Ai Security
CERT-EU: Defending at Machine Speed
Presentation materials for Defending at Machine Speed, focused on practical use of security context to improve AI-assisted detection and response workflows.
AI attackers on adoption curve with first report of a novel malware strain
Video appearance covering AI-enabled adversary trends and emerging malware behavior.
Defending at Machine Speed: Guiding LLMs with Security Context
Enhance LLM performance for cybersecurity tasks with few-shot learning, RAG, & fine-tuning guide models for accurate PowerShell classification.
Defending at Machine-Speed: Accelerated Threat Hunting with Open Weight LLM Models
Splunker Ryan Fetterman explains how Splunk DSDL 5.2 enhances cybersecurity operations, streamlining PowerShell script classification and reducing analyst workload by 250x.
Model-Assisted Threat Hunting (M-ATH) with the PEAK Framework
Welcome to the third entry in our introduction to the PEAK Threat Hunting Framework! Taking our detective theme to the next level, imagine a tough case where you need to call in a specialized investigator. For these unique cases, we can use algorithmically-driven approaches called Model-Assisted Threat Hunting (M-ATH).
Paws in the Pickle Jar: Risk & Vulnerability in the Model-sharing Ecosystem
As AI / Machine Learning (ML) systems now support millions of daily users, has our understanding of the relevant security risks kept pace with this wild rate of adoption?