How AI/ML modeling empowers a new generation of threat hunters
Threat hunting is a critical element of any proactive cybersecurity program. Large enterprises often have internal threat hunting teams, while smaller organizations may outsource the function to a third party due to cost. Regardless of whether it’s done in-house or outsourced, threat hunters are still mostly reliant on manual processes and investigations that can span days, weeks or months.
Artificial intelligence (AI) and machine learning (ML) tools are already being used to help security teams expose cyberthreats. But while generic, out-of-the-box detections can assist a security team in getting up and running, the real potential of AI/ML to expedite and improve threat hunting timelines and results lies in using custom-built models with data directly sourced from the organization. AI/ML used on this contextually rich data leads to better decision-making, fewer false positives, and it empowers threat hunters to focus more on higher-priority threat signals.
Download Data-centric Threat Hunting ebook to learn more about:
What it means to take a data-centric approach to improve threat hunting using AI/ML modeling
How AI/ML accelerates and elevates threat hunting efforts
The specific workflow steps for AI/ML modeling
The benefits of engaging with data early in the pipeline
How DataBee can help organizations of any size with modern threat hunting