Understanding Oas36ty Data Protection Management and Its Significance

Posted On June 25, 2024

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Data Protection Management (DPM) uses redundancy and rigorous backup processes to ensure that tasks run on schedule and that data is securely backed up and recoverable. The best data protection management strategies have proven processes and methodologies in place to maintain data integrity.

The concept of data protection management started with data backup reporting tools designed to help storage administrators analyze their backup environments. Early DPM software provided basic information on backup failure and success rates.

With modern data protection management tools, storage administrators have tremendous visibility and control, including the ability to set service level-driven backup and recovery policies and tier data protection based on their value to the customer’s business. Newer products are designed to help administrators understand what is, or is not, working in their environment so they can spend less time troubleshooting and more time managing errors and infrastructure issues.

Some of the benefits of effective DPM include:

  • Improved backup and recovery success rates
  • Timely identification of areas in need of backup policy improvement
  • An enhanced ability to meet data protection SLAs
  • Faster response to backup compliance audits
  • Lower administrative costs for managing backups


What is data protection management?

Data protection management (DPM) is the administration, monitoring and management of backup processes to ensure backup tasks run on schedule and data is securely backed up and recoverable. Good data protection management means having effective backup processes and methodologies in place to maintain data integrity.

Data protection management refers to the best practices an organization applies to managing data protection applications and operations. However, it's also a technology and product category with both backup and systems management application vendors providing software that addresses data protection.

DPM software first entered the enterprise data storage market with data backup reporting tools designed to help storage and backup administrators analyze their backup environments. Early DPM products reported on backup failure and success rates, typically relying on the backup applications' operational log files.

Over time, DPM products became more than just log analyzers; they could also survey the environment and determine if any volumes, directories or files were missed during the backup process. DPM software can also discover orphaned servers that aren't included in current protection policies. They are essential tools for demonstrating compliance with laws and regulatory requirements addressing data protection and data management.


Why is data protection management important?

DPM applications provide backup management features that many backup apps lack. DPM programs have been around for more than two decades. Many backup applications include DPM features and have extensive DPM capabilities.

In addition to reporting on the effectiveness of backup activities, DPM also indicates the likely success of data recovery efforts. Confidence in the recoverability of backed-up data is important for routine recoveries where a file or two or a directory has been deleted or damaged, and DPM technology is increasingly essential for disaster recovery (DR) planning. Many DPM applications can simulate a DR scenario and indicate which files can be recovered to ensure business continuity.

DPM is particularly useful for customers who use more than one vendor's backup application in their data protection infrastructure. Those mixed-vendor environments require expertise in each of the backup apps in use, as well as monitoring and reporting on each one separately. Some DPM products can support a variety of backup applications, making it possible to access, manage and monitor the various backup systems from a single dashboard.

DPM can enhance the backup process and data protection policies in other ways, including the following:

  1. Managing snapshots: Increasingly, users are turning to storage array snapshotting capabilities to accelerate data backup. DPM apps provide tools to manage those proprietary storage snapshots from different storage vendors.
  2. Ensuring regulatory compliance: DPM tools can track data to ensure that its use doesn't conflict with specific regulatory directives such as the Health Insurance Portability and Accountability Act (HIPAA) and the European Union's General Data Protection Regulation (GDPR).
  3. Storage management: Given their reach across multiple server and storage systems and cloud computing resources, DPM applications can report on the general health of these systems, going beyond just managing data protection processes.


Who needs data protection management?

The short answer is any organization that has data and wants to keep it safe. From public and private sector organizations to individual users, data protection is an essential part of overall IT administration. It takes many forms, from thumb drives used as data backup tools to on-premises data storage assets and cloud storage resources.

Deciding to establish a process for protecting data is one of the most important decisions an organization can make. Not only is it an important step toward DPM, it's also essential for complying with laws and regulations addressing data protection issues.

The GDPR and HIPAA address data protection and privacy, respectively. Compliance with the GDPR is an increasingly important mandate, and compliance with HIPAA rules for information security and data privacy is essential in the United States.


What should be part of a DPM strategy?

Modern DPM tools let storage administrators use service levels to set backup and recovery policies and tier data protection based on its value to the business. Newer products are designed to help admins understand what works in their environment, so they spend less time troubleshooting and more time managing errors and infrastructure issues. A DPM tool provides insight into these issues; something standalone backup applications can't always do.

In addition to the features described above, a good DPM process should also include the following capabilities:

  • Configure storage systems and related software, such as general storage management such as space allocation.
  • View and determine the status of all current and past data protection processes.
  • Automate data loss prevention and provide information to address potential losses.
  • Establish a data inventory using data discovery technologies.
  • Enable a system to handle alerts that would indicate a backup process failure and a methodology for responding to and resolving alerts.
  • Provide resources that protect the confidentiality, integrity and availability of sensitive data from data breaches and cyber-attacks using access control and authentication.
  • Ensure data privacy as part of an overall data protection strategy.


Types of Data Management

Data management plays several roles in an organization’s data environment, making essential functions easier and less time-intensive. These data management techniques include the following:

  • Data preparation is used to clean and transform raw data into the right shape and format for analysis, including making corrections and combining data sets.
  • Data pipelines enable the automated transfer of data from one system to another.
  • ETLs (Extract, Transform, Load) are built to take the data from one system, transform it, and load it into the organization’s data warehouse.
  • Data catalogues help manage metadata to create a complete picture of the data, providing a summary of its changes, locations, and quality while also making the data easy to find.
  • Data warehouses are places to consolidate various data sources, contend with the many data types businesses store, and provide a clear route for data analysis.
  • Data governance defines standards, processes, and policies to maintain data security and integrity.
  • Data architecture provides a formal approach for creating and managing data flow.
  • Data security protects data from unauthorized access and corruption.
  • Data modelling documents the flow of data through an application or organization.


Why data management is important

Data management is a crucial first step to employing effective data analysis at scale, which leads to important insights that add value to your customers and improve your bottom line. With effective data management, people across an organization can find and access trusted data for their queries. Some benefits of an effective data management solution include:

  • Visibility: Data management can increase the visibility of your organization’s data assets, making it easier for people to quickly and confidently find the right data for their analysis. Data visibility allows your company to be more organized and productive, allowing employees to find the data they need to better do their jobs.
  • Reliability: Data management helps minimize potential errors by establishing processes and policies for usage and building trust in the data being used to make decisions across your organization. With reliable, up-to-date data, companies can respond more efficiently to market changes and customer needs.
  • Security: Data management protects your organization and its employees from data losses, thefts, and breaches with authentication and encryption tools. Strong data security ensures that vital company information is backed up and retrievable should the primary source become unavailable. Additionally, security becomes more and more important if your data contains any personally identifiable information that needs to be carefully managed to comply with consumer protection laws.
  • Scalability: Data management allows organizations to effectively scale data and usage occasions with repeatable processes to keep data and metadata up to date. When processes are easy to repeat, your organization can avoid the unnecessary costs of duplication, such as employees conducting the same research over and over again or re-running costly queries unnecessarily.


Understanding data protection management

Data protection management (DPM) is the guardian angel of your organization's information, constantly safeguarding and preserving the confidentiality, availability, and accuracy of your valuable data. DPM is an essential practice that involves the strategic use of various tools, techniques, business processes, and methodologies to ensure the overall security and integrity of data within your company. Regardless of where your data is stored—whether it's in an on-premises data center, a managed data center, or in the cloud—DPM encompasses a wide range of critical data protection processes, including:

  • Data lifecycle management: A comprehensive approach to handling data throughout its entire existence, from creation to disposal. It involves understanding the data's value and relevance at each stage and applying appropriate security measures and access controls accordingly. The data lifecycle typically includes stages such as data creation, storage, processing, transmission, archival, and eventually, secure deletion or destruction when no longer needed. DPM ensures that data is protected throughout this entire lifecycle, minimizing the risk of unauthorized access, loss, or corruption.
  • Handling of dispersed data in silos: Many organizations face the challenge of managing dispersed data in silos, where information is scattered across various applications, systems, and departments. This decentralized nature of data can lead to difficulties in maintaining uniform data protection measures and can increase the risk of data breaches. DPM addresses this issue by establishing a unified data protection strategy that encompasses all data sources, ensuring consistent security policies, encryption practices, and access controls across the organization.
  • Implementing data protection policies: A set of guidelines and rules that govern how data should be handled, accessed, and protected within the organization. DPM involves the creation and enforcement of these policies to mitigate data-related risks and maintain compliance with industry standards and best practices, including how to protect your cloud data. Such policies might include encryption requirements, password complexity guidelines, user access controls, data retention periods, and protocols for data backup and recovery.
  • Ensuring compliance with regulatory requirements: In today's data-driven landscape, organizations must comply with a multitude of data protection and privacy regulations, such as the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), or the California Consumer Privacy Act (CCPA). Non-compliance can result in severe penalties and reputational damage. DPM plays a vital role in ensuring that the organization's data practices align with these regulatory compliance requirements. It involves regular audits, risk assessments, and ongoing monitoring to detect and address potential compliance gaps.


What are the key elements of data protection management?

To build an effective DPM system, several key elements must be considered part of an effective data protection management framework:

  • Data inventory and classification: One of the fundamental aspects of data protection management is conducting a comprehensive inventory of all data assets within an organization. This process involves identifying and categorizing different types of data, such as personal information, financial data, intellectual property, or sensitive business data. By classifying data based on its sensitivity and value, organizations can implement appropriate security measures and allocate resources more effectively.
  • Data mapping: Data mapping helps create a visual representation of data relationships within the organization, adhering to data protection principles. Protective technologies, such as encryption, access control, and data backup and recovery, safeguard data from unauthorized access, modification, or destruction. The use of data protection management software can help streamline these processes and ensure the proper functioning of your DPM system.
  • Risk assessment and mitigation: Conducting regular risk assessments is crucial to identify potential threats and vulnerabilities in the data environment. This involves analyzing various risk factors, such as cyber threats, insider attacks, physical security risks, and data breaches. After identifying risks, organizations can develop mitigation strategies and establish controls to minimize the likelihood and impact of data breaches or incidents.
  • Data access controls and encryption: Data protection management relies on robust access controls to ensure that only authorized individuals can access specific data. Role-based access control (RBAC) and multi-factor authentication (MFA) are commonly used to restrict access to sensitive information. Additionally, encrypting data at rest and in transit provides an extra layer of protection, making it challenging for unauthorized parties to access or interpret the data even if they manage to bypass access controls.
  • Monitor and audit: Finally, regular monitoring and auditing of data protection efforts are essential to protect data, ensure data protection, guarantee data security, and identify potential risks, allowing for timely corrective action in accordance with data protection guidelines.


What are data protection management strategies?

A data protection management strategy outlines the overall approach an organization takes to safeguard its data. An effective data protection management strategy is the foundation of an organization’s data security, involving several essential components that work together to create a robust data protection framework.


What should be included in a data protection management strategy?

A comprehensive DPM strategy should include:

  • Data protction policies: Establish clear and comprehensive data protection policies that align with relevant regulations and industry best practices and ensure data is only accessible to those who need it. Ensure that employees, contractors, and third-party partners understand and adhere to these policies. Appoint a data protection officer or a responsible team to oversee data governance and compliance.
  • Employee training and awareness: Educate all personnel about the importance of data protection, the potential risks, and the correct procedures for handling data. Regular training sessions and awareness programs help foster a security-conscious culture within the organization.
  • Incident response plan: Develop a detailed incident response plan that outlines the steps to be taken in case of a data breach or security incident. The plan should include procedures for containment, investigation, communication, and recovery to minimize damage and potential legal consequences.
  • Data backup and disaster recovery planning: Implement a robust data backup and disaster recovery strategy to ensure data availability and integrity. Regularly back up critical data to secure locations to prevent data loss due to hardware failures, ransomware attacks, or natural disasters.
  • Audits and risk assessments: Conduct periodic audits and assessments to evaluate the effectiveness of data protection controls and assess privacy risks for individuals arising from the processing of their data. Compliance audits help ensure that the organization adheres to relevant data protection regulations and identifies areas for improvement.
  • Vendor management: If the organization shares data with third-party vendors, conduct due diligence to ensure they have adequate data protection measures in place. Implement contractual agreements to hold vendors accountable for maintaining data security standards.
  • Data retention and destruction: Develop clear guidelines for data retention and secure data disposal. Unnecessary data should be regularly purged, reducing the risk of data exposure and unauthorized access.
  • Tools, technologies, and practices: Put the appropriate systems and tools in place that allow IT teams to control access to customer and employee-sensitive data.
  • Continuous improvement: Data protection management is an ongoing process. Continuously monitor, review, and update the strategy to adapt to evolving threats and regulatory changes.

By incorporating these elements into a data protection management strategy, organizations can effectively safeguard sensitive data, maintain customer trust, and mitigate the risks associated with data breaches and security incidents.


What are some examples of data protection management tools, services, and solutions?

A variety of tools, services, and solutions are available to support your organization’s DPM efforts. Data loss prevention software, for instance, is designed to detect, prevent, and respond to data breaches, monitoring data in motion and alerting administrators of any suspicious activity. Encryption tools, on the other hand, use algorithms to scramble data, ensuring that only authorized users can access it.

Backup and recovery solutions, also known as backup processes, create copies of data in case of disaster or data loss, while data protection management platforms employ artificial intelligence or other automation to detect errors and optimize them. You can read further about what is cloud backup and recovery here.

When sourcing services, it's important to look for those that can customize to meet your unique security and compliance requirements. At Flexential, we have experts who will help you to choose and implement a backup solution that outfits your unique data retention and compliance requirements as well as help you further design and refine your disaster recovery strategy, including:

  • Encryption in flight and at rest
  • Built-in resilience supported by a robust network backbone
  • Off-site redundancy 
  • Simplified backup and recovery management

By implementing tools, services, and storage systems, organizations can strengthen their data protection management system, ensuring greater security and peace of mind.


Principles of Data Protection

The basic tenet of data protection is to ensure data stays safe and remains available to its users at all times. These are the two key principles of data protection: data availability and data management.

Data availability ensures users can access the data they need to do business, even if the data is corrupted or lost.

Data management encompasses two main areas of data protection:

  • Data lifecycle management: automatically distributes important data to online and offline storage, depending on its context and sensitivity. In today’s big data environment, this includes methods of identifying valuable data and helping the business derive data from it, by opening it for reporting, analytics, development, and testing.
  • Information lifecycle management: assesses, classifies, and protects information assets to prevent application and user errors, malware or ransomware attacks, system crashes or malfunctions, and hardware failures.


Enterprise Data Protection Trends

The latest trends in data protection policy and technology include the following:

  • Hyper-Convergence: With the advent of hyper-converged systems, vendors are introducing devices that can provide backup and recovery in one device that integrates computer, networking, and storage infrastructure. Hyper-converged systems are replacing many devices in the traditional data center, and providing cloud-like capabilities on-premises.
  • Ransomware Protection: Ransomware is a type of malware that infects a system, encrypts its data, and demands a ransom fee to release it. Traditional backup methods are useful for protecting data from ransomware. However, new types of ransomware are able to infect backup systems as well, rendering them useless. This makes it very difficult to restore the original version of the data.

To solve this problem, new backup solutions are designed to be completely isolated from the corporate network, and use other measures, like data encryption at rest, to prevent ransomware from infecting backups.


  • Disaster Recovery as a Service: Disaster Recovery as a Service (DRaaS) is a cloud-based solution that allows an organization to create a remote copy of local systems or even an entire data center, and use it to restore operations in case of disaster. DRaaS solutions continuously replicate data from the local data center to provide a low recovery time objective (RTO), meaning they can spring into action within minutes or seconds of a disastrous failure.
  • Copy Data Management (CDM): CDM solutions simplify data protection by reducing the number of copies of data stored by the organization. This reduces overhead, maintenance, and storage costs. Through automation and centralized management, CDM can accelerate development lifecycles and increase the productivity of many business processes.


Data Protection Strategy

Every organization needs a data protection strategy. Here are a few pillars of a robust strategy:


Audit of Sensitive Data

Before adopting data protection controls, you must first perform an audit of your data. Identify data sources, data types, and storage infrastructure used throughout the organization.

Classify data into sensitivity levels, and see what data protection measures already exist in the organization, how effective they are, and which can be extended to protect more sensitive data. Often, the biggest potential is in leveraging existing data protection systems that are “lying around” or are not used consistently throughout the organization.


Assessing Internal and External Risks

The security team in the organization should regularly assess security risks that may arise inside and outside the organization. Data protection programs must be designed around these known risks.

Internal risks include errors in IT configuration or security policies, the lack of strong passwords, poor authentication, and user access management, and unrestricted access to storage services or devices. A growing threat is malicious insiders or compromised accounts that have been taken over by threat actors.

External risks include social engineering strategies such as phishing, malware distribution, and attacks on corporate infrastructure such as SQL injection or distributed denial of service (DDoS). These and many most security threats are commonly used by attackers to gain unauthorized access to sensitive data and exfiltrate it.


Defining a Data Protection Policy

Based on the organization’s analysis of its data assets, and the most relevant threats, it should develop a data protection policy that determines:

  • The tolerance for risk for every data category: data protection has a cost, and protection measures must be applied in accordance with the sensitivity of the data.
  • Authorization and authentication policy: use best practices and historical information to identify which business applications or user accounts should have access to sensitive data.


Security Strategy

With respect to data protection, an organization’s security strategy should:

  • Take measures to prevent threat actors from accessing sensitive data
  • Ensure that security measures do not hurt productivity or prevent employees from accessing data when and where they need it
  • Manage backups effectively to prevent ransomware or other threats, and ensure constant data availability


Data Protection with Oas36ty

Oas36ty’s data security solution protects your data wherever it lives—on-premises, in the cloud, and in hybrid environments. It also provides security and IT teams with full visibility into how the data is being accessed, used, and moved around the organization.

Our comprehensive approach relies on multiple layers of protection, including:

  • Database firewall: blocks SQL injection and other threats, while evaluating for known vulnerabilities.
  • User rights management: monitors data access and activities of privileged users to identify excessive, inappropriate, and unused privileges.
  • Data masking and encryption: obfuscates sensitive data so it would be useless to the bad actor, even if somehow extracted.
  • Data loss prevention (DLP): inspects data in motion, at rest on servers, in cloud storage, or on endpoint devices.
  • User behavior analytics: establishes baselines of data access behavior, uses machine learning to detect and alert on abnormal and potentially risky activity.
  • Data discovery and classification: reveals the location, volume, and context of data on-premises and in the cloud.
  • Database activity monitoring: monitors relational databases, data warehouses, big data, and mainframes to generate real-time alerts on policy violations.
  • Alert prioritization: Oas36ty uses AI and machine learning technology to look across the stream of security events and prioritize the ones that matter most.



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