Data security is an umbrella term, which covers all the tool and measures experts use to safeguard the data and the analytical procedures from any attacks or malicious activities. Harm to business data or theft of it can adversely affect any organization as data is considered to be one of the most valuable assets. Like any other element of cybersecurity, big data is also under the threat of attacks from online intruders as well as the offline attackers. Organizations which are operating on cloud, the security of big data is one of the biggest challenges they face, which is also multi-faceted to be handled very carefully.
Big data security
As discussed above, a threat to big data is multi-faceted, which include, but not limited to:
- The threat of data-stealing from information which is stored online.
- DDoS attacks
- Ransomware etc.
Such issue may go worse while organizations tend to store their confidential and sensitive information like customer demographics, financial details, credit card details, etc. in a delicate manner. Any such attack to the big data stores of an organization can have very serious financial and operational repercussions in businesses including big losses on market, litigations, losing business privileges, fines, and other big costs.
Considering big data security, no there are three major best practices in terms of big data security, or rather we can call it three major challenges which define how the modern-day organizations should set up their big data security.
- The first and foremost challenge is the security of incoming data with a chance of it getting corrupted or intercepted during the inflow transit.
- The second challenge is related to the storage of big data, which could be stolen from there or held hostage being at the servers, either on-premise or cloud.
- The third challenge is about the data getting outputted, trivial among the three but can surely act as an access point to the malicious intruders to reach other areas.
All three concerns play prime roles in creating a foolproof big data security policy for any organization, irrespective of its size, modes of operation, or the industry in represent.
Implementation of big data security
There are many ways through which the modern-day organizations plan to implement data security in order to safeguard their big data stores and analytics tools. A very common and conventional measure for data security is encryption of sensitive data, which remains a very simple but strong tool to rely on. The encrypted data is of no use to the data hackers without decrypting it. Both input and output data can be encrypted this way to ensure complete protection of information.
Setting up a solid firewall, as done by a provider like RemoteDBA.com, could also be a reliable security tool for big data protection. Firewalls act as an effective mode of filtering the traffic which enters and leaves the business database servers. With the help of a reliable firewall, enterprises can prevent any data damage before it happens with the help of reliable filters and avoid any unknown sources of data. Most importantly, having control over who has access to the business data and analytics platform is a key thing in data protection. Organizations now have a tiered data access model, which help reduce any opportunity of an attack or data damage by mistake.
Protecting the transaction logs
As discussed above, one important thing in terms of database security is the protection of the transaction logs. While crucial data is stored in any storage medium, which includes sensitive data and transaction logs of varying levels, it is important to ensure security and privacy. For example, transfer of data between various levels in an organization given an insight to the IT managers over the volume of data being moved and the path of its movement as the size of data is constantly increasing lately, availability and scalability of data mandate auto-tiering for big data management. Many new challenges are getting added to the storage of big data daily, and more advanced measures also needed to be adopted by organizations.
Filtration and validation of the end-point Inputs
One major reason for maintaining big data stores is the end-point devices. Input data is used to perform key tasks like storage and processing, which is offered by the end-points. This is the reason why organizations adopting big data should ensure to use only legitimate and reliable end-point devices.
Securing the distributed framework
Taking care of digital assets and computational security of the frameworks as Hadoop functions and MapReduce tend to lack attention in many organizations in a big data environment. However, this is one important area to consider if you are seriously thinking about big data security. The major preventative measures for it are safeguarding the mappers and ensuring the protection of data if there is the presence of any unauthorized mapper.
Protecting and securing data in real-time
With a larger amount of data getting generated each day in big or small organizations, one should find it difficult to maintain regular data security checks. With a huge volume of data generation, it is easy to lose focus and ultimately lead to data leaks. But it should be streamlined and done a default practice to perform adequate security checks and implement modes of close observation of data security in real-time. There are many big data tools available lately in order to monitor and ensure data security closely and identify any threats at the first point and give suggestions to eradicate such issues.
In such cases, it is also an intelligent move to use secured devices for data storage, which has many options now both in terms of on-premise storage and also cloud storage. Relying on vulnerable data storage devices may end you up in big trouble over time. If in case you have to use such services for even unimportant data storage, ensure that you encrypt data and also encrypt the access control methods too as it can otherwise be an easy entry point to the intruders as we discussed above.
Data provenance and enabling granular-level access control are other measures for database protection. In any case, organizations should ensure that all their big databases and storages are immune to any vulnerabilities and security threats.
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