This post explores the key obstacles companies face when adopting AI and offers practical, actionable solutions, drawing on insights from the article below.
– ZDNET news

7 out of 10 respondents stated, "The barriers posed by security risks are high, and there is also a lack of proper data infrastructure, capabilities, and talent."

"Small and medium-sized enterprises are often overwhelmed by how to implement AI. Where should they begin?"

Both domestic and international companies have recently been striving to adopt AI to enhance operational efficiency. However, many face significant challenges due to insufficient data infrastructure and employee capabilities to leverage AI fully.

On August 10th, Cloudera, Inc. surveyed 600 IT leaders across the United States, Europe, the Middle East, Africa, and Asia-Pacific region to examine 'The State of Enterprise AI and Modern Data Architecture.' The survey revealed that 8 out of 10 respondents had adopted AI for business processes. Still, many struggled to maintain their systems effectively.

Additionally, 74% of responding companies expressed significant concerns regarding security and compliance risks associated with AI. Challenges such as insufficient training or skilled personnel to manage AI tools (38%) and the high cost of AI tools (26%) also contributed to the difficulties in adopting AI.

A survey representative stated, "AI has been gaining global attention in recent years due to its benefits, such as strengthening business operations, accelerating innovation, and enhancing experiences for both employees and customers." However, they also noted that "not all companies are experiencing these benefits." Additionally, they emphasized that "despite the rapid adoption of AI, many critical elements of a resilient AI strategy are being overlooked or ignored."

Domestic small and medium-sized enterprises (SMEs) face even more significant challenges in adopting AI. According to the 'KOSTAT Statistics Plus' report published by Statistics Korea earlier this year, there is a substantial gap in adopting new technologies, such as AI and blockchain, between large enterprises and SMEs. In 2021, the adoption rate for big data was 12.7% among large enterprises compared to just 3.7% among SMEs. For AI, the adoption rate was 9.2% for large enterprises versus 2.9% for SMEs. Cloud adoption showed similar gaps, with 12.1% of large enterprises using it compared to just 3.7% of SMEs. The adoption rates of the Internet of Things (IoT) were 6.9% for large companies and 3.1% for smaller ones.

Even so, many companies see AI adoption as something they can't afford to ignore. More and more businesses are recognizing that the benefits of AI outweigh the risks, and the number of industries adopting AI is increasing steadily. In Microsoft's 'Work Trend Index 2024,' released this past May, 79% of global business leaders said adopting AI is essential.

With that in mind, this section explores how ThanoSQL addresses one of the biggest challenges in AI adoption: security concerns.

ThanoSQL minimizes the risk of exposing Personally Identifiable Information (PII) or sensitive company data when exchanging information with public LLMs like ChatGPT through processes such as Masking (replacing data with symbols like * or O), Faker (generating fake data), and Re-transforming (restoring the original data).

Moreover, ThanoSQL only transfers the file path and embedding values (mathematical vector representations of the actual object) for unstructured data like images and videos, fundamentally preventing data leakage. ThanoSQL also shares the database schema, ensuring the original database remains internal. Furthermore, the column and table names are replaced with fake entities and re-transformed based on LLM responses, further strengthening data security.

Below are specific use cases that demonstrate how ThanoSQL detects anomalies and manages security risks for businesses:

1) Automatic Risk Analysis in Emails and Chats

• ThanoSQL: Extracts and analyzes email and chat data from the database. It identifies risk factors by filtering messages that contain specific keywords or patterns.
• Natural Language Analysis: Large language models (LLMs) are used to understand the entire context of messages and assess the risk. The risk is then automatically classified by analyzing the tone and seriousness of the content.

2) Key Information Masking

• ThanoSQL: Extracts and masks sensitive information from the database, including data such as email addresses and phone numbers. It ensures that sensitive information remains secure.
• Natural Language Analysis: LLMs are used to understand the context and identify important information, even if it does not match standard patterns. They automatically recognize and mask entities like names and addresses based on the context.

3) Unstructured Data Security: Managing Images and Attachments

• ThanoSQL: Manages unstructured data using metadata. It extracts only the file paths and embedding vectors of attachments, storing and managing them in the database.
• Natural Language Analysis: LLMs are leveraged to analyze the content of images and attachments, extracting key information. Risks are assessed by recognizing and analyzing text and tables within images and documents.

4) Monitoring Anomalies Through Communication Pattern Analysis

• ThanoSQL: Analyzes patterns based on historical data and then detects anomalies. It filters messages that occur repeatedly at specific times or deviate from established patterns, allowing it to identify abnormal signals.
• Natural Language Analysis: LLMs are utilized to analyze message content and recipients to detect anomalies. They automatically identify messages with unusual tone or content and generate alerts.

ThanoSQL by SmartMind is an all-in-one platform that supports companies to store structured and unstructured data in database tables and analyze using the latest AI models through an interactive interface. As we have seen, implementing ThanoSQL can effectively prevent the leakage of sensitive internal data from a security standpoint.

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