It is vitally important for organizations to protect the valuable data resources their businesses rely on. The complex nature of modern IT environments can make it challenging to provide the necessary level of protection, and addressing these challenges requires companies to develop and implement a viable data protection strategy.
What is a data protection strategy?
A data protection strategy is the collection of tools, processes, and procedures that an organization uses to maintain the availability, integrity, and security of its information assets. While there is certainly room for flexibility when developing a data protection strategy, there are key elements that should be incorporated into all plans.
Without the following essential components, a data protection strategy may not be able to effectively protect sensitive business data.
Identifying and categorizing data resources
The first element of a data protection strategy is to identify and categorize a company’s information resources. In the past identification and categorization was part of a data discovery project, but in today’s world, with the volume of information being created and that it’s constantly in-motion, a real-time approach is necessary.
It’s impossible to devise a realistic protection strategy without adequate knowledge of what types of data an organization processes or stores. Developing a deep understanding of a company’s data resources provides the level of protection the information deserves based on its sensitivity and value. Not all data elements require the same treatment.
For example, companies operating in regulated industries such as healthcare handle sensitive protected health information (PHI) that needs to be handled in compliance with strict data security and privacy standards. The same company may also have a large volume of health-related information that it makes available for free on its website. These two types of data need different levels of protection that will be provided by the other key elements of the strategy.
Categorizing data enables it to be provided the precise level of protection its value warrants. Without effective categorization, companies may end up spending too much time and money protecting low-value data while exposing sensitive data to unnecessary risks.
Data lifecycle management
Lifecycle management defines a framework for how data is handled throughout its lifetime. Lifecycle management begins when a data element is created or ingested into the infrastructure. Ideally, the data is immediately categorized so it can be managed appropriately. Next-generation data loss prevention (DLP) solutions have the ability to automatically classify data. The Reveal Platform by Next, for example, is the first DLP agent to deliver Machine Learning on the endpoint, with a smart agent that identifies and categorizes data at the point of risk.
This is the first of many times you will see how the categorization of a data element is an intrinsic component of how it is protected.
During its lifecycle, a data element will typically be stored in primary and easily accessible storage while it is being actively used. At some point, the information may become less relevant to daily operations and be archived to secondary, long-term storage. Eventually, it may become unnecessary for the organization to retain the data and it can be destroyed — and some regulations require data to be destroyed after a certain time. How data is destroyed may also be informed by its sensitivity and value.
Data backup and recovery
Backup and recovery are critical components of a data protection strategy. Information needs to be backed up to protect against a wide variety of dangers. These include corruption by malware, accidental or deliberate data loss, or inaccessibility due to a ransomware attack.
Organizations may implement multiple backup and recovery tools and procedures to address the varying levels of value and sensitivity of their data assets. Production systems that process regulated data need to be backed up differently than development systems using test information. All business-critical systems should be recoverable in a reasonable timeframe using the implemented backup and recovery procedures.
Data risk management
Data risk management entails instituting procedures and policies that address the wide range of threats that can damage a computing environment and data resources. Risk management is a multi-faceted element of a data protection strategy that includes items such as:
- Data access controls to keep unauthorized users away from sensitive information
- Encryption to ensure data is not usable if it is compromised
- Malicious software protection to keep the environment free of ransomware or other types of malware
- Employee training to raise the level of cybersecurity awareness and underscore everyone’s role in providing data protection
Data loss prevention
Data loss prevention protects valuable information by enforcing a data handling policy in real time. The policy is enforced with procedures such as automatically encrypting data before allowing it to be transmitted or prohibiting the printing of sensitive information in remote locations. (Evaluate the performance of your DLP solution with Next’s DLP Policy Testing Tool.)
Data categorization is essential for a DLP solution to work. The Reveal Platform by Next provides an advanced and reliable data loss prevention tool that dynamically categorizes data so it can be processed according to a company’s data handling policy. The solution employs a modern, cloud-native architecture that works seamlessly and non-intrusively within a customer’s environment to provide DLP functionality without impacting business processes.
As mentioned, Reveal’s DLP agent delivers machine learning on the endpoint. The intelligent agent categorizes data at the point of risk and provides data protection without connection to a separate analysis engine. Reveal also promotes a positive security culture with incident-based user training that increases productivity and reduces the risk of data loss.
Get in touch with Next DLP today and learn how Reveal can become a vital component of your company’s data protection strategy. You can also book a demo and see this valuable tool in action.
Frequently asked questions
What are the key elements of a data protection strategy?
Every data protection strategy has five key elements:
- Categorizing data resources
- Lifecycle management
- Backup and recovery
- Risk management
- Data loss prevention (DLP)
How can businesses develop a data protection strategy?
First, conduct a risk assessment to identify potential risks and vulnerabilities associated with the data you handle and store.
Next, define your specific goals and objectives for protecting this data. They should align with both your business needs and regulatory requirements. Involve key stakeholders, including IT, legal, compliance, and business units, in the strategy development process to address any gaps in your strategy.
Then, ensure you follow industry best practices for data protection, including encryption, access controls, and regular backups. You will need to continuously review and update this strategy as new threats emerge or business needs change.
How can organizations assess the effectiveness of their data protection strategy?
Regular audits are the best way to evaluate the effectiveness of your data protection strategy. However, we also recommend tracking key performance indicators (KPIs) like incident recovery times, conducting regular security drills, gathering employee feedback, and reviewing incident reports to identify patterns.
What are common data protection strategies for cloud environments?
The cloud has unique considerations because, although it makes data more available, this same availability increases security risks. Cloud environments should use a mix of data protection strategies to preserve data integrity, including:
- Encryption
- Access control
- Regular backups
- Data masking
- Monitoring and logging
- Vendor management
How can businesses stay compliant with data protection regulations?
Data regulations like GDPR, CCPA, and HIPAA are well-known standards that change over time. For starters, develop comprehensive data protection policies that align with regulatory requirements as they are today.
You can stay on top of these regulations by appointing a compliance officer, maintaining documentation, and subscribing to alerts about these compliance frameworks. Remember to update your policy documentation and retrain employees on the changes so you stay compliant on paper and in practice.
