The world of data protection has changed significantly in recent years. Personal and sensitive data used to be treated like any other data, and used to be stored along with the rest of the information in many tables under many databases.
However, several factors have shifted the landscape and raised the stakes for data protection:
- More data is being collected than ever before, and it’s easier than ever to make many copies of that data with a single click.
- Threats and attacks are becoming more sophisticated and harder to protect against.
- Rising complexity of modern cloud architectures makes it harder to ensure consistent data protection measures.
- New and stricter data and privacy regulations, such as GDPR, CCPA, and CRPA, have been enacted to protect individuals’ rights and freedoms.
- Customers are becoming more aware of the importance of protecting their data and are demanding fair processing and usage.
To design a cloud system that is compliant with data protection regulations, emphasizing their data security aspect in this article, organizations must ensure they meet data protection requirements. These include data security to avoid unauthorized access, lawful processing, data minimization, individual rights, data breach notification, and cross-border data transfers.
In this article, we’ll explore these requirements in more detail, discuss the challenges of implementing data protection in modern cloud architectures, and provide practical guidance for designing and building secure and compliant cloud applications.
Data Protection Principles and Requirements
Organizations must ensure they meet the principal data protection requirements to design a cloud system that is compliant with data protection regulations. These requirements include (partially resembles to GDPR):
- Data Security: Organizations must implement appropriate technical and organizational measures to protect personal data against accidental or unlawful destruction, loss, alteration, unauthorized disclosure, or access. This includes measures such as encryption, access controls, monitoring, and regular security testing.
- Lawful Processing: Personal data must be processed lawfully, fairly, and transparently, with a lawful basis for processing such as consent, legitimate interest, or contractual necessity. Organizations must inform individuals about the purposes of processing their personal data and obtain their explicit consent when required.
- Data Minimization: Organizations must only collect and process personal data that is necessary for the specific purposes for which it was collected. This means collecting only the data that is relevant and necessary for the organization’s operations and avoiding unnecessary data collection.
- Accuracy: Personal data must be accurate, up-to-date, and kept for no longer than is necessary for the specific purposes for which it was collected. Organizations must take reasonable steps to ensure that personal data is accurate and up-to-date, and delete data that is no longer needed.
- Individual Rights: Individuals have certain rights with respect to their personal data, including the right to access, rectify, erase, or restrict the processing of their data. Organizations must provide individuals with the means to exercise these rights and respond to their requests within a reasonable timeframe (e.g. through cookie banners, or profile settings).
- Data Breach Notification: Organizations must notify individuals and relevant authorities in the event of a data breach that is likely to result in a risk to their data and their rights. Organizations must also have procedures in place to respond to data breaches and minimize their impact.
- Cross-Border Data Transfers: Organizations must comply with applicable laws and regulations when transferring personal data across borders. This includes obtaining appropriate consent, implementing safeguards such as standard contractual clauses, and ensuring the same level of data protection as required in the country of origin.
Challenges of Implementing Access Controls in Modern Cloud Architectures
Modern cloud architectures are much more complex than in the past. This rising complexity makes it harder to ensure consistent data protection measures. In distributed, microservices, and event-driven architectures, services are designed to be independent and loosely coupled, and data is often propagated and copied across services to keep them independent. This results in more copies of data being created, which makes it very hard to implement and enforce data protection measures consistently across all copies of the data. It also increases the risk of unauthorized access or data breaches if appropriate. Holding many copies of the same data results in multiplied risk and thus storing it has to be designed differently.
In the next section, we’ll provide practical guidance for designing modern cloud applications with data protection in mind.
Designing Modern Cloud Applications with Data Protection in Mind
To address the challenges of implementing data protection in modern cloud architectures, organizations should consider the following best practices:
- Identify the crown jewels. Personal Identifiable Information (PII) is among the most important to protect. Because a stolen customer data record with identifiers can easily be used to single out that individual and compromise her privacy. However, a leaked de-identified record, without identifiers, won’t require a breach notification. Other types of sensitive data, such as Protected Health Information (PHI), credit card numbers (PCI), bank account numbers, SSNs, KYC documents, or secrets, require special care as well.
- Segregate, tokenize, and centralize The crown jewels. PII and other sensitive data should not be kept like any other information in normal data stores, and should not be communicated in clear across internal systems by default, unless the channels are secure too. Instead, it should either be redacted, or be stored and communicated in tokenized or encrypted form. The real values and encryption keys should be protected by a solution that allows centralized governance over the data – a data privacy vault.
- Decryption to real values should only happen when it’s really needed, typically when crossing external system boundaries. Otherwise, the goal is to keep the footprint of the sensitive data as minimal as possible.
- Use deterministic security measures, as much as possible, like data masking to reduce sensitive data footprint is one of the most effective techniques, so even if the web application is fully compromised it will be impossible to leak all the information.
- Implement a data privacy vault that can securely store sensitive data in a centralized fashion, enforce consistent access controls, log all accesses, enforce data minimization, and support individual rights.
- Standard encryption and tokenization algorithms should be used for the implementation. Don’t roll your own crypto. Also using open source libraries for the cryptography algorithms is very error prone, so make sure you audit the code carefully.
By implementing these best practices, organizations can ensure that their cloud applications are secure. In the next section, we’ll discuss the key requirements for a data privacy vault solution.
Requirements for a Data Privacy Vault Solution
A data privacy vault is a centralized solution for securely storing, encrypting and managing sensitive data. When selecting a data privacy vault solution, organizations should ensure that it meets the following requirements:
- Usability and integration: The solution should be easy to use and integrate with other systems and services, without requiring significant changes to the existing application architecture.
- Resilience and scalability: The solution should be highly resilient, robust, and scalable, to ensure that it can handle large volumes of sensitive data and traffic.
- Security: The solution should be highly secure, with data encrypted within it and encryption keys rotated periodically. Access should be tightly controlled and monitored, with granular access control policies and masking or tokenization of sensitive data when possible.
- Audit logging: The solution should store an audit log of all accesses to sensitive data, to enable monitoring and reporting of data access and usage. Remember to not log the data itself.
- Privacy controls: The solution should enforce privacy controls, such as access for an approved reason (traceability), consent management, and data minimization.
- Compliance: The solution should support compliance functionality like DSAR and RTBF, data retention, data localization and others to support regulations such as GDPR, CCPA, and CRPA.
By selecting a data privacy vault solution that meets these requirements, organizations can ensure that their sensitive data is well-protected and compliant with data protection regulations. Great companies such as Netflix, Apple, Slack, and JP Morgan Chase and Grubhub have successfully done it on their own with a massive undertaking.
In summary, designing and building secure and compliant cloud applications requires careful consideration of data protection requirements, challenges, and best practices. Here are the key takeaways from this article:
- The world of data protection has changed significantly in recent years, with more data being collected, modern cloud architectures becoming more complex, threats and attacks becoming more sophisticated, and new and stricter data and privacy regulations being enacted.
- To ensure compliance with data protection regulations, organizations must meet data protection requirements, including data security, lawful processing, data minimization, individual rights, data breach notification, and cross-border data transfers.
- Implementing data protection measures in modern cloud architectures is challenging, but can be achieved by identifying the crown jewels, segregating and centralizing PII and sensitive data, decrypting only when necessary, and implementing a data privacy vault solution.
- When selecting a data privacy vault solution, organizations should ensure that it meets key requirements, such as usability and integration, resilience and scalability, security, audit logging, privacy controls, and compliance.
- Great companies have successfully implemented data protection measures in their cloud applications, providing valuable examples and best practices for others to follow.
By following these key takeaways and best practices, organizations can ensure that their cloud applications are secure and compliant with data protection regulations, providing their customers with the confidence that their personal data is well-protected.