fbpx
5 Cybersecurity Tips for Data Warehousing 5 Cybersecurity Tips for Data Warehousing
Data warehousing makes large-scale AI and machine learning applications much more manageable. While having everything in one place enables faster, more... 5 Cybersecurity Tips for Data Warehousing

Data warehousing makes large-scale AI and machine learning applications much more manageable. While having everything in one place enables faster, more accurate analysis, it also introduces some security challenges. These large, consolidated databases are tempting targets for cybercriminals, so they need thorough protection.

Just as data warehouses themselves vary between organizations, so do specific security systems. Still, you should implement a few best practices regardless of your setup. Here are five key cybersecurity tips to consider.

In-Person and Virtual Conference

April 23rd to 25th, 2024

Join us for a deep dive into the latest data science and AI trends, tools, and techniques, from LLMs to data analytics and from machine learning to responsible AI.

 

1. Anonymize and Encrypt Data

Encrypting all the data in your warehouse is the first step. Applying a higher encryption standard to your data will ensure it’s useless to hackers even if they do access it. New technologies like homomorphic encryption take this a step further by letting you use data while encrypted instead of decrypting it before use.

Depending on the kind of data you use, you may also need to anonymize it. This is the process of removing personal identifiers from your information to prevent privacy breaches. Swapping real-world figures for synthetic data is the most secure method, but pseudonymization is a good alternative if your data must reflect real-world people.

2. Restrict Access Privileges

The next step in data warehouse cybersecurity is to restrict users’ access privileges. The best way to approach this is to go by the principle of least privilege (PoLP).

PoLP holds that you should only be able to access what you need to do your job correctly. Employees who don’t work with machine learning models shouldn’t be able to access data warehouses specifically for machine learning training. By the same token, data scientists shouldn’t be able to see payroll data.

Restricting access privileges has two key benefits. First, it minimizes human error — which plays a role in 74% of data breaches — by reducing how many people can affect a given data warehouse. Next, it minimizes lateral movement if an attacker breaches one account.

3. Improve Authentication Measures

Remember that privilege restriction only works if you have a reliable way of telling who’s who. Consequently, you must pair PoLP with strong authentication measures. At its most basic, that means enacting multi-factor authentication (MFA).

While you can run MFA in several ways, not all methods are equally secure. SMS-based authentication is more secure than email, for example, as it requires access to a specific device. Biometric authentication may be harder to hack than a password, but if attackers gain access to biometric data, you can’t change it, so it’s not ideal for sensitive warehouses.

 

4. Classify and Organize Data

One easy-to-miss but still crucial step in data warehousing security is to classify your data. While organization may seem like more of an operational issue than a cybersecurity one, it has several significant security implications.

First, you can’t secure what you can’t see. Roughly 60% of security software users analyze less than 40% of their data, meaning they may overlook critical vulnerabilities or fail to recognize breaches. A lack of organization limits visibility, so you must classify your data to organize it into groups to enable more thorough vulnerability analysis and faster incident response.

Classification also aids in refining access privileges. Sorting data by use or sensitivity makes it easier to determine who should be able to access it and to enforce those policies. It also lets you implement behavioral biometrics, which alerts you of potential breaches when people are accessing data they normally wouldn’t.

5. Monitor Warehouses Closely

After you’ve enacted these other changes, you must monitor your data warehouse. No defense is 100% effective, but a quick response will minimize damages in the event of a breach. The only way to ensure you can adapt to new threats or respond to real-time security events is through continuous monitoring.

AI and automation are essential here. Around-the-clock manual monitoring requires a large security workforce. That’s not an option for most organizations, considering the world is short 3.4 million cybersecurity workers despite a growing labor pool. Automated network monitoring can watch your data warehouse for real-time incident containment and alerts to work around that shortage.

In-Person Data Engineering Conference

April 23rd to 24th, 2024 – Boston, MA

At our second annual Data Engineering Summit, Ai+ and ODSC are partnering to bring together the leading experts in data engineering and thousands of practitioners to explore different strategies for making data actionable.

 

Data Warehousing Security Deserves Attention

Data warehouses improve security in the sense that it’s easier to protect one big database than to spread your resources across multiple. At the same time, their size can attract unwanted attention, especially if you’re storing highly sensitive information.

Given these risks, data warehousing cybersecurity is essential. Integrate these five best practices into your existing security system to keep your warehouse as safe as possible.

Zac Amos

Zac is the Features Editor at ReHack, where he covers data science, cybersecurity, and machine learning. Follow him on Twitter or LinkedIn for more of his work.

1