Charles Herring
1 February 2024
Abstract
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. Data verification, sanitization, and vectorization will be reviewed in this session. Research for preventing AI "hallucinations" and treating data as evidence in inferences will also be covered.
Objectives
- Training a Model for Truth over Believability
- Use of Referenceable Data Vectors in Inferences
- Forensic Considerations of AI in Cybersecurity
