Securing Your Data- Understanding Data at Rest, In Use, and In Transit

Difference in Data at Rest, in Use, and in Transit.

For any organization to settle on an appropriate encryption technique, it should be able to tell apart these three data states. The first state of data is data at rest, which involves data stored on devices operating on a particular network. Since this data type is mostly preserved, it has been identified as less risky than data in motion. In this regard, data at rest may include information saved in a database or on a portable device (Ghouse et al., 2019). Secondly, the data in use denotes data actively accessed and processed by the users. Data in this state has been identified as the most vulnerable, irrespective of whether it’s being read, processed, or updated, because of its ease of accessibility (Ghouse et al., 2019). In this stage, the data is regularly exposed to external attacks or becomes vulnerable to human mistakes that may yield serious implications.

Apart from these two states, the third data state is the data in transit. Transit data entails information traveling from one device or location to another. The data in transit includes data on emails, instant messengers, and collaborative tools, together with any other public communication channel (Albugmi et al., 2016). Based on the increased accessibility of this type of data as it travels over the internet or through the private business network, the data often experiences low levels of security as opposed to inactive data. A significant portion of hackers often target data in transit because of its decreased level of security.

Securing Data

Various techniques can be adopted to secure the data in either of the three data states. The data at rest can be secured through data encryption, cloud-based firewalls, and data loss prevention. Encryption is one of the best strategies that can be implemented to protect data at rest. The data files can be encrypted, or the organization may encrypt the entire storage drive. Service providers, such as Google Cloud, AWS, and Microsoft Azure, provide various degrees of automated encryption (Albugmi et al., 2016). Also, some of the latest encryption tools allow the secure deployment of encrypted data in the cloud. These firewalls have also been identified as a technique that can be adopted to protect the data at rest. Some of the key features of cloud-based firewalls include domain name system (DNS) security intrusion prevention and deep packet inspection (DPI) tools (Nanda et al., 2023). If the data at rest should be moved, the firewall’s SSL compatibility ensures that it stays protected.

The third strategy for securing data at rest is the data loss prevention strategy. Data loss prevention is a strategy that prevents the organization from being exposed to various threats, such as phishing attacks and insider sabotage. The access control tools used in this mechanism have been established to prevent data loss by blocking external hard drive connections to enterprise devices and preventing file transfers to personal email addresses (Nanda et al., 2023).

Consistently, these strategies change when the state of data changes. For instance, when data is in transit, it is recommended that organizations automate the detection of unintended data access together with the authentication of network communications. Organizations can use tools such as Amazon Guard Duty to automatically detect suspicious attempts to move data outside the defined boundaries (Nanda et al., 2023). On the other hand, the authentication of network communications involves verifying the identity of communications using protocols such as IPsec or Transport Layer Security (TLS).

Furthermore, the adopted strategy also changes when the considered data is in use. When the data is regularly utilized, it is recommended that the organization control access to the data and adopt encryption strategies. Organizations can regulate access to data by ensuring that metadata is used, thus preventing sensitive information from leaking. Also, the data in use can be encrypted through SSL/TLS or use encrypted tunnels, including but not limited to VPNs and Generic Routing Encapsulation.

References

Albugmi, A., Alassafi, M. O., Walters, R., & Wills, G. (2016, August). Data security in cloud computing. In 2016 Fifth International Conference on Future Generation Communication Technologies (FGCT) (pp. 55-59). IEEE.

Ghouse, M., Nene, M. J., & Vembuselvi, C. (2019, December). Data leakage prevention for data in transit using artificial intelligence and encryption techniques. In 2019 International Conference on Advances in Computing, Communication and Control (ICAC3) (pp. 1-6). IEEE.

Nanda, S. K., Mohanty, S., & Pattnaik, P. K. (2023). An optimized 128-bit cellular automata-based hash function for data authentication at rest and in transit—International Journal of Electrical and Computer Engineering, 13(2), 1858.

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