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How can you balance data validation and security in the era of cyber threats?

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Data validation and security are two essential aspects of data engineering, especially in the era of cyber threats. Data validation ensures that the data you collect, process, and analyze meets your quality standards and business rules. Data security protects your data from unauthorized access, modification, or leakage. However, balancing these two goals can be challenging, as they may require different trade-offs and strategies. In this article, we will explore some of the common challenges and best practices for data validation and security in data engineering.

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MEE SUNTHORN

R at MOFRD Partner Ecosystem | Business Development | Emerging Technology | tiny.ee/View-And-Download-Now | Ready to…

1 Data validation challenges

Data validation can be performed at different stages of the data pipeline, such as ingestion, transformation, or loading. However, each stage may present its own set of challenges. For instance, data volume and velocity can affect the performance and scalability of your validation processes, and thus you might need to use parallel processing, streaming, or batch processing techniques to handle large or fast data. Additionally, data variety and veracity can impact the accuracy and consistency of your validation rules. To address this issue, you may need to use schema validation, data profiling, or anomaly detection techniques. Lastly, data value and viability may affect the relevance and applicability of your validation criteria. To tackle this issue, you may need to use business logic, data quality metrics, or feedback loops.

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MEE SUNTHORN

R at MOFRD Partner Ecosystem | Business Development | Emerging Technology | tiny.ee/View-And-Download-Now | Ready to Make an Impact in the Industry | t.ly/ZK0mO
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Cybersecurity Measures - Data Validation Balancing the Two: Risk-based approach: Prioritize security and validation efforts based on the potential impact of a data breach or system compromise. Data classification: Classify data based on its sensitivity and implement appropriate security and validation controls for each category. Automated solutions: Utilize automated tools and technologies to streamline data validation and security processes. Security by design: Integrate security considerations into the design and development of new systems and upgrades. Continuous improvement: Regularly review and update data validation and security measures to adapt to evolving threats and technologies.

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Lochan Basyal

Machine Learning | Data Scientist | Computer Vision | Generative AI
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To balance data validation and security in the era of cyber threats, implement robust validation processes like input validation and anomaly detection to ensure data integrity. Simultaneously, prioritize security with measures such as encryption, access controls, and regular audits. A balanced approach involves secure coding, thorough vulnerability assessments, and staying informed about evolving cyber risks. Integrating vigilant data validation with strong security protocols enables organizations to mitigate risks and maintain data integrity and confidentiality in the face of cyber threats.

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Tiran Fernando

Data Architect ? Data Governance Expertise ? Senior Data Engineer ? Data Scientist
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mploying stringent data validation processes, including input validation and thorough testing, helps maintain data integrity. Utilizing encryption protocols and access controls safeguards sensitive information, preventing unauthorized access. Regular security audits and monitoring tools can proactively identify vulnerabilities, while incorporating user authentication and authorization mechanisms ensures only authorized personnel interact with critical data. Striking the right balance requires a comprehensive strategy that integrates data validation practices with robust cybersecurity measures, creating a resilient defence against potential threats in today's dynamic digital landscape.

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2 Data security challenges

Data security can be implemented at different layers of the data architecture, such as storage, processing, or access. However, each layer may pose different challenges. For instance, data confidentiality and integrity may be compromised by cyberattacks like ransomware or phishing and encryption, hashing, or digital signatures should be used to prevent or detect data breaches. Data availability and resilience could be affected by cyber incidents such as denial-of-service or corruption and backup, replication, or recovery techniques may be necessary to restore or maintain data operations. Additionally, data compliance and ethics may be regulated by cyber laws like GDPR or HIPAA and auditing, anonymization, or consent techniques should be utilized to comply with data privacy and rights.

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Paul Sweatte
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Do not confuse challenges with priorities. Challenges which are not prioritized may be more costly than challenges which are prioritized.

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3 Data validation best practices

To balance data validation and security, you may need to adopt some best practices, such as defining clear and consistent validation rules, automating and monitoring validation processes, and testing and reviewing validation results. You should document and communicate your validation rules to stakeholders and collaborators. Additionally, you should use tools and frameworks that can automate and monitor your validation processes, such as Apache Airflow, Apache Beam, or Great Expectations. To assess the quality of your data, you should use methods such as unit testing, data quality scoring, or data quality dashboards. Finally, it is essential to perform regular reviews and updates of your validation rules and processes.

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Paul Sweatte
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Rules should be specified based on the consequences rather than the technology. For instance, validation that an input is a number can be easily done, but validating that it is a positive integer within a specific range requires documentation, implementation, testing, and automation.

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4 Data security best practices

In order to ensure data security, it is important to implement layered and granular security measures. This includes using network security, database security, and application security. Additionally, authentication, authorization, and encryption should be used to control data access and permissions. Furthermore, it is beneficial to use security standards and frameworks like ISO 27001, NIST, or OWASP. Additionally, following best practices from experts like CERT, SANS, or CIS is recommended. Finally, it is essential to educate and train data engineering teams and users on security awareness and skills like policies, procedures, or tools. This will help foster a culture that prioritizes data security.

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Paul Sweatte
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Best practices are nothing without enforcement, and enforcement is nothing without validating assumptions. Specifically, assumptions about deterministic data need to be visualized, verified, and audited for continuous improvement. A completely automated system cannot continuously improve. Ensure your implementation encodes assumptions into basic hello world tests, otherwise false positives and false negatives will go unseen.

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5 Data validation and security balance

In order to balance data validation and security, you should consider various factors and trade-offs. This includes aligning your data quality and security objectives with your business goals and requirements, assessing and managing risks with a risk-based approach, and collecting and analyzing feedback with a data-driven approach. Furthermore, you should ensure that your expectations and standards for data quality and security are balanced with your data engineering resources and capabilities, that your data quality and security controls and mitigations are balanced with costs and benefits, and that your data quality and security improvements are balanced with your data engineering needs and opportunities.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Paul Sweatte
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Before starting, make sure your organization has a process for capacity planning, engineering budgeting, and penetration testing. Otherwise, the definition of balance will not matter. Take heed to Brook's Law and Hofstadter's law as it pertains to avoiding pitfalls when making estimates based on time and resource management without taking domain knowledge, technical debt, and software complexity into consideration.

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How can you balance data validation and security in the era of cyber threats?

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1 Data validation challenges

2 Data security challenges

3 Data validation best practices

4 Data security best practices

5 Data validation and security balance

6 Here’s what else to consider

Data Engineering

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How can you balance data validation and security in the era of cyber threats?

1

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3

4

5

6

1 Data validation challenges

2 Data security challenges

3 Data validation best practices

4 Data security best practices

5 Data validation and security balance

6 Here’s what else to consider

Data Engineering

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