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Data is moving to the cloud, creating a new challenge for organizations to protect their consumers’ data stored. The first step to securing sensitive data in the cloud is sensitive data discovery. This article describes the types of cloud data migration and their advantages, security & privacy risks of cloud data migration, and why deep data discovery is essential to secure all sensitive data.
A data warehouse is a large collection of an entire organization’s data. It stores data from multiple databases and is optimized for aggregation and retrieval of large data sets. Increasingly, organizations are opting to migrate their data warehouse from on-premise machines to the cloud. Migrating to the cloud helps organizations reduce costs, build the ability to scale quickly, improve security controls, and enhanced governance capabilities such as User Identity and Access Management (IAM) and analytics.
A data lake is a centralized repository that allows organizations to store all their structured and unstructured data at any scale. With a data lake, organizations can store data as-is without having first to structure the data. A data lake can store unstructured data from IoT devices, websites, mobile apps, social media, and corporate applications.
Moving the data lake into the cloud has similar advantages to migrating a data warehouse to the cloud. However, a unique advantage of having the data lake in the cloud is that organizations can run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.
Cloud collaboration brings several benefits to the organization, which traditional file sharing fails to convey. Besides improved security during data transfer, cloud collaboration can make editing and transferring files easier, offer a real-time response, and improve brainstorming.
Migrating data to the cloud can increase performance, scalability, and cost benefits. While the cloud undoubtedly brings several advantages, migrating data to the cloud requires planning & preparation, keeping in mind all the data privacy requirements.
Many privacy laws like the CCPA and GDPR have imposed strict data sharing requirements on organizations. These requirements aim to protect organizations from potential privacy and security risks that might emanate after the cloud migration process is complete and protect consumers’ personal data. Let’s explore some of the major types of risk.
According to market estimates, the global cloud computing market is set to grow at a CAGR of 18%, making its value USD 623.3 billion by 2023. Most major privacy regulations have specific guidelines on data sharing as well as cross-border data transfers. For instance, organizations are required to assess the security posture of a third-party vendor before transferring any personal data and leverage contractual agreements to protect transferred personal data.
Transferring data from legacy infrastructure to cloud storage can come with a myriad of security concerns. These concerns can range from insider threats where the authorization of access is abused to accidental errors from employees, external attacks in the form of malware, misconfiguration within servers, and problems on the cloud provider’s side. Any of these can lead to compliance breaches which can result in heavy fines and penalties. A recent case study by IBM X-Force IRIS showed that cloud-based applications are severely prone to cyber-attacks and cybercriminals exploit cloud applications more than any other platform. Another study by IBM showed that 21% of data stored in cloud storage is sensitive data of the consumer, which can be detrimental in case of a breach in the system.
Lax security controls make organizations vulnerable to data breaches. Data breaches can severely damage an organization’s reputation, decimate customer trust, and expose them to severe legal penalties from regulators. In some jurisdictions, impacted individuals can file civil lawsuits against the organization as well.
During the deep data discovery exercise, the organization needs to make sure that specific steps are performed. A good data discovery solution should help you:
Securiti provides an AI-powered Sensitive Data Intelligence (SDI) solution to help organizations protect data in multi-cloud. Securiti’s SDI delivers all the core features to discover, classify, catalog, analyze and protect sensitive data across all cloud data assets, including all structured and unstructured data systems while ensuring security & compliance are always up to date.
While Securiti’s SDI provides visibility into sensitive data across various data systems, it offers many more features and modules to help organizations fulfill their privacy and security functions. For data privacy, it provides: Data Mapping, Data Subject Rights (DSRs), Privacy Assessments, Third-Party Assessments, and Consent. For data security, it includes data risk, protection, investigation, and access control features to help mitigate the risk of data breaches. All of these functionalities are supported by policy and workflow engines that help automate these functions.
To learn how you can recruit automation and artificial intelligence to help you safely migrate to cloud storage, click here to download our whitepaper.
At Securiti, our mission is to enable enterprises to safely harness the incredible power of data and the cloud by controlling the complex security, privacy and compliance risks.