AI Research Center

Overview of ISO/IEC 42001:2023 ISO/IEC 42001:2023 is an international standard defining requirements for organizations to develop, operate, and manage Artificial Intelligence (AI) systems responsibly and safely. While the existing ISO/IEC 27001 provides a management system focused on information security, ISO/IEC 42001 presents an Integrated Management System (AIMS) that reflects unique AI characteristics, including ethics, safety,

1. Should every AI system in the organization be subject to risk management? AI products or services that companies (AI developers and AI users) build or adopt vary widely in type. They also differ from existing IT systems, so organizations often struggle from the current-state survey onward when responding to the Framework Act on Artificial

1. What are the obligations under the AI Framework Act? The ‘Framework Act on the Development of Artificial Intelligence and Establishment of a Foundation for Trust (AI Framework Act),’ enacted with the goals of ‘fostering the AI industry’ and ‘creating an environment for safe use’ and taking effect on January 22, 2026, differs in many

1. What is GPU infrastructure? GPU infrastructure, a container-based distributed computing platform centered on GPUs, is an integrated computing environment composed of GPU resources, compute nodes, networks, storage, orchestration, and a software stack, built to perform large-scale parallel computation (AI training, deep learning, HPC, and so on) that is difficult to process on a CPU-based

Name Full Name Architecture Base Model Developed Training Dataset Lib. & Framework Use Cases HF URL Githhub URL TimeSformer TimeSformer (Time-Space Transformer) Transformer Vision Transformer (ViT) 2021 Evaluated on datasets like Kinetics-400 and Kinetics-600 PyTorch Video classification and action recognition tasks https://github.com/facebookresearch/TimeSformer VideoMAE Video Masked Autoencoders Masked autoencoder Vision Transformer (ViT) 2022 Pre-trained on large-scale

Name Full Name Architecture Base Model Developed Training Dataset Lib. & Framework Use Cases HF URL Githhub URL Audio Spectrogram Transformer Audio Spectrogram Transformer Transformer ViT 2021 AudioSet PyTorch, Hugging Face Transformers Audio classification, sound event detection https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer https://github.com/YuanGongND/ast Bark Bark GPT-like, Transformer GPT-2 2023 Proprietary dataset PyTorch, Hugging Face Transformers Text-to-speech, voice synthesis https://huggingface.co/docs/transformers/model_doc/bark

Name Full Name Architecture Base Model Developed Training Dataset Lib. & Framework Use Cases HF URL Githhub URL BEiT Bidirectional Encoder representation from Image Transformers Vision Transformer ViT 2021 ImageNet-21k, ImageNet-1k PyTorch, Hugging Face Transformers Image classification, semantic segmentation https://huggingface.co/microsoft/beit-base-patch16-224 https://github.com/microsoft/unilm/tree/master/beit BiT Big Transfer ResNet ResNet 2019 JFT-300M, ImageNet-21k TensorFlow, Hugging Face Transformers Image classification, transfer learning

Name Full Name Architecture Base Model Developed Training Dataset Lib. & Framework Use Cases HF URL Githhub URL ALBERT A Lite BERT Transformer-based sequence-to-sequence BERT 2019 BookCorpus, English Wikipedia TensorFlow, PyTorch Natural Language Understanding https://huggingface.co/albert-base-v2 https://github.com/google-research/albert Bamba Bamba Transformer GPT-2 2023 Bambara language corpus PyTorch Bambara language generation https://huggingface.co/masakhane/bamba https://github.com/masakhane-io/bamba BART Bidirectional and Auto-Regressive Transformers

In preparation for our GenAI (LLM) Red Team service, we organized a comprehensive set of detailed evaluation test cases aligned with the OWASP Top 10 for LLMs.

Prompt Injection, one of the key vulnerabilities in GenAI (LLM) systems, appears as the first item in the OWASP Top 10 for LLM Applications due to the wide range of existing attack techniques. At the same time, modern LLM-based services inevitably rely on multiple types of prompts to deliver accurate instructions and consistent system behavior.