The increasing adoption of large language models in the fi-nancial sector introduces significant challenges related to the handling of sensitive financial information (SFI). Existing general-purpose content safety solutions, or guardrails, often fall short in detecting domain-specific risks inherent in finan-cial data processing. This study addresses these gaps by de-veloping a comprehensive taxonomy of SFI, grounded in globally recognized financial, information security, and AI governance standards. Leveraging this taxonomy, we synthe-sized an extensive dataset encompassing diverse categories of SFI and trained GARD (Generative Adversarial network Risk Detection) model to detect sensitive content in both in-puts and outputs of GenAI systems within the financial do-main. Our evaluation compared GARD against commercial guardrail solutions, including the OpenAI Moderation API and Microsoft Azure Content Safety (ACS). The results demonstrated that while commercial solutions maintained high precision, their recall was substantially lower, indicating many risky instances went undetected. In contrast, our model achieved a recall score of 0.98, significantly outperforming the benchmarks and enhancing SFI detection. These findings underscore the necessity of domain-specific guardrails tai-lored to the financial sector to ensure robust AI safety and compliance. In conclusion, this work contributes (1) A de-tailed taxonomy of SFI tailored for GenAI applications, (2) A comprehensive synthetic dataset that encompasses a wide range of sensitive topics relevant to the domain and (3) A high-performance risk detection model that can be deployed independently or alongside existing solutions to improve con-tent safety in financial services. This approach promotes trust, mitigates financial, legal, and reputational risks, and supports the responsible adoption of GenAI technologies in sensitive domains.

Query spotting in document images is a subclass of Content-Based Image Retrieval (CBIR) algorithms concerned with detecting occurrences of a query in a document image. Due to noise and complexity of document images, spotting can be a challenging task and easily prone to false positives and partially incorrect matches, thereby reducing the overall precision of the algorithm. A robust and accurate spotting algorithm is essential to our current research on sketch-based retrieval of digitized lecture materials. We have recently proposed a modular spotting algorithm in [1]. Compared to existing methods, our algorithm is both application-independent and segmentation-free. However, it faces the same challenges of noise and complexity of images. In this paper, inspired by our earlier research on optimizing parameter settings for CBIR using an evolutionary algorithm [2][3], we introduce a Genetic Algorithm-based optimization step in our spotting algorithm to improve each spotting result. Experiments using an image dataset of journal pages reveal promising performance, in that the precision is significantly improved but without compromising the recall of the overall spotting result.

We report our ongoing research on an application-independent and segmentation-free approach for spotting queries in document images. Built on our earlier work reported in [1][2], this paper introduces an image processing approach that finds occurrences of a query, which is a multi-part object, in a document image, through 5 steps: (1) Preprocessing for image normalization and connected components extraction. (2) Feature Extraction from connected components. (3) Matching of the query and document image connected components' feature vectors. (4) Voting for determining candidate occurrences in the document image that are similar to the query. (5) Candidate Filtering for detecting relevant occurrences and filtering out irrelevant patterns. Compared to existing methods, our contributions are twofold: Our approach is designed to deal with any type of queries, without restriction to a particular class such as words or mathematical expressions. Second, it does not apply a domain-specific segmentation to extract regions of interest from the document image, such as text paragraphs or mathematical calculations. Instead, it considers all the image information. Experimental evaluation using scanned journal images show promising performances and possibility of further improvement.