In recent years, business professionals realized the vital role of data analytics in supporting organizational decision making. Cloud-agnostic data analytics continues to gain the favor of enterprises for activities performed on online data, such as analysis and visualization. Data privacy remains a key concern in terms of legal or competition, as large amounts of data stored on the cloud tend to be sensitive and prone to cyberattacks.
Ensuring data privacy is a significant issue because enterprises lose control over their data in cloud environments. As privacy threats come from inside and outside, it is imperative to maintain data protection, not only from external adversaries but also from malicious insiders along with keeping an eye on the CSPs.
Radical Pillars of Data Security in Cloud Analytics
Handling critical enterprise data on the cloud seemed like a difficult proposition, as the penalty for breach is severe. This further led organizations to consider four key measures for security in cloud analytics.
- Incident Logging and Monitoring: It is imperative to monitor the data infrastructure for threats while subjecting the same to penetration tests. The changes observed in the environment must be logged and reviewed periodically.
-
Data Classification: Some data fields are highly sensitive and create the need for categorization in terms of encryption and access-level requirements. These requirements involve the encryption of data fields at rest and/or during transit. The diversity of business requirements further necessitates the logical and/or physical segregation of the data, to prevent accidental access or contamination.
-
Third-Party Audits: The value of audits carried out by third parties cannot be overstated. This is because, despite their best intentions, enterprises can develop collective myopia when testing is avoided. The audit reports are standardized according to the industry guidelines. Although this comes with a significant investment, IT IS IMPORTANT, as the organizations cannot afford to lay weak foundations.
-
Access Control: In order to regulate access, movement, and manipulation of the data, technical controls and policies are required to be in place. These controls can further vary on the basis of the access profile, application programmer, and data analyst.
If an enterprise is missing even one of these pillars, it implies a hole in the security blanket, which is a crucial matter of concern.
Laying the Groundwork for the Future
It is evident that the cloud computing environment is suitable to analyze and host big data. There are several developed as well as developing security attacks, which are huge threats to the cloud computing environment. Data security in cloud analytics means that the demand for new countermeasures that are more effective and efficient is on the rise.
Key stakeholders in the space of data security in cloud analytics are investigating the deployment of modern cryptographic methods in real-time, which includes the format-preserving encryption and homomorphic encryption in cloud environments for data security in cloud analytics.
Regulatory frameworks appear as a hindrance for enterprises that look to process their data in the cloud. However, it is a fact that remaining compliant is the first step toward protecting data, respecting the confidentiality of individuals, and demonstrating attributes of forward-thinking organizations.