Introduction to Security
Security is a major concern these days, independent of fields and technologies. As compared to other areas Big Data to have securities issues and attacks happening every single minute, these attack can be on different components of Big Data, like on stored data or the data source.
So what is Big data security? Its the collective term for all the measures and tools used to guard both the data and analytics methods from attacks, theft, or other malicious activities that could cause a problem or negatively affect them. Like other forms of attacks, the big data can be compromised either by attacks originated from online or offline spheres.
As a Hadoop process, multiple types of data which are combined and stored in Hadoop data lake, and then the stored information is processed accordingly.
As we know, big data is not small things, and also we can’t describe it in the context of size, as size is one of the main features of Big Data. So to secure it someone can construct various strategies like keeping out unauthorized users and intrusions with firewalls, making user authentication strong, giving training to end-user training, and many others.
What are the Challenges in Securing Big Data?
Below are some common challenges –
- Vulnerability to fake data generation
- Struggles of granular access control
- Often “points of entry and exit’ are secured, but data security inside your system is not secure.
- Data Provenance
- Securing and protecting data in real time
Why Big Data Security is important?
Today almost every organization is thinking of adopting the Big Data as they are seeing the potential and utilizing the power of Big Data, they are using Hadoop to process these large data sets. And securing your data is the most important step they are concern about, independent of organization sizes, everyone is trying to secure their data.
As a Hadoop process, multiple types of data which are combined and stored in Hadoop data lake, and then the stored information is processed accordingly. As it saves a different kind of data from various sources, so we need to make security essential as almost every enterprise that are uses big data has some form of sensitive data, which need to be protected.Sensitive data can be the user’s credit card details, banking details, passwords.
Big Data Security Best Practices
Today large and variety of data are collected and stored than ever making big data a solution to every industry’s need. And securing big data is not such an easy task, but we can formulate some best practices to secure it, below are some best practices for big data security –
As discussed above the flow of big data, its comprised of three layers: Incoming, stored, and outgoing data. So we can construct our security accordingly, and make it more secure.
- Continuously monitoring and auditing all access to sensitive data(firstly find out all the sensitive data).
- Alert and react to intrusions and unlawful actions in real time.
- Use Latest Antivirus Protection.
- Secure Data Storage, we can implement a technique called secure untrusted data repository (SUNDR).
- Secure hardware and software configurations.
- Implement the principle of least privilege (PoPL).
- Implement the data-in-transit and at-rest encryption throughout the Hadoop clusters.
- Enable encryption for NoSQL database, and all entry points.
- Implement intrusion protection systems and intrusion detection systems.
Strategies for Granular Access Control, some are listed below –
- Point out mutable elements and immutable elements.
- Access labels should be maintained, track admin data too.
- Use single sign-on, and maintain proper labeling scheme.
Big Data Security Tools
So keeping the basic security pillars in mind, we can implement different tools for different categories, like for –
Authorization, Centralized Administration and Audit – Ranger
Data at rest/in-motion Encryption – HDFS and Wire encryption.
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