What is data in use encryption?

Data in use encryption refers to encrypting data while it is actively being used and processed. This differs from data at rest encryption, which encrypts data while it is stored, and data in motion encryption, which encrypts data while it is being transmitted. The goal of data in use encryption is to protect sensitive information from unauthorized access or modification while it is being used in memory.

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Why is data in use encryption important?

With the rise in data breaches and cyber attacks, organizations need to protect sensitive data at all times – not just while it is at rest or in transit. Data in use is often seen as the weak link, as traditional security controls like firewalls don’t protect data that is already inside an organization’s systems. Some key reasons why data in use encryption is critical:

Prevents privileged insider attacks: Many damaging data breaches are caused by insider threats with privileged system access. Encrypting data while in use protects against malicious activities by privileged users.

Compliance with regulations: Regulations like HIPAA, PCI DSS, and GDPR are increasingly mandating protection of sensitive data in use in addition to at rest and in transit.
Reduces risks from memory scraping malware: Malware designed to harvest sensitive data from memory can be thwarted by encrypting data in active memory.
Enables secure processing in the cloud: As more data processing shifts to the cloud, data in use encryption allows organizations to securely leverage cloud resources.

In summary, data in use encryption is critical for a robust data security strategy in today’s threat landscape. It protects against both external attacks and insider threats, while also enabling compliance and secure use of cloud resources.

When does data need to be encrypted in use?

Here are some of the most common scenarios where sensitive data should be encrypted while in active use:

Processing financial information: Anytime financial data like credit card numbers, bank account details, or financial transactions are being processed in memory, encryption should be applied.

Handling healthcare data: Protected health information (PHI) should remain encrypted while being processed to comply with HIPAA.
Storing passwords or encryption keys in memory: This sensitive credential data should always be encrypted in memory to prevent theft.
Using live production databases: Real-time encryption of sensitive fields in production databases helps secure data from privileged insiders.

Analyzing confidential corporate data: Encryption preserves need-to-know access controls during data analytics workloads.
Developing applications with sensitive data: Encrypting live data used in development protects against theft of test data sets.

In general, any time sensitive regulated data like financial information, healthcare data, intellectual property, or personal data is being actively processed in a server’s memory, the data should be protected with encryption. This protects against both external attacks and insider threats.

What are the primary methods of data in use encryption?

There are two core technical approaches to encrypting data while in use:

Application-layer encryption

With application-layer encryption, developers modify applications to encrypt sensitive data within the application environment before use and decrypt it when needed for processing. This provides fine-grained control for developers to encrypt specific data objects, variables, or functions.

Advantages:

Very granular control over what data to encrypt.
Can leverage encryption APIs/libraries for easier implementation.
No changes needed to underlying infrastructure.

Disadvantages:

Requires modifying application source code, so existing apps require refactoring.
Encryption logic must be added to every app that needs security.

Can impact application performance if not well optimized.

Hardware-enhanced encryption

Hardware enhanced encryption uses dedicated hardware capabilities integrated into CPUs/chips to encrypt data within the processor environment rather than in software. This happens transparently from an application perspective.

Advantages:

Encrypts all data in memory without application changes.
Minimal performance impact due to hardware acceleration.
Consistency across applications and environments.

Disadvantages:

Requires specialized hardware/CPUs with encryption capabilities.
No ability to fine tune which data gets encrypted.

Upfront investment to upgrade hardware.

The optimal approach depends on use case – application-layer works well for selectively encrypting fields in an existing application, while hardware encryption provides across-the-board protection transparently.

How does data in use encryption work at a high level?

Here are the core steps that take place behind the scenes with data in use encryption:

A cryptographic key is generated to be used for encrypting data.
When an application needs to use sensitive data fields, those fields are encrypted using the encryption key.
The encrypted data sits in the server’s active memory in its secure encrypted form while being used and processed.

When the application needs to access the original plain text data, the encrypted data is decrypted on-the-fly using the encryption key.
Once processed, the data is encrypted again before transitioning back into memory.
This cycle repeats for the duration the data needs to be in active use by the application.

The encryption keys are also usually stored in protected memory regions inaccessible to external threats. This prevents an attacker from gaining access to the keys and using them to decrypt the sensitive information.

On the hardware side, the processor leverages capabilities like secure enclaves and instruction sets for efficient encryption/decryption of data in the CPU’s memory space between execution cycles.

What are the requirements for implementing data in use encryption?

To implement data in use encryption effectively, organizations need to ensure these core requirements are met:

Select the right encryption algorithms: The encryption cipher used should follow current best practices – typically AES 256-bit is recommended for symmetric encryption.
Securely manage encryption keys: Keys should be protected from unauthorized access and rotated periodically based on security policy.
Integrate encryption into data workflows: The solution needs to encrypt data seamlessly as it’s used while minimizing performance impact on applications and users.

Validate correct implementation: Rigorously test solutions to confirm sensitive data is fully encrypted as expected without gaps.
Establish sound key management: Policies and procedures for key generation, storage, access control, and rotation should be instituted.
Monitor for misconfigurations: Continuously monitor for any lapses in configuration that could accidentally expose data.

With the right solutions and processes in place, data in use encryption can be deployed successfully without disrupting operations or workflow for users and administrators.

What are the potential performance impacts of encrypting data in use?

Encrypting data in use can introduce additional performance overhead, but the impacts can be minimized through proper design and planning. Here are some considerations around performance:

There will naturally be some computational load added by encrypting/decrypting data on the fly during processing.

Hardware encryption optimizations like AES-NI instruction sets on modern CPUs help accelerate encryption with integrated silicon.
Strategically encrypt only the most sensitive fields rather than all data to optimize performance.
Application-layer solutions should leverage efficient native encryption libraries instead of custom routines.

Benchmark solutions using realistic data workloads to quantify performance impact before deployment.
Additional memory may be consumed by the encrypted data footprint – encrypted data can be larger than plaintext.
Tuning, load balancing, and scaling up capacity can help overcome hardware resource constraints.

With a well-tuned implementation, performance overheads for data in use encryption typically remain in the single digit percentage – a small price to pay for significantly enhanced security.

What are some best practices for encryption key management?

Proper management of the encryption keys used to secure data in use is essential. Some key best practices include:

Leverage a hardened, access controlled key management system – Avoid manual key handling processes.

Generate keys using secure cryptographic algorithms with sufficient entropy.
Rotate encryption keys periodically based on organizational policies to limit potential compromise.
Store keys securely in hardware security modules or cloud-based key stores.

Enforce access controls and auditing for all key management operations.
Document detailed procedures for key rollover, emergency access, and other scenarios.
Separate keys used for test vs production environments.

Monitor keys for any unauthorized actions like copying or unusual usage patterns.
Backup keys securely to enable decryption of archived data as needed.

Leveraging a dedicated key management platform provides automation, access control, auditing, and disaster recovery capabilities for keys used in data in use encryption.

How can data in use encryption be implemented cost-effectively?

There are a number of approaches to help minimize costs when deploying data in use encryption:

Prioritize high risk data – Encrypt the most sensitive data first where breaches would have the greatest impact.
Leverage native CPU instruction sets like AES-NI for low overhead hardware encryption support.

Use open source software libraries where possible instead of expensive proprietary tools.
Optimize performance to minimize hardware growth requirements.
Utilize cloud infrastructure for scalability and shifting from capital to operating expense.

Phase in encryption controls over time on a roadmap to spread out budget.
Automate key management processes as much as possible to reduce administrative costs.
Consolidate multiple security tools into integrated suites to reduce licensing costs.

Evaluate data in use encryption included in some database vendor solutions.

The costs associated with a breach far outweigh the investment to implement robust data in use encryption. Organizations can balance protection for high risk data while optimizing expenses.

What are some common misconceptions around data in use encryption?

Some frequent misconceptions include:

Misconception: Data at rest and in transit encryption are sufficient to protect data.
Reality: Data in use also needs to be secured as it creates vulnerabilities from insider threats with system access.
Misconception: It requires too many application changes to encrypt data in use.
Reality: Modern solutions allow transparent data encryption without changing application code.
Misconception: The performance overhead makes it impractical.
Reality: Hardware and software optimizations now make overheads manageable for most workloads.

Misconception: It’s only necessary for public cloud environments.
Reality: Data in use encryption is critical regardless of infrastructure or location.
Misconception: Only regulated data like healthcare requires this protection.
Reality: Any sensitive data like IP, trade secrets, and credentials need encryption in use.

It’s important to understand data in use encryption’s benefits and dispel outdated notions about performance or implementation challenges. When properly implemented, it is an essential data protection capability.

Conclusion

Protecting the security of sensitive data is a constant challenge for modern organizations. While at rest and in transit protections are more mature, safeguarding data in active use remains critically important. By encrypting data seamlessly while being processed in memory, data in use encryption provides defense against both external attacks and insider threats. With modern solutions minimizing performance impact and management overhead, organizations should strongly consider deploying data in use encryption as part of a comprehensive data security strategy.