CSIA31 Cybersecurity Processes and Technologies

Research and Analysis of Sifers-Grayson:

In the current aspect, it has been identified that Sifers-Grayson requires a strong security posture so that proper security can be achieved in the organisation. There are several important technologies which can help Sifers-Grayson to achieve the desired security posture. Learning and implementing new technologies for security is very much important for Sifers-Grayson, so that their organisation can be protected from external threats. The most important technologies on which Sifers-Grayson should be focusing on, are discussed in the following section.

In this context, deception technology can be an important solution for Sifers-Grayson for improving the existing security posture. This is a very important technological security solution which helps the organisations to protect their information by deceiving the attackers. This is the process of using fake servers for distracting hackers from targeting the real servers. The fake servers will not only protect the crucial information of the organisation but it will also help Sifers-Grayson to learn about main objectives and attack procedures of the hackers (Bushby, 2019). In this way, Sifers-Grayson can identify their existing loopholes without being compromised. In the market there are different types of deception tools are available that can be used for protecting the organisation including TrapX, Smokescreen and Cymmetria. These tools can effectively identify any attacks and can safeguard vital information assets of the organisation.

User behaviour analytics is the second solution that can be used by Sifers-Grayson for improving the security posture. This technology focuses on the behaviour of users in any specific department. In this case, big data analytics is used for identifying any suspicious activity or behaviour from the users so that actions can be taken on an immediate basis, if the user tries to do anything wrong (Nguyen et al., 2019).

Improving the existing data loss prevention strategy is another important area for improving the overall security posture of Sifers-Grayson. In this context, encryption technology can be utilised. Encryption of crucial business information will reduce the exposure chances of these data (Adhie et al., 2018). Also, even if the data is exposed, still the data will be of no use for the attacker if it is in encrypted form. Therefore, Sifers-Grayson should be focusing on encryption to improve the security posture.

Deep learning is another important technology that can be used by Sifers-Grayson for improving the security posture. Deep learning can be extremely helpful for advanced threat detection. This technology can be used in the most sophisticated areas of the organisation to have a high level of threat detection mechanism (Kelleher, 2019). In this way, most sensitive departments of Sifers-Grayson can be secured. The main vendors are Google and Databricks.

Cloud network is the fifth technological solution that can be used by Sifers-Grayson to improve the security posture. Using the cloud network, Sifers-Grayson will be able to store their crucial information on a remote site and an extra layer of security can be applied on this information (Brattstrom & Morreale, 2017). For this reason, the cloud network is an important solution for Sifers-Grayson. The main vendors are the AWS, IBM Cloud and Google Cloud.

Improving the existing security posture is very much important for Sifers-Grayson. In the above section most important security technologies that Sifers-Grayson can apply to improve the security posture has been identified. In this context, Sifers-Grayson should be hiring expert teams for implementing these identified technologies.

References:

Adhie, R. P., Hutama, Y., Ahmar, A. S., & Setiawan, M. I. (2018). Implementation cryptography data encryption standard (DES) and triple data encryption standard (3DES) method in communication system based near field communication (NFC). In Journal of Physics: Conference Series (Vol. 954, No. 1, p. 012009). IOP Publishing.

Brattstrom, M., & Morreale, P. (2017, June). Scalable agentless cloud network monitoring. In 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) (pp. 171-176). IEEE.

Bushby, A. (2019). How deception can change cyber security defences. Computer Fraud & Security, 2019(1), 12-14.

Kelleher, J. D. (2019). Deep learning. MIT press.

Nguyen, P. H., Henkin, R., Chen, S., Andrienko, N., Andrienko, G., Thonnard, O., & Turkay, C. (2019). Vasabi: Hierarchical user profiles for interactive visual user behaviour analytics. IEEE transactions on visualization and computer graphics, 26(1), 77-86.

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