Learn about the risk-reward equations associated with mining data gold within an enterprise. Jorge Torres, CEO and co-founder of MindsDB, says advances in machine learning will improve how we handle, process, control, analyze and detect threats, enabling us to unlock the full value of our data. Advice on how businesses can safely and easily apply AI to sensitive data. data insights.
Businesses have long recognized the value of data as a valuable asset. Data is a veritable treasure trove of actionable intelligence, with the right team of data scientists and data science tools in place to unlock it. Digging the data right reveals pain points, areas for improvement, and opportunities for new and innovative solutions.
However, mining this data for business intelligence is not without its challenges. Companies need to ensure they have the talent and tools to uncover data. Understanding Not only do you effectively leverage that data, but you also make sure you can do it fast enough to maintain a competitive advantage. There is another challenge that is often overlooked. It’s about keeping your data treasure trove safe from attackers who steal your data and demand a ransom.
Data is most secure when stored on a well-configured, enterprise-grade data platform that is regularly updated and maintained by a dedicated team. These databases tend to be heavily locked, but still pose a human factor risk. outside Database vulnerabilities that can lead to credential theft, malware introduction, and basic user error.
Inherent risk of data manipulation
In fact, the very processes involved in manipulating and analyzing data, as is often the case, can put data at risk, especially if that data is manipulated by users or programs outside the enterprise’s database. For example, when a data scientist prepares a dataset to train a machine learning (ML) model, the standard process is to extract, transform, and load (ETL) the data into a comma-separated value (CSV) file and then Use a data analysis tool such as Pandas on your data and load it into an ML tool to train a model. This exposes a large amount of data and poses an obvious security risk. It is also more susceptible to human error. For manual data entry, Error rate reaches 4%So even if a company can avoid a breach, manually importing and exporting data will still cause problems.
Developers are another group that tends to inadvertently expose data when building solutions. Use your API key to make API calls and access data for inferring machine learning models. These keys are usually embedded in the code of the app, and many developers (including younger developers) tend to have access to the code.is more than 40% of companies An API-based security incident occurred in 2022. investigationOver 90% of respondents said their organization has API authorization policies in place, but nearly a third (31%) say those policies ensure an adequate level of authorization. I answered that I am not sure if there is.
Both of these scenarios, involving data scientists and developers, represent the risk of human-caused data breaches occurring outside the database, typically when trying to develop AI-driven solutions.
For this reason, enterprises should start focusing not only on threat detection, but also on architectural risk analysis. In other words, how we manipulate and transfer data is more important than ever.According to one sauce, the dataset on which the machine learning system is trained accounts for 60% of the risk, and the learning algorithm and source code account for 40% of the risk.But what if you could build a solution like this internal Do you use databases instead of pulling data and developing?
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Benefits of in-database machine learning
AI-powered tools are now emerging that can completely bypass the need for cumbersome manual processes involving ETL and CSV and train ML models instead. internal A protective wall for the database itself. Incorporating AI into a data storage platform, rather than handing data off to an AI tool, is a surefire way to avoid the above challenges. Here are some of the ways in-database machine learning can improve data security:
- Improved data processing: In-database machine learning allows organizations to apply AI without transferring sensitive data out of the database, reducing the risk of data breaches. This ensures sensitive data is stored in a secure environment, reducing the risk of data breaches and unauthorized access.
- Streamlined data processing: Organizations can process data quickly and efficiently within the database, reducing the risk of data errors that can occur during data transfer. This ensures that data is processed accurately and securely, and also reduces the risk of data breaches and other security threats.
- Fine-grained access control: Databases provide fine-grained access control to stored data at various levels. This allows organizations to implement strict security policies and ensure that only authorized users have access to sensitive data. This helps reduce the risk of data breaches and ensure compliance with data protection regulations.
- Efficient data analysis: Organizations can efficiently analyze AI-generated data within their existing sanctioned business intelligence tools, reducing the time and effort required to analyze large amounts of data. This enables organizations to easily share predictions and expectations with decision makers, reducing the risk of data breaches and other security threats.
- Improved anomaly detection: In-database machine learning algorithms also help detect anomalies and unusual patterns in data that may indicate potential security threats. This enables organizations to quickly identify and respond to security threats, reducing the risk of data breaches and other security incidents.
Give your database brains
Running machine learning inside the database has additional benefits beyond security. In-database machine learning is a very easy way to train models as it is largely automated and integrated with SQL commands. This means that data engineers and developers with basic knowledge of SQL can work inside the database and train the ML model itself to solve the problem. without it Data must be moved.
In-database machine learning is like giving brains to a database. It’s fast, effective, affordable, and most importantly, it locks a treasure trove of data securely in your database, eliminating the need to send it to a third-party program to take advantage of it.
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