Digital transformation is disrupting industries and fueling data growth. The global datasphere was initially projected to reach 175 zettabytes by 2025. Recent projections, however, indicate that it will be in excess of 200 zettabytes. Safeguarding this hypervolume data generation and sprawl is no longer a choice but a critical priority.
This is particularly crucial for organizations that engage in collecting, processing, sharing, and storing individuals’ sensitive data, including financial details, health records, etc. As reliance on digital systems accelerates, there is a need to protect against vulnerabilities that might result in inadvertent data exposure.
This gives rise to the concept of data protection. Let’s dive deeper into what data protection is.
What is Data Protection?
Data protection is the set of processes, industry best practices, data privacy and security policies, and the implementation of robust data protection tools that ensure the protection of sensitive data from data exposure, data loss, and corruption that might jeopardize data integrity.
At its core, it’s the practice of ensuring data remains protected against evolving cyber threats, unauthorized access, and misuse throughout the data lifecycle.
Why is Data Protection Important
Daily, 2.5 quintillion bytes of data are created, which comes down to 29 terabytes of data each second. For data-hungry businesses, each megabyte of this data is crucial as it helps them make informed decisions, improve user experience, and streamline operations.
Any adversary that aims to target this data, including data systems, data assets, and data warehouses, is a massive threat businesses shouldn’t take lightly. A single exploit can drain years of reputation and completely disrupt operations, causing significant financial losses.
The emergence of Generative AI has further escalated cyber risks as malicious actors leverage the technology to conduct attacks on a massive scale. Thus, data protection is crucial to protect organizations from vulnerabilities, data loss, theft, and leaks.
Principles of Data Protection
The foundation of data protection is to use measures and tools to protect and ensure that data is readily accessible in any situation. The two core aspects of data protection are data availability and data management.
Data availability ensures data users are able to obtain the data they want for daily tasks, regardless of the data status. On the other hand, data management contains two critical aspects: data lifecycle management and information lifecycle management.
Data lifecycle management ensures data is handled properly throughout its lifecycle and complies with industry-wide best practices and regulatory requirements. Whereas information lifecycle management assesses, protects, and determines data relevance.
The most notable data protection principles are outlined by the European Union’s General Data Protection Regulation (GDPR), which outlines the following data protection principles:
- Lawfulness, Fairness and Transparency: Personal data should be processed lawfully, fairly and in a transparent manner.
- Purpose Limitation: Personal data should only be collected for specified, explicit and legitimate purposes.
- Data Minimization: Personal data should be adequate, relevant, and kept to a minimum for processing purposes.
- Accuracy: Personal data should be accurate and updated as needed.
- Storage Limitation: Personal data should only be stored in a format that allows data subjects to be identified for as long as is required to fulfill the purposes for which it is processed.
- Integrity and Confidentiality: Personal data processing must be done in a way that ensures proper security, including safeguarding it from unlawful or unauthorized processing as well as against unintentional loss, destruction, or damage via the use of suitable organizational or technical safeguards.
- Accountability: Data controllers are responsible for honoring compliance requirements and demonstrating compliance with data protection principles.
Key Pillars of a Data Protection Strategy
The basis of a robust data protection strategy begins by aligning the data protection strategy with business goals. Key pillars include:
a. Data Discovery and Classification
You can’t protect what you can’t see. Data often resides in silos, unprotected and susceptible to exposure. Begin by conducting a comprehensive data discovery of data present in on-premises and cloud environments. Assess data assets across a broad spectrum of data stores to identify data types and data sources. Once identified, classify data into sensitivity levels and determine whether any protection measures exist.
b. Assess Internal and External Threats
Cyber threats are prevalent and will only amplify in the coming years. The key is to gear robust defences that amplify data security posture against evolving threats. Conduct an internal and external audit of security posture to assess the current threat vector. Adopt measures, including strong passwords, role-based access controls, and state-of-the-art encryption, to steer clear of internal and external threats that put data at risk.
c. Regularly Review and Update Data Retention Policies
Ensure your data retention guidelines are routinely examined and revised to adhere to the most recent laws and industry best practices. To minimize data exposure, implement custom policies based on the business context and automatically remove data that is no longer required.
Data Protection vs Data Privacy
Despite the regular disarray between the phrases, there is a significant distinction between data protection and data privacy. Data protection offers the security measures, tools, processes, and regulations that limit access to sensitive data. Whereas data privacy establishes who can access the data and empowers data subjects with the rights to access, correct, delete and know whether the organization is keeping their data private.
Notable Data Protection Regulations
Governments and regulatory bodies have established various data protection regulations in recognition of the significance of data protection. Among the most notable are:
a. GDPR
By far the most stringent data protection regulation, the EU’s GDPR imposes strict compliance obligations on data controllers and is particularly concerned with personally identifiable information, or PII. It requires businesses both inside and outside of Europe to be transparent about how they handle data. It requires the appointment of a dedicated data protection officer to supervise data handling, among several other obligations.
b. HIPAA
HIPAA creates standards for how healthcare organizations and companies manage patients' personal health information (PHI) in order to ensure its privacy and security. All "covered entities" are required to adhere to specific data security and compliance guidelines. These organizations include business associates who have access to PHI in addition to insurance companies and healthcare providers.
c. CCPA/CPRA
Similar to the GDPR, the CCPA/CPRA empowers individuals with more control over their personal data and puts the burden of transparency on companies about their data practices. Californians can opt out of data sales, seek data erasure, and obtain information about the data that organizations gather about them.
Automate Data Security with Securiti DSPM
Data protection, privacy and security go hand in hand. Securiti’s Data Security Posture Management (DSPM) provides holistic insight into the security posture of your data assets, whether on premises, cloud, or spanning multi-cloud environments, and automates your data protection initiatives.
It automatically remediates misconfigurations by continuously assessing, managing, and reporting on data protection posture while organizations focus on detecting and remediating risks, ensuring that your sensitive data stays protected at all times.
Request a demo to learn more.
