Methods To Make The Public Cloud More Secure

Methods To Make The Public Cloud More Secure

The public cloud continues to grow at a good pace. According to consulting firm, the market for public cloud services will reach $397.4 billion this year. However, there is still a certain distrust towards the public cloud, especially in those sectors whose data is related to personal information.

The pandemic has accelerated the adoption of the cloud in its storage, processing, and analysis modalities of vast volumes of data. For 48% of companies, the cloud is already their central data repository. According to data from Gartner, the market for public cloud services will reach 397.4 billion dollars this year and grow by 21.4% year-on-year until 2024, as IDC points out.

Many companies wonder if their data and applications are safe in the public cloud and if privacy is guaranteed. According to Kepler’s cloud expert and Software Architect, Diego Prieto, “it must be made clear that the public cloud is secure. However, given the nature of the public cloud to adapt to all needs, it allows security restrictions to be changed if requested.

Therefore, having good governance of the cloud platform concerning security is essential to guarantee the privacy of the data”. Kepler proposes four techniques that allow for making the public cloud an even more secure environment and meeting the objectives of organizations in terms of data security and privacy:

  • Zero trust. Given the rise and prospects for the future of remote work, companies have found it necessary to constantly control and authenticate those who access the organization’s private network. The zero trust model goes one step further than VPNs, asking users from time to time for their identity to prevent outsiders from the organization from sneaking into the server and offering a better user experience.
  • De-identification of data. A zero trust model is used in the cloud provider to reduce the risk of exposure. Thanks to this technique, personal identification information is eliminated, applying masking or tokenization mechanisms. In addition, this technique can be used in conjunction with other more popular ones, such as data encryption, adding an extra level of privacy.
  • Data Lakes. Once the data has been de-identified and the analytics goals achieved, the next step is storage in a data lake, which enables organizations to store massive amounts of information in one governed location. Sensitive information enters the data lake privately and securely; therefore, the risk of exposure to attacks or misuse is reduced.
  • Data governance. The basis for all data privacy and security issues to be carried out is to establish adequate data governance, which involves a series of phases such as knowledge of the data itself, planning, and strategy around management. And within this, a priority in the field of privacy of the same is to guarantee their security.


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