Top Big Data Security tips

A massive amount of data is being collected every day. Every business that has ever existed online has collected customer data. This data streams from a range of smart devices interconnected as the IoT (Internet of Things). Computer capacities are growing worldwide, so the amount of data is also increasing exponentially; as the number of data increases, so do security concerns. 

A vast volume of available online data is sensitive and up for grabs by whoever knows the nooks and corners of the web, which is worrisome for most people.

So, what is Big Data? Big data is made up of complex, large data sets that need to be analyzed and characterized for the information to benefit businesses or individuals. There are a few factors inherent of big data that can further simplify the concept.

1. Big data is comprised of information that grows exponentially

2. Conventional data processing procedures cannot be used to analyze big data because of it’s the sheer volume

3. Data mining, data analysis, data storage, data sharing, and data visualization, are all parts of Big data analyzing procedure

4. Big data is a comprehensive term including data, data framework, tools, and techniques used to analyze it

Types of Big Data

Although Big data is an all-inclusive term, there are types of Big data. Let’s have a look:

. Structured

When the data can be processed, stored, and eventually retrieved in a secure, per-ordained fashion, the information is called structured data. This data can be easily accessed from a database using a simple search engine algorithm. 

. Unstructured 

Data without a defined form or structure is described as unstructured data. It is difficult and time-consuming to process and analyze this data. An example of unstructured data is email. 

. Semi-structured

Data containing both structured and unstructured format is called semi-structured data. Although this data does not fall under any database, it might provide vital information that segregates individual elements within the data. 

The security challenges of big data

Big data is doing great things, building and tearing down businesses every second is no game. But, are you prepared to take a hit when Big data collapses around you or becomes a death trap for your business? No, right? And you shouldn’t prepare for that; instead, you should prepare to fight off any challenge arising from big data, especially the big security challenges.

These are some of the challenges:

· Fake data generation

Cyber criminals can fabricate data and pour it into your data pool to misguide you into dismissing valuable trends while embracing non-existent ones.

· Untrustworthy mappers

After collection, big data goes through parallel processing. Data might be split into several bulks first, after which a mapper processes them and allocates them to specific storage. If cyber criminals get hold of a mapper’s code, they can utilize these codes to make mappers create inadequate lists of value pairs. Outsiders can also get access to sensitive information.

· Cryptographic protection and its problems

Although the promise of end-to-end encryption to protect confidential information is common these days, the actual procedure is often ignored or kept at the back foot. A lot of data is stored in the cloud without proper encrypted protection.

· Information mining

Perimeter-based security allows systems to be protected from all entry; but, what about inside the system? What IT specialists do inside the system is not only unprotected, but it is also a mystery in most cases.

· Security Audits

It is advised that businesses should hold security audits regularly. However, most companies do not follow this advice; therefore, awareness of security gaps is also dropping every day.

Top Big Data Security Tips

1. Security first

You cannot wait for a data breach to assess your security measures or to secure your data. Before starting a big data project, your IT security team and everybody else involved should have the scary but immensely important data security discussion.

2. Accountability centralization 

There is a possibility that your data currently resides in diverse organizational silos & data sets. It would be best if you centralized the accountability for data security, which will ensure consistent policy enforcement and access control.

3. Data encryption 

Data flow must be protected at entry points and while it is in motion inside the system as well. So, you can add transparent data encryption at the file layer, and SSL encryption to the information as it moves between nodes & applications.

4. Separate encryption keys and data

You cannot store your encryption key and encrypted data together on the same server because this would be like locking your door and leaving the key hanging by the lock. You need a key management system to keep your encryption keys separately and secure.

5. Authentication gateways should be protected

Most data breaches are the result of weak authentication. A hacker can easily access sensitive data by exposing vulnerabilities in the authentication function. 

If the implementation of the user authentication process is flawed, the chances of a breach can increase tenfold, and that is why one must ensure that there are no broken authentication tokens to be exploited by unauthorized users.

6. Implement the principle of least privilege

Ideally, tiered access control and the principle of least privilege or PoLP should be maintained regularly in a business. This ensures limited user access at a minimal level, which allows normal functioning. In short, your users should only get specific privileges that enable them to complete their responsibilities without hiccups.

In the end

Data breaches are increasing every day because of the increased automated data collection. Still, your company does not need to fear big data and the data breach possibility. A big data solution is an answer to all your data breach concerns. 

Employee training, encryption techniques, and a big data strategy created at the inception of a big data project can genuinely help your business. You can also use real information to analyze the current big data situation in your company and create unique solutions.