How do you train for data security in data architecture?

1 Data security principles

The first step to train for data security in data architecture is to understand the basic principles and concepts of data security, such as encryption, authentication, authorization, auditing, backup, recovery, and compliance. These principles provide the foundation for designing and implementing data security policies, standards, and controls that align with the business objectives, regulatory requirements, and ethical values of the organization. You can learn these principles from online courses, books, blogs, podcasts, or webinars that cover data security fundamentals.

2 Data security frameworks

The second step to train for data security in data architecture is to familiarize yourself with the data security frameworks and standards that are relevant for your industry, domain, or project. Data security frameworks provide a structured and systematic approach to assess, plan, implement, monitor, and improve data security practices and processes. Some of the common data security frameworks and standards include ISO/IEC 27001, NIST SP 800-53, COBIT, PCI DSS, HIPAA, GDPR, and CCPA. You can learn these frameworks and standards from official sources, certifications, or case studies that illustrate how they are applied in real-world scenarios.

3 Data security tools and technologies

The third step to train for data security in data architecture is to explore the data security tools and technologies that are available for your data platform, environment, or architecture. Data security tools and technologies provide the means to enforce data security policies, standards, and controls, and to detect and respond to data security incidents and breaches. Some of the common data security tools and technologies include encryption software, firewalls, VPNs, antivirus, IDS/IPS, SIEM, DLP, IAM, MFA, SSO, and CASB. You can learn these tools and technologies from vendor websites, documentation, tutorials, or demos that showcase their features and functionalities.

4 Data security best practices

The fourth step to train for data security in data architecture is to adopt the data security best practices that are applicable for your data lifecycle, architecture, or design. Data security best practices are the recommended guidelines and tips that help you to optimize data security performance, efficiency, and effectiveness. Some of the common data security best practices include following the principle of least privilege, applying the defense-in-depth strategy, implementing data classification and labeling, using secure coding and testing techniques, conducting regular data security audits and reviews, and educating and training data users and stakeholders. You can learn these best practices from industry experts, mentors, peers, or communities that share their data security experiences and insights.

5 Data security challenges and trends

The fifth step to train for data security in data architecture is to keep up with the data security challenges and trends that are emerging or evolving in the data landscape. Data security challenges and trends are the issues and opportunities that arise from the changes and developments in data sources, types, volumes, velocities, varieties, and values. Some of the current data security challenges and trends include cloud data security, big data security, IoT data security, AI/ML data security, data privacy and ethics, data sovereignty and localization, and data security automation and orchestration. You can learn these challenges and trends from research papers, reports, articles, or events that analyze and forecast the data security landscape.

6 Data security mindset and culture

The sixth and final step to train for data security in data architecture is to develop a data security mindset and culture that permeates your data activities, decisions, and behaviors. A data security mindset and culture is the attitude and belief that data security is a shared responsibility and a priority for everyone involved in the data value chain, from data producers to data consumers. A data security mindset and culture can help you to foster a data security awareness, commitment, and collaboration among your data team, organization, and partners. You can develop a data security mindset and culture by setting data security goals and metrics, rewarding data security achievements and improvements, and promoting data security communication and feedback.

7 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?