In the digital space, nothing is worse than getting your data stolen. The thieves will know your customer's name, location, credit card number, transaction history account and password information, and more! And if your business collects data, you are directly responsible for its security.
So, how do you protect your customer’s data during a data migration especially in, out or across your own secure IT environment?
The normal flow of a data migration, just to recap for everyone, is that data is extracted from one or more source systems (the old legacy system) into a file or database format. These files or databases are often transferred between platforms without much protection and are visible to internal users (and all the contractors that are usually involved as well). Copies are sometimes made as “back up” at different stages in this process.
The fact that data needs to be moved and that it most often is all the data in one go makes it highly vulnerable to leaks and mismanagement.
On a slight side note - let’s not even consider the common practice of attaching large spreadsheets to email and sending them back and the between participants in the data migration. Should never be done. A secure contained data migration solution will not let the data leave and maintain strict access controls and logging.
Every data migration should have a written statement on how the data migration should implement the data security standards required to keep sensitive data safe during the data migration – detailed to a level that will satisfy and external audit hereof.
In this article, we want to enlighten you on the 5 electronic data security standards. Read on to learn how you can protect your customers and users from malicious agents whether in-house or externally.
Data Security Architecture Standards
Data security starts with architecture. And that comes with its standards. Here are security standards for data centers and their architecture you must keep in mind:
- Industry alignment. Ensure that the data platform adheres to recognized industry standards and regulations relevant to your organization.
- Compliance integration. Check if the platform seamlessly integrates with your organization's specific compliance requirements. It should allow you to configure security settings in line with industry regulations and adapt to changes in data protection laws, such as GDPR and CCPA.
- Security certifications. Look for certifications like ISO, SOC, or HIPAA to ensure compliance with government data security standards.
Encryption and Data Protection
Ever wondered how your data stays safe amidst the digital chaos? Encryption is the superhero in this tale, turning your data into an uncrackable code. But not all encryption is created equal—let's look into what makes a rock-solid encryption. However, encryption is not practical during a migration where the team needs to test data’s content and its path through the transformation that makes up the migration. Controlling access and the environment is the only way forward in data migration.
Data security standards often consider end-to-end encryption. It’s like having a secret language only you and your intended recipient can understand. When you send a message or data using end-to-end encryption, it gets scrambled into unreadable gibberish while it travels through the internet. The only way to unscramble and make sense of it is with a special key possessed only by the intended recipient.
Data center security standards also include good data key management. Think of good data key management as being the guardian of a valuable treasure. Your data is the treasure, and encryption keys act as the keys to the vault. You want to keep them in a highly secure environment (like a hardware security module), limiting access to only authorized users, and you want to change them often.
This can also be said about physical security standards for data centers. Moreover, using strong generation methods is vital, which slightly falls out of the metaphor of vault keys but is still important nonetheless.
Finally, let’s talk about data masking. It’s a vital part of working with sensitive information. Basically, data masking is like giving your data a disguise, making it look different to unauthorized eyes. This technique protects sensitive information by replacing, encrypting, or scrambling original data with fictional or pseudonymous data. The goal is to provide a functional yet secure version of the data for testing, analysis, or other non-production purposes.
Access Control and Authorization
Data storage security standards always touch on access control measures and authorization. Limiting access to sensitive data is a critical aspect of data security. A data platform should provide robust access control and authorization mechanisms.
- Role-based access. Ensure that the platform allows you to define user roles and permissions, restricting access to only what is necessary for each user. Granular control is key to preventing unauthorized access.
- Multifactor authentication (MFA). Evaluate if the platform supports MFA to add another layer of security to user logins. MFA is an essential user authentication protocol that enhances user experience.
- Audit trails. Assess the platform's ability to maintain detailed audit trails, recording user activities for security and compliance purposes. A comprehensive audit trail ensures accountability and traceability.
- Integration with identity and access management (IAM). Integration with IAM systems allows for centralized control of access across various platforms and services.
Monitoring and Auditing
Data platform security also requires proactive data access monitoring to detect and respond to security incidents promptly. Audit logs play a significant role in maintaining data security:
Real-Time Monitoring
Robust platforms excel in real-time monitoring capabilities, a feature for swiftly identifying and mitigating potential threats. Real-time alerts, a corollary of this functionality, are necessary for immediate incident response, which is critical for any comprehensive cybersecurity strategy.
Alerting Mechanisms
An effective cybersecurity platform extends its utility through dynamic alerting mechanisms. The ability to generate customized alerts and notifications ensures that security incidents are promptly flagged, enabling a proactive response to evolving threats.
Audit Log Retention
The data platform’s evaluation of audit log retention is vital for ensuring compliance with organizational and regulatory standards. This is an especially important financial data security standard. A crucial consideration involves aligning log retention periods with specific compliance mandates. Additionally, safeguarding this data through encryption and secure storage is imperative to prevent unauthorized access and tampering.
Machine Learning
The evolution of cybersecurity measures demands platforms with advanced capabilities, including the integration of machine learning algorithms. Such technologies enable the identification of abnormal patterns and potential threats in both data and user behavior. While it is not an official data security standard, it’s universally recognized as such.
Compliance Reporting
A comprehensive cybersecurity solution encompasses pre-configured compliance reporting tools. These tools serve as a linchpin for simplifying the arduous process of generating compliance reports, which is critical for internal audits and meeting regulatory requirements. The efficiency of these reporting tools contributes significantly to the overall effectiveness of the cybersecurity platform.
Data Governance and Compliance
Compliance with regulatory standards and industry requirements is non-negotiable in data security. Evaluate the data platform's ability to adhere to these standards.
- Compliance mapping. Ensure the platform can map its security controls to specific regulatory and compliance standards, simplifying audits. It should provide clear documentation and evidence of adherence.
- Data classification. Check if the platform supports data classification, allowing you to tag sensitive data appropriately. Automated data classification can help in consistent and efficient data management.
- Data retention policies. Assess if the platform provides tools to manage data retention policies following compliance requirements. This should include automatically deleting or archiving data based on defined policies.
- Data privacy criteria. Privacy-focused platforms offer features such as the ability to honor user data access requests (e.g., GDPR's Right to be Forgotten) and pseudonymization capabilities.
- Regulatory compliance modules. Some platforms offer specialized modules tailored to specific regulatory requirements, making compliance easier to achieve.
Incident Response and Recovery
No matter how secure a data platform is, it's important to be prepared for data breaches and security incidents. Evaluate the platform's compliance with data transfer security standards for incident response and recovery capabilities.
- Incident response plan. Ensure your organization has a well-defined incident response plan with clear roles and procedures. Regularly test and update this plan to address evolving threats.
- Backup and recovery. Assess the platform's data backup and recovery capabilities to minimize data loss in the event of an incident. Backups should be automated, regular, and tested for data integrity.
- Forensics and analysis. Check if the platform provides post-incident forensics and analysis tools to learn from security breaches. Detailed post-incident analysis helps in improving security measures.
- Redundancy and failover. Evaluate if the platform includes redundancy and failover capabilities to ensure data availability despite unexpected outages or disasters.
- Incident communication. A well-defined communication plan ensures that stakeholders are informed promptly during an incident, maintaining trust and transparency.