Organizations across many industries store sensitive structured data that must remain protected for decades. Examples include customer identity records, financial transactions, healthcare histories, subscriber data, and government records.

While encryption at rest protects infrastructure today, many widely used public-key algorithms such as RSA and ECC are expected to become vulnerable once sufficiently capable quantum computers emerge.

This creates a long-term risk commonly described as “harvest now, decrypt later.” Adversaries may capture encrypted data today with the expectation that future quantum capabilities could allow that data to be decrypted years or decades later.

For organizations responsible for protecting long-lived data, preparing systems for post-quantum cryptography (PQC) and maintaining cryptographic agility has become an important part of long-term security planning.

Ubiq helps organizations protect sensitive structured data in a a way that allows cryptographic methods to evolve over time without requiring major application or infrastructure changes.

Common environments where long-lived data exists

Sensitive datasets that must remain confidential for many years typically exist across a wide range of operational and analytical environments.

Examples include:

• operational databases storing customer or subscriber records
• data warehouses used for analytics and reporting
• data lakes storing historical datasets
• AI and machine learning pipelines that train on historical data
• applications and APIs that process sensitive records
• backup and archival storage systems

Because these environments are interconnected, sensitive data often moves between systems many times over its lifecycle.

Preparing these environments for PQC requires protection models that can evolve as cryptographic standards change.

Common use cases

Protecting long-lived regulated datasets

Many organizations must retain sensitive records for extended periods of time due to regulatory or operational requirements.

Examples include:

• financial transaction histories
• patient medical records
• insurance claims data
• subscriber and device histories
• government identity records

These datasets may remain accessible for decades and are often replicated across multiple systems for analytics, reporting, and operational purposes.

Sensitive fields can be encrypted or tokenized before entering databases, analytics platforms, or archival systems. This allows organizations to maintain strong protection today while preserving the ability to update cryptographic methods as PQC standards evolve.

Crypto-agile protection for operational databases

Operational systems frequently store the most sensitive data within an organization, including identifiers such as account numbers, national IDs, device identifiers, or customer records.

Because many applications depend on these databases, upgrading cryptographic algorithms directly within the database layer can require complex application changes.

Protecting sensitive fields at the data layer allows organizations to maintain application compatibility while introducing new cryptographic algorithms or protection models over time.

This enables long-lived datasets to remain protected even as encryption standards evolve.

PQC-ready analytics and data warehouse environments

Sensitive operational data is commonly replicated into analytics platforms such as data warehouses and data lakes.

These environments may contain years or decades of historical records used for reporting, fraud detection, forecasting, and operational analysis.

Protecting sensitive identifiers before they enter analytics platforms allows organizations to analyze data while maintaining strong protection for sensitive fields.

As PQC standards mature, protected data can be updated or re-protected without requiring major changes to the analytics environment.

Protecting historical datasets used for AI and machine learning

Many organizations are beginning to use historical enterprise datasets to train machine learning models and power AI workflows.

These datasets may include customer records, financial histories, healthcare information, or subscriber activity logs.

Because AI models often rely on large volumes of historical data, protecting sensitive fields before they enter training pipelines helps reduce long-term exposure.

This approach allows organizations to support AI initiatives while maintaining control over how sensitive data is protected and accessed over time.

Why traditional approaches fall short

Most organizations rely on encryption capabilities provided by infrastructure platforms such as cloud storage, databases, or disk encryption.

While these controls are important, they typically protect infrastructure rather than individual data fields.

Sensitive structured data often moves between multiple systems including applications, databases, analytics platforms, and AI pipelines. As data moves between systems, it may be decrypted for processing or replicated into new environments.

This makes it difficult to update cryptographic protection consistently across the entire data lifecycle.

Large-scale cryptographic upgrades may require:

• re-encrypting large datasets
• modifying application logic
• rebuilding analytics pipelines
• coordinating changes across multiple teams and systems

Because structured data often sits at the center of operational systems, these projects can become complex and disruptive.

What this enables

Organizations preparing for post-quantum cryptography can gain several advantages from protecting sensitive data at the data layer.

Sensitive records can remain protected even as cryptographic standards evolve.

Cryptographic algorithms can be updated over time without requiring major infrastructure or application changes.

Sensitive fields remain protected across operational databases, analytics platforms, and AI systems.

Security teams can maintain consistent policy control over how protected data is accessed and used.

This approach allows organizations to begin preparing for PQC while maintaining compatibility with existing systems and workflows.

Example implementations

Example: Preparing a financial analytics environment for PQC

Problem

A financial institution maintains decades of transaction history in its analytics environment. These records contain sensitive identifiers such as account numbers and customer identities.

Data in scope

• customer identifiers
• account numbers
• transaction records

Approach

Sensitive identifiers are encrypted or tokenized before entering the analytics platform. Analytics workloads can process historical datasets while sensitive fields remain protected.

Result

The organization can maintain long-term protection for regulated data while preserving the ability to evolve cryptographic methods as PQC standards mature.

Example: Protecting subscriber datasets in telecommunications platforms

Problem

A telecommunications provider stores large volumes of subscriber and device history across operational systems and analytics platforms.

Data in scope

• subscriber identifiers
• IMEI and device identifiers
• billing and usage records

Approach

Sensitive identifiers are protected before entering downstream databases and analytics platforms.

Result

Subscriber data remains protected across environments, while cryptographic protection can evolve as new standards and PQC algorithms become available.