Cybersecurity analysis leading to deterrence of cybercrime requires processing thousands to billions of digital signals per second. Those signals must be accurately comprehended, forensically preserved then used to detect and investigate potential cybercrime. The work products must not only assist the investigators but must be translated into language that non-technical lay audiences including judges, lawyers and jurors can understand.
This presentation explores how generative artificial intelligence (GenAI), natural language processing (NLP), graph-theory and artificial narrow intelligence (ANI) can play a role in delivering these outcomes.
The session includes demonstrations of opensource toolkits, datasets and models designed to assist in this work.
While believability of an AI (Turing test) is important in many applications, the need for forensic truth is paramount in cybersecurity application. In this session, we will evaluate methods for training and tuning models that meet requirements of evidence handling, business analysis and legal and martial response.
Detecting, catching and successfully prosecuting cybercrime requires collaboration across private sector, law enforcement, insurance companies and national security agencies. In this session, approaches to collect, analyze, store and share digital evidence will be examined. Methods of safely transmitting data between private sector and law enforcement will be discussed. Demonstration of workflows between investigators, law enforcement, prosecutors and insurance adjusters will be covered.
I also reference this 2021 DarkReading Article: Handcuffs over AI.
An updated deck for my talk on Big Data in Cybersecurity can be downloaded here.
In a 2021 DarkReading article titled Handcuffs over AI, I describe the importance focusing on the outcome of increasing deterence in cybercrime. The presentation can be downloaded here.
