How to tackle data security and concerns

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In these days of massive data amounts, AI, the development of online media, and such, data privacy and personal data have become an all-time concern. Alongside people that try to test the boundaries of data security and put such information at harm, even data systems and infrastructure can show weaknesses in their defenses. 

But what is data security?

Data security is the process of protecting data from unauthorized and harmful access, corruption, or theft throughout its whole lifecycle. It means protecting data from any infractions and perilous actions, cyber-attacks, or data breaches. Data security can be seen through data management processes, software and data infrastructure, storage, and data utilization, but also through data policies and procedures. 

Digital transformation has changed industries and will continue to do so. But with each passing step, security and data concerns have become strenuously important. The volume of data is growing exponentially, and it can prove to be challenging to handle and protect. 
But, not only companies are aware of data endangerment. Consumers and other users show arise in awareness about data privacy and security issues. Even with laws in place, they are still wary of handing out private data and giving others access to it, and rightfully so.

Not everything is as easy

Data security might seem like a pretty straightforward concept, but it faces many challenges along the way. Each day brings new threats and areas where weaknesses can be exploited. It’s not only external dangers we have to think about, but internal ones as well. Those might be even more complex than we think.

Data storage

A lot of companies save their data in cloud storage because of the sheer volume of data. This means that the control of access can’t fall in the wrong hands. Smaller discrepancies can cause someone not to get access, or give it to someone who’s not supposed to have it. How companies store data is important because of the structure, access, and security access validation processes.

Fake data

When companies use external data sources, it’s integral to validate those data sources and discard fake data created to mislead or misinterpret results. Fake data can skew results and take precious time and resources to get discovered. Continuous data validation should be implemented to prevent anomalies and false findings.

Data privacy

Data privacy is one of the biggest challenges in today’s world. Personal data has become somewhat of a hot topic. Preventing security risks in data privacy is not only regulated by law but is of the highest importance. Personal data and such, are viable for cyber-attacks which can bring a lot of damage in terms of privacy breaches, loss of data, or criminal activities with said data.

Data management

How you manage data is also how you’re going to protect it. Managing user access is one part of it. Structural data management is how companies make sure the infrastructure is done correctly with minimized risk of data infractions. Implementing data observability can help prevent breakage or mistakes in those data management procedures.

Data access control

Controlling which users can see which data, helps preserve data integrity and privacy. It kind of goes against data democratization, but it provides a higher level of security so unauthorized people won’t access sensitive data.

Data poisoning

Data poisoning involves tampering with machine learning training data, by infiltrating maliciously crafted samples, to produce undesirable outcomes. Especially, today when we have so many AI and machine learning tools, it’s tricky to maintain security if only one breach can influence the outcome of the machine learning models.

Employee theft

Trust is a valuable thing, and having trustworthy employees is even more valuable. But, one can not be always sure that some might not misuse data to do harm. With data democratization on the rise, it’s becoming harder to control who has access to which data and how they use it.

How to ensure data security?

Data security is an interesting and really important topic. It’s not something it will ever be taken lightly. With so many risks and threats, companies invest a lot into data protection and making sure it stays safe in their hands. From various procedures to policies and regulations in place, we can sum up some of the data security controls into these segments:

Transparency and compliance

There are certain laws in place, such as GDPR, that serve as a way to protect data and privacy. It’s there to define how customer data can be handled. It also puts the power in the consumers’ hands who decide how they want to share their data. Implementing data transparency and making sure all employees on all levels are familiar with those rules and regulations is integral for stable data security processes.

Access controls

As mentioned before, regulating who has access to data is the first and foremost important step in securing data. As we said, data democratization is on the rise, but not each piece of information should be available across all levels and users. Someone from the lowest hierarchical level doesn’t necessarily need to know top-level information and maybe vice versa. The same goes for different departments. Yes, they might need to share some data that is connected to them both, but certain aspects can be kept to individual users. Also, controlled access to devices or server rooms is an important physical security control.


Multi-factor authentication or verification is important to keep tight security in place. This defines how a system verifies user identities before granting access, from password protections to biometric authentication. Multiple user verification steps are a way of making it harder for other users to access data and content they shouldn’t see.

Backups and recovery

In the case of data corruption, loss, or system malfunction, having backups and data recovery processes in place is a way of making sure that data is not completely lost or damaged. Secure data backups help companies gain control of their data in case of ransomware or other security threats.

Data erasure

Sometimes having old data doesn’t mean that companies should forget about it. Even old data contain sensitive and private information that should’ve been disposed of properly. No matter if it’s in physical or digital form, proper data erasure is another step of protection. This should be done regularly since it presents a security risk.

Data masking

Data masking is the process of modifying data to hide sensitive information. It’s structurally like real data, but with adapted or modified content. Data masking creates fictitious versions of data valueless to those that present security risks. It cannot be reverse-engineered so there’s a lower chance of others getting access to the original data set. One form of data masking can be data anonymization which is often used.

Data resiliency

This is an organization’s ability to provide unobstructed access to data despite unexpected disruptions. Data resilience usually means storing data in multiple locations so users and applications can still access it even if the primary location gets compromised in any way.


This form of security control is done by encrypting data into another form and can be returned to the original state with an encryption key. Data is safe as long as only authorized users have access to the encryption key.  Encrypted data can only be read after being decrypted.

How does data security influence data science and data engineering?

When working with data, which data science and data engineering do, it’s necessary to follow rules and policies to protect that data. Especially, when dealing with big amounts of data, careful handling must be ensured. When companies employ data science or data engineering services, they want to know their data will be in safe hands. 

For some years now, there has been a shift where data science and data engineering have become means of providing data security. With certain techniques and methods, they can deliver additional measures in securing data. One of the examples is using AI and machine learning to discover discrepancies in data and alert users to unexpected changes or data downtime.

As much as data science and engineering need to follow strict rules about data privacy and handling, in the same scope can they be the providers and enforces of data security. They have almost become woven together in terms of maximizing efforts in data protection. Because if you understand how data is collected, managed, analyzed, and finally utilized, can you discover possible threats and opportunities where others can misuse faults in data systems.

Where the future leads?

When you look at the whole picture, and how much information and personal data you share daily, it’s no wonder that some rules and regulations have to be set in place. It’s no wonder that people try to fight hackers and other data villains to protect every piece of information. Maybe we are not even aware of what is being done to secure our private information. 

With how technology evolves, we will most definitely see even more methods to establish top-level security. And it will absolutely rely greatly on data science and data engineering. Cyber security and data science and engineering won’t become mutually exclusive. The rise of the importance of data security and concerns will maintain to be a top priority and a trend most talked about. 

Personal information is worth its weight in gold, so security is worth even more. No matter if you’re a company or an individual, you have to keep track of and observance of your data. If you want full and optimal data utilization, security must be included in your to-do list. There are no cutting corners when it comes to this. Companies need to get familiar with threats and possible controls so they can implement solutions as soon as possible. 

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