Responsible Data Security Techniques for Using Artificial Intelligence Both data lakes and data warehouses may be exploited by AI models, but businesses need ways to grow and train their own AI initiatives.
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In the age of generative Artificial Intelligence (AI), data security is an essential resource for creating responsible models while maintaining ease of data access. Many large corporations can make use of the vast amounts of data available to them, but cannot risk compromising sensitive information.
However, corporate data assets can be secured in the form of unstructured, undefined data lakes and structured, well-defined data warehouses. When these security resources are established, a company can more easily implement fine-grained access to AI models for both unstructured and structured data. Otherwise, when such models are exposed to datasets containing sensitive information, that data is at risk.
Organizing Large Data Sets
Even many large corporations have poorly organized data sets, stored in so-called "data silos," in which a repository of data is controlled by one department and isolated from the rest of the organization. When data silos are formed, the valuable information within is inaccessible to the rest of the company and at greater risk of being exposed. Instead, data should be organized into cohesive on-premises or cloud systems where all data can be tracked and accessed by the company. This takes the form of data lakes and data warehouses.
A data lake is an organization's store of all structured and unstructured data, built with the architecture to handle such large volumes of information. Having data accessible in this way makes it more useful for data analytics and gathering insight into your business. From this point, ELT Extract, Load, Transform (ELT) pipelines can deliver new data to the lake to be structured and transformed when needed.
Data warehouses also gather large stores of data but in a structured and unified fashion. This form of centralized, organized data storage is especially useful for AI models to inform decisions. Data warehouses provide a complete picture of the business with only quality data, but have a higher cost than data lakes and require more effort to maintain.
Maintaining Data Security with AI
Both data lakes and data warehouses may be exploited by AI models, but businesses need ways to grow and train their own AI initiatives. This is where fine-grained access comes in. The method entails fitting access controls that meet each dataset's particular requirements so that only authorized users have access to delicate information. In this way, the data is still useful but remains safe and secure.
"Generative AI applications can only be as safe as the underlying datasets," says Nishchai Jayanna Manjula, a seasoned veteran of big data Information Technologies (IT). "Without coherent methods of handling data, businesses may endanger confidential information leading to serious legal and financial ramifications."
Securing AI with Advanced Data Management
In the changing world of generative AI, maintaining robust data security is critical to maintaining responsible model development and protecting sensitive information. Businesses should prioritize organizing and securing their data assets, whether stored in data lakes or data warehouses.
Effective data management is key to enabling fine-grain access controls, which only allow authorized users to interact with sensitive data. As Manjula states, "Data is the fuel that runs artificial intelligence, and without robust security, the whole system is exposed. My goal is to ensure that while AI keeps getting more and more advanced, our approach toward the security of data evolves at par, protecting the businesses and their customers against the threats of tomorrow."
The ongoing development of data security strategies, particularly with AI, remains a critical concern for businesses. Creating robust data management practices that evolve alongside AI may be essential to protect businesses and their customers from emerging digital threats.
