Protecting enterprise data resources is an essential and complex requirement for companies with a modern IT environment. Virtually every organization has important information that it must safeguard, such as regulated data subject to HIPAA or PCI-DSS guidelines. It can also comprise intellectual property and trade secrets that would damage a company if they were lost or compromised by malicious actors.
This guide looks at data protection from multiple perspectives. We’ll look at how data protection is defined, its core principles, and the different ways it can be implemented. Then, we’ll discuss constructing an effective data protection strategy incorporating multiple technologies and processes.
What is data protection?
Data protection is the process of preventing data loss or misuse. It requires a relationship between the collection and use of data, the expectations of privacy, and the regulatory standards that pertain to specific data resources. Data protection attempts to balance the needs of privacy rights while allowing information to be used effectively to meet business objectives.
Implementing effective data protection involves using technological solutions to ensure enterprise information resources are safeguarded from a wide variety of threats. The common threats addressed by data protection include:
- Cyberattacks that deliver ransomware or other malware affecting the availability and integrity of data resources
- Data loss which can be caused by human error, cyberattacks, or environmental disasters
- Data exfiltration by malicious internal or external actors
- Inappropriate use of data resources that can put sensitive information at risk
In addition to the technical solutions used to provide data protection, the individuals who interact with the information must understand how it can be used safely while maintaining its security. An organization’s people are essential to an effective data protection strategy.
Data protection principles
Data protection is informed by several principles that protect information and ensure its availability in all cases. For example, some companies rely on principles such as:
- Data availability - Data needs to be protected to ensure it can be accessed by users and is available for business operations. This includes situations where data has been lost or damaged by malicious actors.
- Data lifecycle management - Data should be protected by automated methods and procedures that create backups and move important data to secondary storage. These backups need to be easily accessible so that data resources can be recreated if they are lost or damaged.
- Information lifecycle management - All data created or ingested by an organization must be evaluated and classified according to its value and sensitivity. Classification enables data elements to be afforded varying levels of protection that correspond to their value and the level of privacy they require.
Additional principles may be mandated by specific regulatory standards based on the type of information in question, where it originated, and how it is being used. For instance, the European Union’s General Data Protection Regulation (GDPR) defines the following seven key principles that companies must comply with when processing EU-based consumers’ personally identifying information (PII).
- Lawfulness, fairness, and transparency must be followed by all organizations collecting and processing data from EU citizens.
- Purpose limitation specifies that data can only be collected for specific purposes and cannot be used for other reasons.
- Data minimization requires organizations to retain the minimum amount of data necessary to meet their business objectives.
- The accuracy of collected data is essential and needs to be updated or modified to reflect changes.
- Storage limitations are required to ensure data is stored for the minimum amount of time to achieve business objectives.
- Integrity and confidentiality, part of the CIA triad (confidentiality, integrity, and availability), are two of the most important principles and mandate that all measures are taken to ensure the privacy of collected data.
- Accountability mandates that the organization processing regulated data take responsibility for complying with the previous principles.
Organizations processing regulated data must be aware of the specific principles that apply to its regulatory standards.
What are the different types of data protection?
Effective data protection cannot be provided by implementing a single technical solution. It demands a comprehensive approach that combines multiple methods and procedures to protect an organization’s valuable data resources. The following are the most important and commonly implemented types of data protection.
Data discovery and classification
Companies should have an inventory of all data assets and classify them according to organizational value and privacy concerns. Data classification enables companies to identify sensitive resources that may be subject to regulatory standards and that need to be handled differently from ordinary information.
Data loss prevention (DLP)
Data loss prevention implements measures by which an organization can prevent information from being stolen, lost, misused, or accidentally deleted. DLP solutions automate the enforcement of a company’s data handling policies by taking the necessary measures to maintain the security of enterprise information resources. Data needs to be classified before it can be used effectively by a DLP tool. Validate your DLP policies and evaluate your DLP solution’s effectiveness with our easy-to-use DLP Policy Testing Tool.
Firewalls
Network firewalls can and should be implemented to restrict unauthorized access to data resources. Hardware and software firewalls are available that need to be correctly configured to permit authorized traffic while keeping unauthorized users out of the network.
Threat detection
Threat detection solutions continuously scan a computing environment for signs of intruders. They can identify weak signals that may indicate the presence of advanced persistent threats (APTs) that can result in a data breach or infrastructure damage. Advanced detection and response tools automate the actions necessary to address threats and offer proactive protection for enterprise data.
Encryption
Encryption is the process of encoding human-readable data into ciphertext using an algorithm and an encryption key. The encrypted data cannot be read without first decrypting it with the key. The most robust implementation is known as end-to-end encryption. This practice encrypts all three states of data: at rest, in use by applications, and being transmitted over a network. Only authorized personnel with access to the decryption key can return the encrypted data to a human-readable form.
Backups
Backups are an essential component of any data protection scheme. Data must be copied and stored in a separate location to protect it from possible loss, corruption, or modification. The copies should be readily available to recover lost or damaged data. Snapshots are a form of backup that take a complete image of a system at a specific point in time and then can be used later for a quick restore.
Replication
Replication is a data protection method that ensures mission-critical information availability. In replication, data is continuously copied to a secondary location where it can be accessed instantly if the primary system goes down or is compromised. Financial institutions and businesses that cannot afford outages employ replication to protect their data. Replication can be expensive and is not necessary in all cases.
Disaster recovery
Comprehensive data protection requires the inclusion of a disaster recovery plan and procedures. In the event of a manmade or natural disaster, organizations need to have the ability to quickly recover the affected systems. This can be done with backups, snapshots, and fail-over procedures to access replicated data resources. The plan should be tested and updated regularly to reflect changes in the environment. Failure to plan for a disaster can put a company that relies on its IT environment out of business.
Authentication and authorization
Strong measures are required to ensure that data resources are only accessed by individuals or applications with proper authentication and authorization. Implementing these measures involves multiple initiatives such as role-based access controls (RBAC), the principle of least privilege, and robust identity and access management (IAM).
Endpoint protection
Every component of an IT infrastructure offers cybercriminals a potential attack surface. This includes items such as routers, ports, and any device that connects to the network. The rise of the remote workforce has complicated the efforts to provide robust endpoint protection. Every employee’s mobile device provides another gateway to enterprise data resources. Software solutions like personal firewalls and DLP tools can help furnish reliable endpoint protection.
Storage limitations
Limiting the amount of information an organization stores provides a form of data protection. Data should never be stored longer than necessary to meet business requirements. Procedures should be in place to periodically purge data that has become obsolete or irrelevant to the organization. This is especially true of sensitive or personal data that presents an attractive target for external and internal malicious actors.
Employee education and training
Effective data protection requires the informed participation of everyone in the organization. Employees need the necessary education to use the company's data protection tools. They also need to understand their role in protecting data resources. Training should be available that addresses the measures employees can take to protect data such as avoiding phishing schemes, using strong passwords, and securing credentials.
The most effective data protection strategy
An effective data protection strategy involves the coordination of several or all of the multiple solutions previously discussed. Organizations looking for the most effective data protection method need to employ an intelligent mix of tools, processes, and procedures.
The following outline offers a recommended path to robust and effective data protection that leverages the various types of data protection.
Develop a data handling policy
Organizations must define how they intend to handle the different types of data they create or ingest. The policy should establish guidelines on how data of varying levels of risk or sensitivity should be handled by everyone in the company. This policy will be the foundation for subsequent steps in the overall data protection strategy.
Examples of the elements in a data handling policy include:
- The standards by which data elements will be classified according to their sensitivity and risk
- Mandates that all sensitive data is encrypted before being transmitted in any form
- Restrictions on which employees and what applications can access specific data resources
Automate data discovery and classification
Companies must know what types of data they have and where it is located. In the past, companies conducted a complete inventory of the environment to discover and classify data resources to align with the data handling policy (Read more about Data Discovery).
However, given the volume and use of data in modern companies, legacy pre-discovery and pre-classification are impossible. Advanced DLP solutions like The Reveal Platform by Next perform data classification on-the-fly, identifying and categorizing data at the point of risk.
At a minimum, data should be classified as high-risk, medium-risk, or low-risk based on how its loss or corruption would affect the organization. The data handling policy may also define additional categories that address sensitive or regulated information.
Implement measures to restrict network and data access
Keeping unauthorized users and intruders out of the computing environment is critical to data protection. This involves installing hardware or software firewalls to limit network traffic. Supplementary protection can be afforded by implementing a threat detection solution to identify hidden risks to the infrastructure and data assets.
Strong authentication and authorization measures are essential to a data protection strategy. Access to sensitive and high-risk data must be restricted to employees needing it to perform their jobs. These measures should include monitoring access attempts to identify potential threats by malicious insiders.
Develop a data backup plan
A backup plan is necessary for virtually any data protection strategy, and data needs to be backed up regularly and stored in a secure location. Cloud backups are becoming increasingly popular because of their immediate availability when needed. Whatever type of backups are used, they should be readily available for recovering systems or applications.
Snapshots and replication are essentially specialized backups that some organizations may need to ensure business continuity. Companies must balance the cost of replication with the needs of the business. In many cases, a viable recovery strategy can use traditional backups and snapshots to rapidly restore an environment without undue effects on the organization.
Implement a data loss prevention solution
A DLP solution automates the enforcement of a company’s data handling policy and forms a critical component of an advanced data protection strategy. As mentioned above, The Reveal Platform by Next is a modern DLP solution that classifies data as it is created or ingested, eliminating the need for manual classification required for legacy DLP solutions.
An effective DLP tool also ensures the data handling policy is followed by all employees and at all endpoints. It can perform functions such as automatically encrypting data before transmission, restricting users from printing sensitive information, and informing users that they cannot access certain assets.
Another powerful aspect of advanced DLP tools is the ability to provide incident-based user education. This education promotes a more security-conscious environment in which everyone knows why they can or cannot perform certain tasks with specific data resources.
Lastly, a DLP tool also provides visibility into data access, system use, and user behavior. This information can be crucial in identifying insider threats to high-risk data resources. Reveal is the first DLP agent to deploy machine learning on the endpoint, and on-device intelligence keeps personal data on the device rather than sending it to the cloud. Because it doesn’t require a connection to a separate analysis engine, Reveal can enforce policies and provide user education even when a user isn’t connected to the network.
Develop and test a disaster recovery plan
Companies need to be prepared for a worst-case scenario in which systems are destroyed by a disaster. This can result from a cyberattack or an environmental anomaly like a flood or hurricane. Systems need to be recovered in an appropriate time frame to minimize damage to the organization, and such plans should be developed for individual systems as well as the complete infrastructure.
It’s essential to test disaster recovery plans regularly and to implement modifications and updates based on the test results and as the environment changes.
Purge unnecessary data
Obsolete or data no longer necessary for business purposes should be securely purged from the system. This may include outdated backup media, stale database records, or expired user ids. Companies should strive to only store the data required to conduct business operations.
Provide employee training
Employee training should be included in all data protection strategies so all employees know how to use the implemented tools and understand their role in protecting enterprise resources.
A data protection strategy that incorporates the preceding elements gives an organization an effective method of securing its valuable information. Combining advanced technology with intelligently constructed processes and procedures keeps a company’s data secure.
Conclusion
Effective data protection involves a comprehensive strategy incorporating multiple tools and tactics. Data loss prevention is integral to an organization’s data protection strategy. It provides data classification, enforces a data handling policy, and offers user training at the point of risk.
The Reveal Platform by Next provides a modern approach to data loss prevention encompassing all the features necessary to protect valuable enterprise information. It’s an easy-to-use solution that provides immediate visibility and value right out of the box. Get in touch today and book a demo to learn how your organization can benefit from making this advanced DLP solution an integral part of its data protection strategy.
