Data Privacy vs Data Security: How & Why They Aren’t Same?
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Data privacy is the right to control access to one’s personal data. But, data security is the protective measure taken to prevent unauthorized access to databases and websites.
In this article, we will understand how data privacy and data security are closely related concepts, yet they have different meanings and areas of focus.
Table of contents
- What is data privacy and data security?
- What is the difference between data privacy and data security: A tabular view
- What is more important - Data privacy or data security?
- How do you decide what is a higher priority for data security and data privacy?
- Examples of data privacy and security practices in action
- Consequences of neglecting data privacy and security: Risks & instances
- Key elements of data privacy and security: Further insights
- What is the difference between privacy and security in big data?
- Rounding it all up
- Data Privacy vs Data Security: Recommended reads
What is data privacy and data security?
Data privacy and data security are two critical components of information management, particularly in the age of the internet and digital data. They may seem synonymous, but they refer to distinct concepts that provide a comprehensive approach to protecting sensitive information.
What is data privacy?
Data privacy, often referred to as information privacy, is about the appropriate use and handling of data—specifically, personal data. It focuses on the rights of individuals to control or influence what data is collected about them, how it is used, by whom, and for what purposes.
- Data privacy encompasses compliance with laws and regulations, such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States.
- These laws often stipulate how organizations should obtain consent from individuals before collecting or processing their personal data. They also explain how organizations should store and secure this data, and what rights individuals have in relation to their data (such as the right to access or delete their data).
What is data security?
Data security, on the other hand, refers to protective digital privacy measures that are applied to prevent unauthorized access to computers, databases, and websites.
- Data security also protects data from corruption. It is the practice of keeping data protected from corruption and unauthorized access, thus ensuring privacy.
- Data security includes a wide range of security measures, such as encryption, authentication, access controls, network security measures (like firewalls), and secure coding practices. These measures help protect data both at rest (when it is stored) and in transit (when it is being sent or received).
It’s worth noting that while data security is a crucial component of data privacy (you can’t have privacy without security), the inverse isn’t always true. A system could be highly secure (i.e., hard to hack or access without authorization) but still not respect privacy if it collects, uses, or shares data in ways that are inappropriate or that don’t respect individuals’ rights.
In summary, data privacy is about the right use and governance of data, while data security focuses on the protection of data. Both are vital in today’s data-driven world. In the next section, we demarcate the differences between data privacy and data security using a tabular view.
What is the difference between data privacy and data security: A tabular view
Here’s a table that provides a comparison of the two concepts:
|Data Privacy||Data Security|
|Definition||Refers to the rights and controls individuals have over their personal data. It involves the proper handling of data in terms of consent, notice, and regulatory obligations.||Refers to the measures and controls put in place to protect data from unauthorized access, alterations, or destruction.|
|Focus||Protecting individuals' rights to their data and ensuring lawful, fair, and transparent processing of personal data.||Protecting data from unauthorized users, including cyber threats, both external and internal.|
|Key Elements||Consent, Notice, Regulatory compliance, Right to access, Right to deletion ("right to be forgotten")||Encryption, Firewalls, Access controls, Backups and data recovery, Regular updates/patches, Intrusion detection systems|
|Risks if Ignored||Legal consequences (fines, sanctions), Loss of customer trust, Damage to brand reputation||Data breaches, Operational disruptions, Financial losses, Damage to brand reputation|
|Examples of Measures||Privacy policies, Data protection impact assessments, Cookie consent management||Two-factor authentication, Data encryption, Regular system updates, Security training for staff|
|Regulatory Examples||GDPR (EU), CCPA (California, US), HIPAA (Healthcare, US)||ISO 27001 (International), NIST Framework (US), HIPAA Security Rule (Healthcare, US)|
|Ethical Considerations||Respecting individuals' rights to control their personal data, not exploiting personal data for unfair practices||Ensuring robust protections are in place to prevent unauthorized access, not neglecting potential security threats|
Remember, data privacy and security are intertwined. A robust data security infrastructure is essential for ensuring data privacy. Similarly, an understanding and respect for data privacy principles can guide the development of more effective data security practices.
What is more important - Data privacy or data security?
It’s about having the appropriate safeguards and controls in place to manage risks and protect the integrity, availability, and confidentiality of data. While both concepts aim to protect data, they do it from different angles:
- Data privacy is about respecting and protecting the rights of individuals whose data is being collected.
- But, data security focuses on protecting data from unauthorized access and breaches to ensure its confidentiality, integrity, and availability.
However, both concepts are interrelated. A robust data security framework is necessary to maintain data privacy. At the same time, understanding data privacy rules can help in creating a more robust data security framework. Without strong security, privacy may be compromised, and without considering privacy, security controls could be ineffective.
How do you decide what is a higher priority for data security and data privacy?
Determining whether data privacy or data security should take priority is not a binary decision or a choice of one over the other. Both are integral aspects of data protection, and organizations should aim to maximize both.
However, the relative emphasis on privacy or security might vary depending on factors such as the nature of the data, regulatory requirements, and the specific threats or risks faced by the organization.
Understanding the nature of data
- The type of data that your organization handles can dictate the focus. For example, if your organization handles a lot of personally identifiable information (PII) like names, addresses, and social security numbers, there will be a strong emphasis on data privacy to ensure compliance with laws such as GDPR or CCPA.
- On the other hand, if your data doesn’t involve PII but is still valuable (like trade secrets or proprietary research), the focus may be more on data security to protect against threats like cyber theft or sabotage.
- Regulations can significantly impact whether an organization focuses more on privacy or security. For example, a healthcare organization in the U.S. must comply with the Health Insurance Portability and Accountability Act (HIPAA), which has strict requirements for both patient privacy and the security of medical records.
- Similarly, a financial institution needs to comply with regulations that mandate robust security measures to protect financial data, alongside privacy requirements for customer data.
Identifying risks and threats
- A proper risk assessment can help an organization understand its unique threat landscape, and this can inform the balance between privacy and security. For instance, an organization that operates in a high-risk cybersecurity environment might prioritize security measures to protect against these threats.
- However, it’s essential to remember that the strongest security measures won’t be enough if the organization doesn’t also respect data privacy. Even if data is well-protected from outside threats, there can still be privacy issues if the data is misused or mishandled internally.
It’s not so much about choosing privacy over security, or vice versa, but about finding the right balance and integration between the two. Also, it’s crucial to involve all stakeholders - including legal, technical, and business teams - in these decisions to ensure that the approach aligns with the organization’s legal obligations, its technical capabilities, and its broader business objectives.
Examples of data privacy and security practices in action
Now, let us take a look at some practical examples of both data privacy and data security:
Data privacy examples
- Consent Forms
When a customer signs up for a service, the company provides a consent form detailing how the customer’s data will be used and stored. The customer has the right to agree or disagree with these terms.
- Cookie Management
- Right to be Forgotten
This is a fundamental concept in the EU’s General Data Protection Regulation (GDPR). It allows individuals to request the deletion of their data from a company’s records, provided there are no legitimate grounds for retaining it.
Data security examples
A basic yet essential security measure used to protect data. Passwords need to be strong and unique to provide effective protection.
- Two-Factor Authentication (2FA)
This adds an additional layer of security by requiring users to verify their identity using a second method, such as a mobile app or SMS, in addition to their password.
This is a process of encoding data in such a way that only authorized parties can access it. Even if data is intercepted or stolen, encryption ensures that the data remains unreadable without the decryption key.
These can be hardware or software-based and are used to protect internal networks and data from external threats. They filter incoming and outgoing network traffic based on an organization’s previously established security policies.
- Regular updates and patches
Keeping software, systems, and applications updated is crucial for data security. These updates often contain patches for security vulnerabilities that could otherwise be exploited by malicious parties.
Each of these examples serves to safeguard data from different angles, ensuring that both the privacy and security of data are maintained.
Consequences of neglecting data privacy and security: Risks & instances
Absolutely, the lack of effective systems to manage data privacy and data security can expose an organization to numerous risks.
Here are some examples:
- Data breaches
- Regulatory penalties
- Loss of customer trust
- Legal action
- Identity theft
- Operational disruptions
Let’s take a closer look:
1. Data breaches
- If an organization lacks robust data security measures, it may fall victim to data breaches where sensitive information is accessed without authorization.
- A famous example is the Equifax data breach of 2017 where the personal information of 147 million people was exposed. The breach was attributed to an unpatched vulnerability in one of Equifax’s web servers. The financial and reputational damage to the company was extensive.
2. Regulatory penalties
- Non-compliance with data privacy laws such as GDPR, HIPAA, or CCPA can result in significant fines.
- For example, in 2019, British Airways was fined $230 million under GDPR for a data breach that affected approximately 500,000 customers.
3. Loss of customer trust
- If customers feel that their data is not secure or that their privacy is not respected, they may choose to stop doing business with an organization. This can result in the loss of customers and revenue.
4. Legal action
- In cases of severe data breaches or non-compliance with privacy laws, organizations can face legal action.
- For example, in the aftermath of the Yahoo data breach, which compromised 3 billion accounts, Yahoo faced numerous lawsuits leading to a settlement of $117.5 million.
5. Identity theft
- If sensitive information like social security numbers, credit card information, or health records are compromised due to inadequate security measures, those affected could become victims of identity theft.
6. Operational disruptions
- Cyber attacks exploiting security vulnerabilities can lead to significant operational disruptions.
- For example, a ransomware attack can lock an organization out of its systems, halting operations until a ransom is paid or systems are restored.
These risks underscore the importance of having robust data privacy and security measures in place. Not only do these measures protect the organization and its customers, but they also play a significant role in maintaining the organization’s reputation and customer trust.
Key elements of data privacy and security: Further insights
There are several more elements to consider when discussing data privacy and data security, which we have discussed below:
- Ongoing effort
- Employee training
- Privacy by design
- Third-party risks
- Incident response plan
- Technology solutions
- Legal considerations
- Ethical considerations
Let’s look into these elements in brief:
1. Ongoing effort
- Both data privacy and data security are not one-time actions but ongoing efforts. Threats evolve, new vulnerabilities are discovered, and regulations change.
- This means organizations need to continually assess and improve their practices.
2. Employee training
- One of the largest security risks comes from within the organization itself. Human error, such as falling for phishing scams or misconfiguring databases, can lead to data breaches.
- Hence, regular training and awareness initiatives are crucial to ensure every member of the organization understands their role in data protection.
3. Privacy by design
- This concept refers to including data privacy during the design stages of projects, rather than it being an afterthought.
- Privacy by design not only helps with regulatory compliance but also builds customer trust as it demonstrates the organization’s commitment to data privacy.
4. Third-party risks
- Organizations often work with vendors or third-party service providers who have access to their data.
- It’s important to ensure these entities also follow robust data security and privacy practices to avoid exposing your data to additional risks.
5. Incident response plan
- Despite the best precautions, data breaches can still occur. Having a response plan in place can help limit damage, ensure compliance with any breach notification laws, and restore operations as quickly as possible.
6. Technology solutions
- There are numerous tools and solutions available that can help manage data privacy and security. That includes encryption technologies, VPNs, intrusion detection systems, data loss prevention tools, and more.
7. Legal considerations
- Data privacy and security laws vary by region and industry. Understanding these laws is crucial for compliance.
- For example, an organization operating in Europe would need to comply with GDPR, while a healthcare organization in the U.S. would need to comply with HIPAA.
8. Ethical considerations
- Beyond legal requirements, organizations also need to consider the ethical aspects of data privacy and security.
- This involves treating customer data with respect and not exploiting it for unethical purposes.
In conclusion, data privacy and security are multifaceted issues that require a comprehensive, well-thought-out approach. Organizations need to stay abreast of evolving threats, regulations, and best practices in order to protect their data effectively.
What is the difference between privacy and security in big data?
The concept of privacy and security, when applied to big data, gains a new dimension due to the sheer volume, velocity, and variety of data involved. Big data involves the processing of vast amounts of data, often from varied sources, and this can create new challenges and risks in terms of both privacy and security.
Big data privacy
Big data privacy concerns revolve around whether individuals’ data is appropriately collected, stored, shared, and used.
- Given the amount of data collected and processed in big data environments, organizations often hold incredibly detailed information about individuals. This information could be used in ways that individuals did not foresee or consent to when they initially provided their data.
- One of the main privacy challenges with big data is the issue of “data inference” or “data linkage.” Even if data is anonymized, the vast scale and variety of big data can allow organizations to combine different data sets and infer personal information about individuals. For example, they might be able to re-identify individuals in anonymized data by cross-referencing it with other data sets.
- Another privacy concern in big data is “function creep”, which refers to data being used for purposes different from the purposes for which the data was initially collected. This could happen if an organization decides to use its data for new purposes, or if the data is shared or sold to other organizations.
Big data security
Big data security involves implementing measures to protect large volumes of data from threats like unauthorized access, data corruption, or data breaches. The large scale and complexity of big data can make it harder to ensure all data is properly protected.
- In big data environments, data often flows across different systems, networks, and organizational boundaries, which increases the potential points of vulnerability. Traditional security measures like firewalls and security perimeters may not be sufficient to secure big data environments.
- Big data security involves several layers of security including network security, data storage security, data processing security, data transmission security, and access control. Implementing these security measures in big data environments often requires specialized tools and techniques.
- In addition to this, the use of big data often involves cloud-based platforms or other third-party services, which can also raise security challenges. For example, organizations need to ensure that these services provide adequate security measures and that they handle the data in a way that complies with applicable laws and regulations.
So, while the principles of privacy and security remain the same in the context of big data, the scale, complexity, and often the transitory nature of big data create new challenges and complexities. Consequently, privacy and security need to be considered and integrated into the big data lifecycle right from the start.
Rounding it all up
In conclusion, data privacy and data security are interconnected and vital for organizations in today’s digital landscape. Understanding the differences between these concepts and implementing appropriate measures is crucial.
Data privacy focuses on individuals’ rights to control their personal data, while data security safeguards data from unauthorized access and breaches. Neglecting data privacy and security can result in severe consequences such as data breaches, regulatory penalties, loss of customer trust, legal action, identity theft, and operational disruptions.
By prioritizing ongoing efforts, employee training, privacy by design, incident response planning, and ethical considerations, organizations can protect their data, maintain compliance, and foster trust with customers.
Data Privacy vs Data Security: Recommended reads
- Data Lineage Explained: A 10-min Guide to Understanding the Importance of Tracking Your Data’s Journey
- Data Governance vs Data Security: Nah, They Aren’t Same!
- What is Data Governance? Its Importance & Principles
- Data Governance 101: Principles, Examples, Strategy & Programs
- What is Metadata? - Examples, Benefits, and Use Cases
- Metadata Management: Benefits, Automation & Use Cases
- Data Catalog: Does Your Business Really Need One?
- Business Glossary — Definition, Examples, Responsibility & 5 Common Challenges
- Data Governance Framework — Guide, Examples, Template
- Data Governance Roles and Their Responsibilities
- Data Governance Policy — Examples & Templates
- Data Dictionary — Examples, Templates, Best Practices, and How To Create a Data Dictionary
- What Is a Data Warehouse: Concept, Architecture & Example
- Data Marketplace vs data catalog: Understanding the Differences and Choosing the Right Data Management Solution
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