Modern organizations rely on sensitive data to operate, analyze, and innovate. At the same time, that data is accessed by many systems, teams, and partners across its lifecycle. Traditional security controls focus on who can reach a system, but not on who can actually use sensitive data once access is granted.

Encryption, tokenization, and masking are increasingly used to close this gap. They allow organizations to protect sensitive fields at the data layer while still enabling operational workflows, analytics, and AI. In practice, this means sensitive data can be broadly usable, without being broadly visible.

The use cases below reflect how organizations in this industry commonly apply these techniques to reduce risk, meet regulatory requirements, and safely enable data-driven use cases.

Energy, Utilities, and Critical Infrastructure

Energy, utilities, and critical infrastructure operators manage sensitive customer, operational, and identity-linked data that underpins essential services. This data spans billing and customer systems, operational technology environments, grid and network analytics, and regulatory reporting.

The challenge is protecting sensitive data while supporting highly reliable, safety-critical operations and enabling analytics across legacy and modern systems.

Common data environments

Sensitive data in energy, utilities, and critical infrastructure environments typically exists across:

• Customer information and billing systems
• Metering, usage, and telemetry platforms
• Operational technology and control system databases
• Asset management and maintenance systems
• Data warehouses and operational data lakes
• BI, reporting, and regulatory systems
• AI and predictive maintenance platforms
• Third-party contractors, vendors, and regulators

Common use cases

Field-level protection of customer and account data

Utilities encrypt or tokenize sensitive customer fields such as names, service addresses, account identifiers, and billing details directly within customer and billing databases. Protection is applied at the field level so operational systems continue to function normally while sensitive values remain protected at rest and in use.

This reduces exposure from privileged access, system misconfiguration, and data replication without disrupting customer operations.

Identity-based access to cleartext vs masked customer records

Different roles require different visibility into customer and account data. Customer service, billing operations, field operations, and analysts often access the same records for different purposes.

Encryption and masking dynamically return cleartext, partially masked, or fully protected values based on identity and role, ensuring users only see the data required for their function.

Protecting identity-linked operational data

Operational telemetry and asset data is often linked back to customer accounts or service locations. Encryption and tokenization protect identity-linked fields while still allowing operational analytics, outage analysis, and capacity planning.

This enables data-driven operations without broadly exposing customer identities.

Tokenized analytics for usage and demand forecasting

Usage and consumption data is heavily analyzed for demand forecasting, pricing, and capacity planning. Customer and location identifiers are tokenized before ingestion into analytics platforms, allowing aggregation and longitudinal analysis without exposing sensitive identities.

This supports advanced analytics while reducing privacy and compliance risk.

Securing AI and predictive maintenance workflows

AI models are used for outage prediction, asset health monitoring, and maintenance optimization. Encryption and tokenization ensure sensitive customer and location fields remain protected throughout data preparation, model training, and inference.

Cleartext access is restricted to tightly controlled operational workflows, reducing the risk of sensitive data leakage through models or outputs.

Reducing regulatory and compliance exposure

By protecting sensitive fields before they reach downstream systems, utilities reduce the number of platforms subject to privacy and critical infrastructure regulations. Analytics, reporting, and operational tools can operate on protected data without expanding audit scope.

This simplifies compliance while maintaining access to data required for safe and reliable operations.

Limiting insider and contractor access

Energy and utility environments often involve large numbers of internal users and external contractors with system access. Rather than restricting access to systems entirely, organizations restrict access to sensitive values themselves.

Users can perform operational tasks while seeing encrypted, tokenized, or masked data unless explicitly authorized, reducing insider risk without impacting reliability.

Secure data sharing with regulators and partners

Utilities regularly share data with regulators, grid operators, and service partners. Tokenization allows consistent identifiers to be used across shared datasets while preventing unnecessary exposure of underlying sensitive values.

This supports regulatory oversight and operational coordination while maintaining strong data protection controls.

Common high-impact use cases in energy, utilities, and critical infrastructure

The following use cases are especially common in energy, utilities, and critical infrastructure organizations. They arise from the need to operate safety-critical systems at scale while protecting customer identities and identity-linked operational data across legacy and modern platforms.

Identity-linked operational data across grid, meter, and customer systems

Energy and utility providers collect large volumes of operational data from meters, sensors, and infrastructure assets. Much of this data is linked back to specific customers, service addresses, or accounts, creating privacy and security risk when operational and customer systems are combined for analytics and planning.

Organizations address this by encrypting or tokenizing customer and location identifiers before operational data is centralized for analysis. Protected values preserve consistency so usage, outages, and performance can be correlated across systems, while cleartext access to identities is restricted to tightly controlled operational and regulatory workflows.

This enables grid optimization, outage analysis, and planning without broadly exposing customer identities across operational and analytics teams.

Broad operational access without exposing sensitive customer data

Energy and utility environments require large numbers of internal users and contractors to access systems for operations, maintenance, and incident response. These roles require system access but do not require visibility into full customer identities or billing details.

Instead of relying on perimeter access controls alone, organizations protect sensitive fields directly and enforce identity-based access to cleartext values. Most users operate on encrypted, tokenized, or masked data by default, while cleartext access is limited to explicitly authorized workflows.

This reduces insider and contractor risk while preserving the rapid access required for safety-critical operations.

Why traditional approaches fall short

Traditional data protection controls were designed for a different threat model than most organizations face today.

Storage-level encryption does not control data access
Techniques such as database transparent encryption (TDE), full disk encryption (FDE), and cloud server-side encryption (SSE) encrypt data on disk and in backups. They are effective against offline threats like stolen drives or backups. However, these controls automatically decrypt data for any authorized system, application, or user at query time. Once access is granted, there is no ability to restrict who can see sensitive values.

Encryption at rest is not an access control
Storage encryption is enforced by the database engine, operating system, or cloud service, not by user identity or role. As a result, there is no distinction between a legitimate application query and a malicious query executed by an insider or an attacker using stolen credentials. If a query is allowed, the data is returned in cleartext.

Sensitive data is exposed while in use
Modern applications, analytics platforms, and AI systems must load data into memory to operate. Storage-level encryption does not protect data while it is being queried, processed, joined, or analyzed. This is where most real-world data exposure occurs.

Perimeter IAM does not limit data visibility
IAM systems control who can access a system, not what data they can see once inside. After authentication, users and services often receive full visibility into sensitive fields, even when their role only requires partial access. This leads to widespread overexposure of sensitive data across operational, analytics, and support tools.

Static masking breaks analytics and reuse
Static or environment-based masking creates reduced-fidelity copies of data. This often breaks joins, analytics, AI workflows, and operational use cases, forcing teams to choose between security and usability. In practice, masking is frequently bypassed or inconsistently applied.

A false sense of security for modern threats
Most breaches today involve stolen credentials, compromised applications, misconfigurations, or insider misuse. Traditional controls may satisfy compliance requirements, but they do not meaningfully reduce exposure once data is accessed inside trusted systems.

As a result, sensitive data often remains broadly visible inside organizations, even when encryption and access controls are in place.

How organizations typically apply encryption, tokenization, and masking

In energy, utilities, and critical infrastructure environments, encryption, tokenization, and masking are applied at the data layer, close to where sensitive fields are stored and processed. Protection is enforced consistently across customer systems, operational platforms, analytics environments, and AI pipelines.

Access to cleartext or masked values is tied to identity and role rather than embedded in application logic. This allows security teams to enforce policy centrally while operations, engineering, and data teams continue to deliver safe, reliable, and data-driven services.

The result is an environment where sensitive infrastructure data remains usable across operations and analytics, but is only revealed in cleartext when there is a clear, authorized need.

Technical implementation examples

The examples below illustrate how organizations in this industry apply encryption, tokenization, and masking in real production environments. This section is intended for security architects and data platform teams.

Correlating operational telemetry with customer data without exposing identities

Problem
Energy and utility providers analyze meter readings, grid telemetry, and outage data alongside customer and service location data to support planning and incident response. When these datasets are joined, customer identities are often exposed to large operational and analytics teams.

Data in scope
Customer ID, service address, meter identifier, asset reference

Approach
Customer and location identifiers are tokenized at the field level before operational and telemetry data is centralized. Tokens preserve consistency so usage, outages, and performance can be correlated across systems, while cleartext access is restricted to tightly controlled customer service and regulatory workflows.

Result
Enables operational analytics and planning without broadly exposing customer identities.

Broad operational access without visibility into sensitive customer data

Problem
Field operations, maintenance teams, and contractors require access to operational systems to support safety-critical workflows. Traditional access controls grant full data visibility once access is approved.

Data in scope
Customer identifiers, billing references, service location data

Approach
Sensitive fields are protected directly and access to cleartext values is enforced based on identity and role. Most operational users see encrypted, tokenized, or masked values by default, with cleartext access limited to explicitly authorized workflows.

Result
Reduces insider and contractor risk while preserving rapid access for safety-critical operations.

Protecting sensitive data in regulatory reporting and audits

Problem
Utilities regularly generate reports and data extracts for regulators and oversight bodies. These extracts often include sensitive customer and operational identifiers that are copied and retained outside primary systems.

Data in scope
Customer identifiers, account references, regulatory case IDs

Approach
Sensitive fields are tokenized or encrypted before reporting and extract generation. Regulators receive only the minimum data required, while cleartext access remains confined to approved internal workflows.

Result
Supports regulatory compliance and audits without expanding long-term exposure of sensitive customer data.

Securing data across legacy and modern systems during modernization

Problem
Energy and utility providers operate hybrid environments with legacy platforms and modern analytics systems. Data migrations and integrations often increase exposure as sensitive data is replicated across systems.

Data in scope
Customer identifiers, asset references, historical usage data

Approach
Sensitive fields are protected at the source so encrypted or tokenized values persist across migrations and integrations. Cleartext data remains confined to tightly controlled systems.

Result
Reduces exposure during modernization while supporting analytics and operational continuity.