How tokenization benefits enterprises

Tokenization devalues a breach to bad actors while allowing businesses to maintain the usability of their data. Enterprises experience a wide range of both business and technical benefits through tokenization.

• Risk mitigation: A key goal of tokenization is to reduce the amount of sensitive information in an enterprise’s system. If a business experiences a breach, there is much less damage since tokens are useless to bad agents without access to the tokenization vault. Vaultless tokenization additionally reduces the risk of exposing raw data because only randomized tokens move through the business systems.

• Data Governance: Tokenization can help with an effective governance program by reducing an enterprise’s exposure of sensitive data. 

• Business continuity: Tokenized data can still be processed like real data, which allows organizations to maintain workflows and processes throughout business systems without exposing sensitive data.

• Broader data usage: Organizations can give access to data to more people (e.g., interns, analysts, developers, customer support representatives) without revealing the underlying sensitive information. Pseudonymization is especially important for research, analytics, AI and business intelligence, allowing companies to use tokenized data without compromising sensitive information. For example, a market research firm can analyze patient trends for a hospital without exposing patient information.

• Simplified key management: Tokenization eliminates or simplifies the need for complex key management, unlike encryption. While vaultless tokenization requires some key management, it is centralized and not handled at the application or user level, leading to a simpler process. Encryption, in contrast, requires constant encryption and decryption when accessing data, which increases complexity, adds computational load and risks key compromises.

• Data integrity preservation: By preserving the length and format of data, tokenization makes tokenized data ready for immediate implementation across existing systems and applications. Maintaining referential integrity also allows tokenized values to be consistently mapped across databases, leaving data relationships intact.

• Ability to embed metadata: Tokens can store metadata, enabling organizations to retain information about the original data while keeping the data protected. Tokens with metadata improve data usability, for example, allowing for tracking of data origin and usage. The ability to retain information also allows tokens to be easier to index and search, enables tokenized data integration across systems and supports granular access control. Embedded metadata also allows the organization to identify bad tokens and rotate support for outdated tokens.

• Data performance: Encryption adds computational overhead because decryption is necessary for data processing. But tokenized data can be used in its tokenized form across the business, leading to greater efficiency and faster performance.

• Reduced engineering effort: Tokenization allows for replacing sensitive data with tokens without major systems modifications. Format-preserving tokens means there’s no need to alter fields that are necessary at the database and application levels. Complex key management, which can lead to significant engineering overhead, is no longer necessary. 

Tokenization is not a one-step process. There are certain parts of the tokenization journey that are critical to realizing its impactful benefits. The following are best practices for implementing tokenization successfully in enterprises.

One of the first steps for enterprises in a tokenization strategy should be identifying which data needs to be tokenized. Part of the identification is pinpointing the critical data that needs to be protected. Not all data is equally sensitive. Each organization should be able to zero in on two to four critical data types that need protection due to data sensitivity. These may be government-issued identifiers like tax ID numbers or financial data like a credit card PAN. Once identified, enterprises can decide whether tokenization is the right approach for those data types and if they want to fully tokenize the data or partially tokenize the data based on factors such as the necessity for stronger protections, ease of implementation and performance overhead.

Before implementing tokenization, understand where your data resides, how it flows and the potential points of vulnerability. Track your data from the endpoints where it is collected and back upstream to where it is stored and processed. We recommend securing and tokenizing as you go. Identify the highest risk points where tokenization can have the most impact. For example, a business may want to look for a big bulk congestion activity to target because sensitive data is at high risk, such as a batch of health care diagnoses and payments that sit for a period. Another business may want to hone in on a large, centralized database containing personally identifiable information (PII).

A secure tokenization system is required for tokenization to work successfully. To secure the system, you should keep the token server from other enterprise systems through network segregation. The business should make certain only necessary applications and individuals have access to the tokenization system.

Tokenization should be an important component of an enterprise’s data governance to ensure it is applied consistently. A policy framework should define when, where and why tokenization is used. Outlining access control rules is also important, specifying who can tokenize and detokenize data, so only authorized individuals come into contact with sensitive information. Aligning tokenization practices with industry regulations is also necessary for organizations to maintain proper data governance. Clear policies and procedures for applying tokenization will help ensure alignment with the organization’s overall data management practices and data security. 

Security operations, or SecOps, is a proactive, preventive approach to security that integrates security considerations as a shared responsibility across teams (rather than relegating security to a security team responsibility). Tokenization works best when embedded into security operations as an active tool to monitor and improve how data is protected while minimizing potential damage in the case of a breach. Building a culture around tokenization that upholds the importance of data security is crucial to implementing a security operations mindset. Organizations can foster this culture through education on the significance of tokenization and how it strengthens security. Top-down executive buy-in is also important to reinforce the company’s dedication to data security. Lastly, making tokenization seamless within existing workflows and processes will help with adoption.

Rather than a one-time implementation, tokenization is a journey. It’s a continuous process of securing and removing sensitive data that requires monitoring and adapting tokenization as the business grows and the data environments evolve. Enterprises should conduct regular audits to ensure tokenization policies are up to date, monitor new use cases for tokenization and adjust as new security threats emerge.  

An enterprise-wide implementation of tokenization requires a repeatable process that touches all business lines. Businesses should create a structured process that helps ensure data is properly identified and secured. The steps can include 1. Taking an inventory of all data, 2. Scanning to detect sensitive data, 3. Deciding how to treat data (e.g., tokenize, delete), 4. Addressing the sensitive data with the proper security measures.

Encryption and tokenization are not mutually exclusive. Instead of choosing between tokenization and encryption, the best data security approach will usually involve a combination of both. A layered data security approach that employs both and understands the tradeoffs of each technique provides a stronger defense for businesses against threats. For example, enterprises may choose to encrypt data at rest in their databases to defend against unauthorized access and tokenize the data before transmission to reduce exposure in workflows or analytics. Tokenization may also be preferred for high-volume transactions since there is less computational overhead than encryption. Encryption is best for situations that require access to the original data and is preferable for unstructured data like emails and documents. Tokenization is ideal for situations in which the data is structured and maintaining the format and usability are important, such as credit card processing.

Tokenization has become a powerful way for organizations to protect their sensitive information in a data-rich business environment while mitigating risks from data breaches that are all too common. At the same time, the usability and flexibility of tokens allow businesses to balance security with unlocking the value of their data across the organization. As data volumes grow and the risks of data breaches rise, tokenization adds another layer of security so businesses can use data safely and move forward in their analytics and AI initiatives.