Best practices for securing big data

Data security is an issue of critical concern for any IT project. Lack of proper security protocols can lead to devastating repercussions such as financial losses and vital company data loss. The cost of a successful data breach is not something you would wish to hear. According to a 2020 IBM cost of data breach report, breaches of 1 million to ten million records will cost an average of $50 million. A data breach could also damage the reputation of the organization. Thus, protecting data from unauthorized access remains a significant area of focus for data scientists and security experts across the globe. Indeed, data security is a big challenge, especially following the data breaches that have been witnessed in the recent past. Whatever it takes, you should always ensure that you protect your massive data reservoir. There is a much bigger challenge in securing big data. The already existing security practices may not be efficient in protecting big data, which makes the task of building a full-proof security system a challenging task. However, this should not demotivate you. Several security measures can be of great essence as far as protecting big data is concerned.

  1. Access Limitations and Controls

Access control is very critical in the security of big data. Access control is all about granting user accesses and restricting user accesses. You will need to develop a policy that chooses the right path of access control in all scenarios. The principle of the least privilege will is vital here. It advocates limiting user access to resources, which are essential for the user to carry out his/her responsibilities. It should not go beyond this. If a user or an employee has no business with data or digital resources, he/she should not be allowed to access the data. The concept is vital in big data security that prevents unethical IT experts and malicious employees from indulging in unlawful data mining undertakings. 

  1. Employ the Retrospective Attack Simulation

As cyberattacks become prevalent and fast-evolving, every data faces the risk of a cyberattack. Therefore, it is necessary to detect a cyber-attack as early as possible and determine which data has been breached. Among the many ways to detect probable weaknesses in the data system and identify potential vulnerabilities that a hacker might take advantage of, retrospective attack simulation is a promising one. Because of financial constraints, most organizations cannot build an in-house security infrastructure to help in data protection.

  1. Use the latest Antivirus Protection.

Antivirus software will protect your data from viruses, spyware, and other forms of malware attacks. It is like the security guard at the gate that will protect your premises from unauthorized access. As the saying goes, prevention is better than cure. It is less costly to have antivirus software in place to help secure your data from malware attacks than it would be spending time and resources to repair already infected databases. It is, therefore, vital to protect your big data with up to date antivirus software. You should also carry out regular updates to the software to equip yourself with more updated and advanced security features that will positively impact securing your big data.

  1. Conduct Regular Security Audits

Conducting regular security audits is a must if you have to achieve your big data security objectives. Conducting regular security audits will be essential in detecting possible vulnerabilities and loopholes that can easily allow hackers to carry out a data breach. Security audits will also help to verify whether your existing security strategies are adequate. After conducting the security audit, you will then need to seal all the loopholes that were detected from the audits and also realign your security infrastructure to prevent future occurrence of a data breach. 

  1. data encryption using an SSL Certificate

The secured Sockets Layer is a security protocol tasked with ensuring the safety and integrity of data transfers being transferred between web browsers and web servers. SSL encryption translates data into an undecipherable form. The only person who can unlock the data is the one who possesses the right decryption key to do so. SSL encryption will, therefore, make it hard for intruders and unauthorized parties to access your big data. 

  1. Fortify Authorization Gateways

Not just anybody should be able to access your data reservoirs. Weak authentications mechanisms could render your big data vulnerable to intruders. You should make sure that you use strong and unique passwords when validating who can access your data reservoirs. A strong password is one that can withstand attempts to get past it. A unique password is one of its kind. You should ensure that you encompass best password practices in your big data security strategies.

Apart from just using passwords to authorize accesses, you can go an extra mile by using a multiple factor authentication process. You can also take advantage of biometric authorization features such as fingerprints and face recognition. In so doing, you will be strengthening your security walls and protecting your big data from being accessed by unauthorized parties. 

  1. Take note of insider threats.

Sometimes, some of the worst security threats are brewed and organized from within your organization. As you build security walls to protect your big data from external attacks, you should also start thinking about preventing insider threats. Insider threats may occur due to malicious employees or errors committed by employees, which intruders might take advantage of. 

You must equip your employees with digital security skills and data integrity knowledge. Your employees should know the best password practices and detect a possible data breach and security threats that could lead to data losses. Doing this will be helpful in insider threats that could be a threat to your big data reservoirs. 

  1. Centralized Cryptographic Key Management 

Cryptographic key management refers to the processing of safeguarding your cryptographic keys from loss or ill-use. Having a centralized key management system is more efficient when compared to distributed management. A reliable cryptographic key management system is very crucial in ensuring security for big data.

  1. Use the Intrusion Detection and Prevention Mechanism

The intrusion detection mechanism is needed in protecting your big data from unauthorized accesses and vulnerability exploits. It does this by frequently inspecting network traffic. The Intrusion Prevention System usually sits behind the firewall and would prevent intrusion before it leads to actual damage.  


There has been an increase in big data security threats, especially in the recent past. Safeguarding data from security vulnerabilities has, therefore, become a critical aspect of every organization. Although a challenging task it might seem, securing your big data from intruders is achievable. This article has provided eight of the best tips with which this can be done. I advise that you make use of multiple measures, if not all, to maximize the security of your data reservoirs. 

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