Secure Data Stack: Navigating Adoption Challenges of Data Encryption

Encryption is one of the most effective data security strategies, alongside access control, software updates, and network segmentation. However, adding encryption to an existing tech stack can be challenging. Data pipelines aren’t typically designed to handle encryption and decryption, leading to additional migration work, operational complexity, and higher compute costs.

Data encryption presents its own challenges in modern times. While at-rest and in-transit encryption are common, they often fall short of preventing data breaches within the data store itself. Unlike typical encryption in TLS, where AES and RSA are effective for securing data during transmission, applying these methods to long-term data storage introduces unique challenges.

Currently, there is no clear strategy for maintaining long-lived encryption keys. Managing keys and performing key rotations can be error-prone and costly. The high computational cost of processing encrypted data further complicates maintaining secure encryption over time. This highlights the need for data-specific security strategies that go beyond just encryption in-transit or at-rest, ensuring robust protection throughout the entire data lifecycle.

Challenges of Better Data Security

Technical Complexity

Data pipelines are often built without the flexibility to handle encryption and decryption processes. For example, a pipeline designed to compute feature values from event data may need to be re-engineered to include encryption and decryption layers, adding cost and complexity to the implementation. Adding data encryption can also require updates to APIs, databases, and storage systems to support encrypted data formats. This involves updating the codebase, rewriting queries to handle encrypted fields, and ensuring that encryption keys are properly managed and accessible to services and compute environments.

Performance

Encrypting and decrypting data in real-time introduces higher latency. In high-throughput use cases, as large volumes of data are processed, the extra time needed to encrypt or decrypt each piece can slow down data processing. Encryption and decryption involve mathematical operations that require additional computational resources. When handling large datasets, this can be both costly and time-consuming, potentially preventing organizations from adopting encryption, even though it's a better data security practice.

Key Management

Currently, there’s no unified key management system specifically for data encryption. Common use cases like TLS are designed for short-lived keys, and long-lived key management lacks standardized solutions. Like any security component, visibility into key management is important — understanding when and how a key was created, when and where it has been accessed, and how long it’s been in use before being rotated. Without this visibility, maintaining secure and effective data encryption practices can be risky and error-prone. The complexity increases when different teams within a company, or across partner organizations, need to securely share or manage keys. Gaps in key management practices can lead to security breaches, misconfigurations, and unauthorized access.

Cost

Adding encryption to an existing tech stack can involve non-trivial development work. The additional encryption and decryption processes require more computational resources, which can lead to higher operational and cloud infrastructure costs. The financial impact of a breach can be enormous, including fines for non-compliance with regulations, legal fees, and the costs associated with incident response and remediation. Like many security practices, the cost of data encryption must be balanced against the potential costs of a data breach (e.g., fines for non-compliance with regulations, legal fees, incident response and remediation expenses, and the reputational damage to the brand).

Expertise Gaps

Existing data security practices are increasingly insufficient to defend the growing frequency and scale of data breaches. As the technology involve, data and software engineers will also need to catch up on the corresponding skills and knowledge to implement data security effectively. This requires a commitment to continuous learning and adaptation by the companies and organizations.

Data Security Strategy: A Gradual Approach

Technology is ever-evolving, and so too is software implementation. A complete rewrite is not only costly but can also be detrimental to businesses. Instead, a less risky but more common approach is to identify high-impact, low-interruption service areas to roll out the change. By starting small, organizations can try out new technologies and processes, minimizing risks and disruptions to the business while gradually scaling up.

High Impact Areas

Field-Level Encryption

Field-level encryption targets specific sensitive information within a data object. Whole message or full file encryption, by contrast, encrypts entire data objects in one shot, making the entire content inaccessible without the proper decryption key. 

For real-time or data processing pipelines, field-level encryption allows the system to continue operating, enabling engineering teams to gradually update dependencies or downstream steps. In complex systems, a hard, full cutover can be challenging and risky. Field-level encryption lowers the adoption barrier by avoiding the need for an immediate, full migration, thus allowing for a more manageable and phased approach.

One advantage of starting with field-level encryption is that it opens the door to techniques such as data masking, redaction, deterministic encryption, and privacy-preserving encryption. These solutions can be implemented based on the specific security needs and the existing tech stack.

Whole File Encryption in Data Security Vulnerability Remediation

While field-level encryption is useful in more complex real-time systems and data pipelines, whole file encryption can be very effective for data security vulnerability remediation. Encrypting files containing sensitive information make it much easier for security teams to handle vulnerabilities on their own. Instead of having to consult with engineering or legal teams on whether potentially exposed data can be deleted, security teams can easily encrypt log files or data archives with sensitive information. This makes the remediation easier and also ensures that sensitive information is not only protected, reducing the risk of unauthorized access, but also recoverable when needed.

Summary

While data encryption is effective for protecting sensitive information, it can be costly to implement. Taking a gradual approach, starting with high-impact areas like field-level and whole file encryption, makes adoption more manageable. As technology and security threats evolve, adopting new security strategies is essential. With the right migration strategy, you can keep your tech stack up to date without excessive risks of disrupting your day-to-day business operations.