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How Can I Secure Sensitive Data In Cloud Environments?

Life in the cloud moves faster than anyone imagined just a year ago. And data protection in the cloud has grown ever more complex. Although many organizations have been using cloud-based solutions for years now, they often struggle to adequately monitor, manage, and secure their cloud infrastructure and protect sensitive data. Misconfiguration, distributed workloads, exfiltration of sensitive data, and multi-cloud deployments are just some of the challenges enterprises face as they try to secure cloud access and ensure compliance.

By adopting a comprehensive and layered security approach, including robust encryption, access controls, and continuous monitoring, organizations can effectively safeguard their sensitive data in the cloud, aligning with industry best practices and staying ahead of potential threats.

Access Control

Often overlooked due to its “everywhereness,” access control supports the need-to-know principle that ensures only authorized users can access sensitive data. Using explicit permissions, it establishes a secure environment where data is protected and carefully governed.

Access control is the cornerstone of effective data protection strategies in cloud environments. Systems like Attribute-Based Access Control (ABAC), Role-Based Access Control (RBAC), Discretionary Access Control (DAC), and Mandatory Access Control (MAC) authenticate and authorize users based on their roles and responsibilities within an organization and ensure each is given access only to the information and resources necessary for their job functions. Because access rights are tightly controlled and monitored, risks like unauthorized access and data breaches are minimized.

Policy-based access control (PBAC) is an innovative method of managing access that relies on rules or policies to determine who can access what data under what circumstances. The evolution of all other access control methods that have come before it, PBAC lets you quickly adjust entitlements in response to changes in requirements, ensuring assets are secure. It’s also a highly adaptable solution, supporting various access points using automated security controls in applications and on data.

AI in access control uses sophisticated features like anomaly detection, facial recognition, and predictive analytics to enhance traditional mechanisms and uses real-time data to adapt to changing situations and optimize access control decisions.


Integral to safeguarding at-rest and in-transit data, encryption transforms sensitive data into a secure format, readable only by those with the necessary decryption key. Data-at-rest encryption addresses risks associated with physical storage devices, keeping data protected and unintelligible to unauthorized users or hackers who attempt to exfiltrate files. It’s particularly useful for ensuring that, even if data is stolen, it remains useless to cyber attackers.

Data-in-transit encryption secures data as it moves between services, virtual machines, and external partners. It prevents interception, eavesdropping, or manipulation during data exchanges and ensures data integrity and confidentiality as information flows across a diverse and intricate cloud infrastructure.

Major cloud providers like AWS, Microsoft Azure, and Google Cloud Platform (GCP) integrate robust encryption mechanisms in their services. For instance, AWS S3 uses “bucket versioning” to preserve, retrieve, and restore any amount of data from anywhere at any time. It offers extensive configuration options for access control and encryption, including setting up connectivity controls, implementing user and role-based access policies, mandating HTTPS for encrypted access, and providing advanced encryption-at-rest options.

Masking, Anonymization, and Pseudonymization

Three pivotal techniques used in cloud environments to secure sensitive data are masking, anonymization, and pseudonymization. They protect personally identifiable information (PII) such as bank account details, social security numbers, and private health records. While each method differs in its approach to concealing real data, they’re united in protecting sensitive information.

  • Masking obscures sensitive data by replacing it with placeholders like Xs or zeroes. For instance, an IBAN number might be disguised as XXXXOOOOXO. Masking is often used in outsourcing and offshoring scenarios where a business needs to hide specific data without altering the overall application structure.
  • Anonymization uses techniques like shuffling, swapping, and masking to replace sensitive data with a pseudonym or a value that doesn’t allow the individual to be directly identified. The method is standard practice for creating test data in data-intensive systems like accounting software, where maintaining inter-table dependencies is critical.
  • Pseudonymization replaces sensitive data with tokens in specific systems or databases. The tokens are indecipherable to users without access to the de-pseudonymization. While pseudonymization and anonymization are technologically similar, they serve different use cases, with pseudonymization processing highly sensitive data in a way that results in the entire system working only with data that’s had its identifying details removed.

AWS, Azure, and GCP each provide various features that support these data protection methods. Services offered include AWS Data Migration Service and Azure SQL Database’s Static Data Masking, both of which transform data during copying processes. GCP BigQuery’s Dynamic Data Masking provides on-the-fly masking in user interfaces. However, it’s important to keep in mind that while these cloud services provide tools for masking and anonymization, they don’t offer comprehensive, one-size-fits-all solutions. Each service has different capabilities and terminologies, and organizations must tailor them to fit their specific cloud landscapes and security requirements.

Data Loss Prevention and Cloud Access Security

Velotix’s automated data security solution represents a cutting-edge approach to safeguarding sensitive information. By introducing PBAC into the data protection process, this dynamic model transforms traditional access control, providing a more flexible, secure, and comprehensive solution that allows you to take your cybersecurity strategy to the next level.