cybersecurity Survey, the top three benefits of using GenAI and LLMs in a cybersecurity program were more efficient threat detection (28%), improved analyst productivity and efficiency (27%), and better threat intelligence analysis (23%).

The promise of AI tools is enhancing the analysts' performance. Almost one-fifth (19%) of respondents said they appreciated how AI helped generate reports faster, while the same number (19%) liked how AI-infused technologies learn and adapt from new information. Fifteen percent felt the pressure lift from AI's ability to analyze security alerts to reduce false positives, while another 15% appreciated how the technology helped them discover and reduce misconfigurations.

Cybersecurity practitioners also saw value in active uses for AI, such as proactive threat hunting (16%), greater user behavior analysis (15%), improved incident response (15%), and a better security posture (11%).

There are also benefits to the business side. LLM tools can optimize resources (13%) to help make an organization's network more efficient and reduce costs (9%). Respondents also see ways GenAI can scale up their operations (12%), as well as improve cybersecurity response by supplementing the skills of the current team (12%). On the budgeting side, LLM use can reduce both the need for additional headcount (11%) and costs of operation (9%).

For more on the impact of AI and ML on cybersecurity, download the Dark Reading report "The State of Artificial Intelligence and Machine Learning in Cybersecurity."