Year: 2026
Type: dissertation
Abstract: The growing amount and complexity of organizational data has increased the need of anomaly detection. Traditional methods provide stable and interpretable results but struggle with contextual anomalies in high-cardinality and unstructured data. Large language models provide semantic reasoning but can cause challenges related to non-determinism and reproducibility. This thesis investigates how LLMs can be integrated into an anomaly detection pipeline so that determinism and reproducibility are not jeopardized. As a result, a proof-of-concept system with hybrid architecture combining deterministic methods and constrained LLMs is developed using a design science approach. The results show improved detection on context-dependent anomalies while maintaining reliability, demonstrating that LLMs are most effective as controlled components within deterministic systems.
Source: Theseus (Ammattikorkeakoulujen)
Author: Maaranen, Timo
Institution: LAB University of Applied Sciences
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7160671228