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What Is Cloud Data Protection?
5 min. read
Cloud data protection is the practice of securing a company’s data in a cloud environment, wherever that data is located, whether it’s at rest or in motion, and whether it’s managed internally by the company or externally by a third party.
This practice has become increasingly important as more companies have switched from building and managing their own data centers to storing their applications and data in the cloud instead. A 2018 survey by IDG, a leading technology media company, stated that 73% of companies had applications or infrastructure in the cloud, with another 17% expected to make the move in the coming year.1
Why Companies Need Cloud Data Protection
Companies are collecting massive amounts of data, ranging from highly confidential business, financial and customer data to fairly unimportant information. They’re also moving more and more of their data to the cloud and storing it in more places than ever – public, private and hybrid clouds, cloud storage environments, software-as-a-service applications, and so on.
As they do this, companies are discovering just how complicated protecting and securing all their data across multiple environments can be. For example:
- They no longer know where all their applications and data are.
- With most of their applications and data housed on third-party infrastructure, companies no longer have visibility into who is accessing and using their applications and data, which devices are being used for access, or how their data is potentially being used or shared.
- They have no insight into how cloud providers are storing and securing their data.
- Even though most cloud providers have state-of-the-art security, this security is limited. After all, companies and cloud providers share responsibilities for cloud security.
- Different cloud providers have varying capabilities, which can result in inconsistent cloud data protection and security.
On top of this, companies face a host of security challenges, including the potential for:
- Security breaches
- Loss or theft of sensitive data
- Application vulnerabilities and malware propagation
Companies must also comply with data protection and privacy laws and regulations, such as the General Data Protection Regulation, or GDPR, in the EU; the Health Insurance Portability and Accountability Act of 1996, or HIPAA, in the U.S., and others. However, it can be incredibly difficult for companies to consistently establish and enforce security policies across multiple cloud environments, let alone prove compliance to auditors.
For these reasons, it’s no surprise that nine out of 10 cybersecurity professionals are concerned about cloud security. They say their biggest challenges are protecting against data loss and leakage (67%), threats to data privacy (61%) and breaches of confidentiality (53%).2
This also explains why the data protection market is projected to surpass US$158 billion by 2024.3
How Companies Can Better Protect Their Data in Cloud Environments
To successfully protect and secure their data in cloud environments, companies must first know:
- Which data they have and where it’s located.
- Which data is exposed, how it’s exposed, and potential risks.
- Which applications are being accessed and by whom.
- What’s happening inside their applications (e.g., how people are accessing and using them).
- Which data they need to protect and at what level.
With this information in hand, companies must then put consistent, unified, and automated cloud data protection offering in place – one that will help them discover, classify, monitor, protect, and secure their applications and data across multiple environments. This offering must also be able to distinguish between everyday activities and potentially suspicious ones.
The Benefits of Cloud Data Protection
Among the benefits of cloud data protection, it enables companies to:
- Secure applications and data across multiple environments while maintaining complete visibility into all user, folder and file activity.
- Proactively identify and mitigate risks, such as security threats, suspicious user behavior, malware and others.
- Better govern access.
- Define policies.
- Prevent and detect data loss and disruption.
For more information on cloud data protection and cloud security, visit paloaltonetworks.com/cloud-data-loss-prevention.
Sources:
- “2018 Cloud Computing Survey,” IDG, August 14, 2018, www.idg.com/tools-for-marketers/2018-cloud-computing-survey.
- “2018 Cloud Security Report,” Cybersecurity Insiders, 2018, start.paloaltonetworks.com/cloud-security-report-2018.
- “Data Protection Market … Global Forecast by 2018 - 2024,” Market Research Engine, December 2018, https://www.marketresearchengine.com/data-protection-market.
Cloud Data Protection FAQs
Data encryption in the cloud involves converting plaintext data into ciphertext using cryptographic algorithms to ensure that only authorized users can read it. Encryption can be applied to data at rest, such as stored in databases or file systems, and data in transit, such as during transmission over networks. Techniques like Advanced Encryption Standard (AES) and RSA are commonly used. Cloud providers often offer encryption services, enabling customers to manage their own keys through Key Management Services (KMS). Encryption ensures data confidentiality and integrity, protecting against unauthorized access and breaches.
Access control in cloud environments involves managing who can access resources and what actions they can perform. It uses policies and mechanisms to restrict access, ensuring that only authorized users and applications can interact with sensitive data and services. Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) are common models. Implementing access control includes defining roles and permissions, using Identity and Access Management (IAM) tools, and enforcing least privilege principles. Effective access control mitigates the risk of unauthorized access and potential data breaches, enhancing overall cloud security.
Cloud data loss prevention (DLP) refers to the use of technologies and practices to detect, monitor, and protect sensitive data from unauthorized access, leaks, or breaches. DLP solutions identify and classify sensitive information, such as personally identifiable information (PII) and intellectual property, and apply policies to prevent its unauthorized transfer. Techniques include content inspection, contextual analysis, and user behavior analytics. DLP tools integrate with cloud services to enforce policies on data sharing, storage, and transmission. Implementing DLP helps organizations comply with regulatory requirements and safeguard sensitive data in the cloud.
Cloud data classification involves organizing data based on its sensitivity and importance to the organization. It helps in identifying and protecting sensitive information by assigning categories such as public, internal, confidential, or restricted. Automated tools use techniques like pattern matching, machine learning, and contextual analysis to classify data. Classification policies guide how data should be handled, stored, and shared. Effective data classification enhances security by ensuring that appropriate controls are applied to different data types, aiding in compliance and risk management efforts.
Cloud storage security encompasses policies, technologies, and practices designed to protect data stored in cloud environments. It includes measures like data encryption, access control, and regular security audits. Cloud providers offer features such as encryption at rest and in transit, identity and access management (IAM), and anomaly detection. Secure configuration of storage services, monitoring for unauthorized access, and implementing backup and disaster recovery plans are essential aspects. Ensuring cloud storage security protects against data breaches, loss, and unauthorized access, maintaining data integrity and confidentiality.
Identity and access management (IAM) in the cloud involves managing user identities and controlling access to cloud resources. IAM systems authenticate users and authorize their actions based on defined policies. They support features like single sign-on (SSO), MFA, and role-based access control (RBAC). Cloud IAM services provide centralized management of identities, enabling administrators to enforce security policies and monitor access activities. Implementing robust IAM practices ensures that only authorized users can access sensitive data and applications, reducing the risk of unauthorized access and enhancing security.
Multifactor authentication for cloud security strengthens the authentication process by requiring users to provide two or more verification factors. These factors typically include something the user knows (password), something the user has (security token or mobile device), and something the user is (biometric verification). MFA significantly reduces the risk of unauthorized access by ensuring that even if one factor, such as a password, is compromised, the attacker cannot gain access without the additional factors. Integrating MFA with cloud services enhances security, protecting sensitive data and applications from unauthorized access.
Cloud data residency refers to the physical location where data is stored and processed within cloud environments. It is influenced by regulatory and compliance requirements that mandate data to be stored within specific geographic boundaries. Organizations must ensure that their cloud providers comply with data residency laws, which can impact data sovereignty and privacy. Cloud providers offer options to select data storage regions, enabling organizations to meet legal obligations. Understanding and managing data residency is crucial for ensuring compliance with regional regulations and maintaining data privacy and security.
Cloud data integrity ensures that data stored in the cloud remains accurate, consistent, and unaltered during storage or transit. Techniques like cryptographic hashing and digital signatures verify data integrity. Regular integrity checks and validation processes detect any unauthorized changes. Implementing strong access controls and encryption further protects data integrity. Monitoring tools can alert administrators to potential integrity breaches. Ensuring data integrity is critical for maintaining trust, compliance, and operational reliability in cloud environments.
Cloud data backup and recovery involve creating copies of data and storing them in a separate location to protect against data loss. Automated backup solutions capture data at regular intervals, ensuring up-to-date copies. Recovery processes enable quick restoration of data following incidents like accidental deletion, corruption, or cyberattacks. Cloud providers offer scalable storage options and disaster recovery services to minimize downtime. Implementing robust backup and recovery strategies ensures business continuity and data resilience.
Key management in cloud security involves the creation, distribution, storage, and lifecycle management of cryptographic keys. Effective key management ensures that keys are protected, rotated regularly, and retired when no longer needed. Cloud providers offer Key Management Services (KMS) to automate and simplify these processes. Using Hardware Security Modules (HSMs) enhances key protection by providing a secure environment for key storage. Proper key management is essential for maintaining the confidentiality, integrity, and availability of encrypted data in the cloud.
Cloud data anonymization involves transforming data to prevent the identification of individuals while retaining its utility. Techniques include data masking, pseudonymization, and generalization. Anonymization ensures compliance with privacy regulations, such as GDPR, by protecting sensitive information from unauthorized access. Implementing anonymization processes allows organizations to use and share data for analytics and research without compromising privacy. Ensuring effective anonymization requires careful planning and robust methodologies to balance data utility and privacy.
Cloud data masking involves obfuscating sensitive data elements to protect them while maintaining data usability. Techniques include substituting real data with fictitious data, shuffling data values, or applying encryption. Data masking is used in non-production environments, such as testing and development, to prevent exposure of sensitive information. Implementing data masking ensures that sensitive data remains protected from unauthorized access and reduces the risk of data breaches. Effective data masking strategies balance data protection with the need for realistic test data.
Cloud data auditing involves systematically reviewing and verifying the integrity, access, and usage of data stored in the cloud. Auditing tools track and log activities, such as data access, modifications, and deletions. Regular audits ensure compliance with regulatory requirements and organizational policies. Automated auditing solutions provide real-time monitoring, generating alerts for suspicious activities. Conducting thorough data audits helps identify security gaps, enforce data governance, and maintain accountability in cloud environments.
Tokenization in cloud security replaces sensitive data elements with non-sensitive equivalents, or tokens, that retain the data's format but have no exploitable value. Tokens are mapped to the original data in a secure token vault. Tokenization is commonly used for protecting payment card information and personal data. Implementing tokenization reduces the risk of data breaches by ensuring that sensitive information is not stored or transmitted in its original form. Effective tokenization strategies enhance data security and compliance with regulatory standards.
