The reliability of productive Artificial Intelligence depends on the security of Databases.
Data security and privacy are a priority in the development of productive AI. Organizations often wonder how to use productive AI while complying with all relevant regulations. To help them, the data and analytics specialist has developed seven recommendations for protecting databases.
Choosing the right strategy for generative AI takes work. One way to ensure security is to train large linguistic models only on the organization’s data. We have made seven recommendations to help organizations build the infrastructure and invest in this strategy. These recommendations will help create a transparent and secure database for AI applications used in business.
Intelligent Integration
Does your organization have many data from different sources, often in other formats? Big data sets make LLM learning easier and are usually good news for creative AI systems. However, organizations need to discover, collect, and make available the correct data at the right time in a centralized data warehouse or high-performance data set to ensure a smooth and efficient flow of information for generative AI models. Low latency and maximum data availability are ensured by a data replication platform that distributes, synchronizes, replicates, and consolidates data.
Regular Updates
Up-to-date data allows the LLM system to adapt, improve, and produce consistent, relevant, and contextualized results for different language activities and applications. This requires a data management strategy that continuously collects and reproduces data at the right time and place and collects real-time data on changes. The real-time data flow increases the accuracy and usability of the language model results.
Data Conversion
Data should be converted from raw state to a format that can be used in IMM, preferably in the most efficient way for the target system. For example, Spark SQL and Spark Cluster are best suited for a data warehouse, and Push Down SQL is best suited for a cloud-based data warehouse.
Automated Data Cleansing
Data quality must be considered for creative AI, as it directly affects the consistency, correctness, and reliability of modeling results. When high-quality data is used in training, the model can identify key patterns and relationships, providing valuable, context-aware information. With the proper techniques, data can be cleaned and profiled automatically and in real-time, ensuring that the model is trained on only high-quality data from the outset.
Data Management
In addition, data management is critical to creative AI, as it ensures that the language model uses data ethically and efficiently. This can be achieved through technologies that automate data processes, best practices, and principles for data collection, storage, and management. Data cataloging and genealogy tracking solutions make data transparent in the analytics pipeline from source to application. They provide direct insight into the origin and history of data.
Ensure Privacy
All organizations, especially those in the healthcare and financial sectors, should prioritize data protection and privacy. By creating synthetic data that preserves the statistical properties of the original dataset but remains confidential, Manufacturing AI offers a way to protect privacy. This strategy protects sensitive data while facilitating sharing and collaboration.
Fraud Detection
Artificial intelligence can help detect and prevent fraud by analyzing data patterns, anomalies, and potential threats. It can improve security systems, detect vulnerabilities, and mitigate threats. Software such as Dynatrace, CrowdStrike, and many others, albeit in different ways, use AI to detect fraud and ensure cloud security.
The use of generative AI in enterprise applications is revolutionary. With generative AI, businesses can unleash new levels of creativity, deliver personalized experiences, simplify and secure operations, improve decision-making, and drive innovation.
