Big data security is one of the most crucial issues for developers. Every company has been using it for at least five years, and the number of enterprises using cutting-edge data analysis solutions is rising.
Big data has become a hot topic for almost every organization as they attempt to utilize its possibilities. No matter how big or little, every business aims to protect its data.
A 2023 analysis by IBM and the Ponemon Institute estimated that the average cost of a data breach will be US$ 4.45 million in 2023, an increase of 2% from US$ 4.35 million in 2022.
Due in large part to more stringent data privacy laws in places like the EU, California, and Australia (e.g., GDPR, CCPA, and CSP234), data breaches have grown more common, leading to an increase in legal actions and fines. In addition, businesses operating in regulated industries like credit card processing and healthcare must adhere to industry-specific standards like HIPAA and PCI/DSS.
Advanced persistent threats (APTs), ransomware, and social engineering are emerging risks that present serious difficulties since they are difficult to counter and can seriously corrupt data.
Data security issues are complex, thus adding more security solutions won’t be sufficient to solve them. The IT and security teams must collaborate creatively to address these problems. Here, it’s crucial to assess the potential returns on further investments and the affordability of the installed security measures.
Best Practices for Big Data Security
Enterprises can benefit greatly from the insights and improved decision-making capabilities that big data use offers in today’s data-centric economy. However, it also brings with it certain difficulties. We will discuss the key approaches that, in this case, guarantee large data security.
1. Control of user access
To address big data security concerns such as insider threats and excessive privileges, effective access management is essential. An effective way to control access across multiple tiers of large data pipelines is through role-based access management.
For instance, ETL software and other analytics tools that aren’t just for big data engineers should be available to data analysts. Applying the least privilege concept aids in limiting access to the information and resources required for a user’s tasks.
2. Data Privacy
Strict safeguards are required to protect sensitive personal data in the digital era from cyber attacks, breaches, and data loss. Businesses should adhere to strict data confidentiality guidelines and use cloud access management systems that are compliant to strengthen data protection. Having graduated with a degree in cybersecurity, I understand the critical importance of robust data privacy measures and the implementation of effective security protocols. This specialized knowledge equips me to contribute effectively to enhancing data security within any organization.
3. Cloud Security Monitoring
Cloud computing has become a viable option for many businesses because of the significant requirement for processing and storage in big data workloads. Vulnerabilities like misconfigured cloud infrastructures and unprotected API keys, however, cannot be disregarded.
It’s difficult, for example, to leave an AWS data lake on S3 completely exposed to the internet. The usage of an automated scanning tool that swiftly examines public cloud assets for security vulnerabilities makes it easier to address these issues.
4. Centralized Key Management
A centralized key management strategy is necessary for encryption security in a complex big data ecosystem to provide efficient and policy-driven handling of encryption keys. Key governance, from key creation to key rotation, is likewise governed by centralized key management.
Bring Your Own Key (BYOK) is likely the best solution for companies running big data workloads in the cloud since it enables centralized key management without giving an outside cloud provider control over the innovation and administration of encryption keys.
Major Big Data Security Challenges Faced
There are benefits and cons to the ever-expanding amount of data. Improved data analysis can help firms make better decisions, but it also raises cybersecurity issues, particularly when dealing with sensitive data. These are a few large data security concerns that businesses must overcome.
1. Data Storage
For more efficient operations, businesses are using cloud data storage more and more, however, there are security dangers associated with this ease. Sensitive information can be made public through even small data access control breaches. To combine security and flexibility, many big IT organizations use both on-premise and cloud data storage. Less sensitive data is kept on the cloud for accessibility, but crucial data is kept in on-premise databases.
2. Data Privacy
Strict safeguards are required to safeguard sensitive personal data in the digital era from cyber attacks, breaches, and data loss. To strengthen data protection, businesses should adhere to strict data confidentiality guidelines and use cloud access management systems that are compliant.
3. Data Management
Severe consequences could arise from a security breach, such as vital corporate data being exposed in a compromised database. Setting up highly secure databases with different access rules is crucial to guaranteeing data protection. Extensive security features, such as data encryption, segmentation, partitioning, safe data transfer, and trusted server implementation, are provided by robust data management systems.
4. Theft by Employees
Because data access has become more accessible to all employees, there is a greater chance of negligent or intentional data leaks because every employee now has access to vital business information. All businesses, from start-ups to IT behemoths, worry about employee theft. Companies should put legal policies into place and use virtual private networks to safeguard their networks to prevent this threat. Additionally, Desktop as a Service (DaaS) can improve security and limit access to data on local disks.
Conclusion
Big data is an essential element of the digital economy and is already the foundation of numerous sectors, such as e-commerce, finance, and healthcare. Furthermore, as its loss or breach could have disastrous consequences, the security of sensitive data must be of utmost concern. Companies must invest in robust security measures and implement industry best practices. And it includes more than just technology.
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