Can In-database Machine Learning Help Eliminate Breach Risk?

Learn about the risk and reward equation associated with mining the data gold within your enterprise. Jorge Torres, CEO and co-founder of MindsDB, advises on how your company can apply AI to sensitive data safely and easily through advances in machine learning that enable improved handling, processing, control, analysis, and threat detection to realize the full value of data insights. 

Enterprises have long recognized the value of data as a valuable asset. Data is a veritable gold mine of actionable intelligence, providing the right team of data scientists and data science tools are on hand to uncover it. When excavated correctly, data can reveal pain points, areas for improvement, and opportunities to create new, innovative solutions.   

However, mining this data for business intelligence is not without its challenges. Companies must resource the necessary talent to uncover the data, ensure they have the tools in place to understand that data effectively, and also make sure they’re able to do it fast enough to maintain a competitive advantage. There is one other challenge that is often overlooked – keeping the treasure trove of data safe from threat actors that want to steal it and hold it for ransom. 

Data is most safe when it’s stored in a well-configured enterprise-grade data platform that is regularly updated and maintained by a professional team. These databases tend to be locked tight, but even then, there are human factor risks that occur outside of the database that can lead to stolen credentials, the introduction of malware, and invite basic user error. 

The Inherent Risk of Data Manipulation

Indeed, the very processes around manipulating and analyzing data can expose it to risk, particularly when that data is manipulated by users and programs outside of the company’s database, as tends to be the case. For instance, when data scientists are preparing datasets for training machine learning (ML) models, the standard process is to extract, transform, and load (ETL) the data into a comma-separated values (CSV) file, massage that data using a data analysis tool like Pandas, and then load it up into an ML tool in order to train a model for it. This gives the data a great deal of exposure, which obviously creates a security risk. It also leaves it vulnerable to human error. Manual data entry has an error rate as high as 4%Opens a new window , so even if businesses manage to avoid a breach, they’re still creating problems for themselves by manually importing and exporting data. 

Developers are another cohort that tends to expose data when building solutions inadvertently. They use API keys to make API calls to access data for inferencing machine learning models.  These keys are usually built into the app code, and many developers – even junior developers – tend to have access to the code. More than 40% of businessesOpens a new window had an API-based security incident in 2022. In one surveyOpens a new window , more than 90% of respondents noted that their organizations have API authentication policies in place, but nearly a third (31%) said they weren’t confident that those policies ensured adequate levels of authentication. 

These two scenarios, involving data scientists and developers, both represent human-factor data breach risks that occur outside of the database, usually while trying to develop AI-driven solutions. 

This is why businesses need to start focusing their efforts not just on threat detection but on architectural risk analysis. In other words, how they manipulate and transport data matters more than ever before. According to one sourceOpens a new window , data sets in which a machine learning system is trained account for 60% of the risk, while learning algorithms and source codes account for 40% of the risk. But what if these kinds of solutions could be architected within the database instead of pulling data out to develop them? 

See More: Five Cybersecurity Simulations to Reduce the Risk of a Painful Data Breach

Benefits of In-database Machine Learning

There are now AI-powered tools that can completely avoid the need for cumbersome manual processes involving ETL and CSVs, and instead train ML models from within the protected walls of the database itself. Bringing AI into the data storage platform, instead of passing data out to AI tools, is a bulletproof way to avoid the challenges described above. Here are some of the ways in-database machine learning can improve data security:

1. Better data handling: In-database machine learning enables organizations to apply AI to sensitive data without having to transfer it outside of the database, reducing the risk of data exposure. This helps ensure that sensitive data stays within a secure environment, reducing the risk of data breaches and unauthorized access.
2. Streamlined data processing: Organizations can process data quickly and efficiently within the database, reducing the risk of data errors that can occur during data transfers. This helps to ensure that data is processed accurately and securely, also reducing the risk of data breaches and other security threats.
3. Granular access control: Databases offer granular access control to data stored at different levels. This enables organizations to implement strict security policies and ensure that only authorized users can access sensitive data. This helps to reduce the risk of data breaches and ensure compliance with data protection regulations.
4. Efficient data analysis: Organizations can analyze AI-generated data efficiently within the existing authorized Business Intelligence tools, reducing the time and effort required to analyze large volumes of data. This helps organizations share predictions and forecasts with decision-makers in a convenient manner, reducing the risk of data breaches and other security threats.
5. Improved anomaly detection: In-database machine learning algorithms can also help to detect anomalies and unusual patterns in data, which can be an indication of potential security threats. This helps organizations identify and respond to security threats quickly, reducing the risk of data breaches and other security incidents.

Give Your Database a Brain

There are further benefits to performing machine learning within a database beyond security too. In-database machine learning is a much simpler way to train models because it’s largely automated and integrated with SQL commands. That means data engineers or developers with a basic knowledge of SQL can work within the database, training ML models themselves to solve problems without needing to move the data. 

In-database machine learning is akin to giving your database a brain. It’s fast, effective, affordable and, most importantly, keeps that treasure trove of data securely locked within the database, removing the need to ship it out to third-party programs to leverage its benefits. 

How are you using ML (machine learning) to improve your data security? Share with us on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window . We’d love to hear from you!

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