K2VIEW TOKENIZATION PLAYBOOK

What is Tokenization?
The Complete Playbook

Protecting sensitive data is a top priority for the enterprise.
Tokenization is one of the best data protection methods –
and a data product approach makes it even better.

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INTRO

Tokenization of Data: A "Must-Have" for the Enterprise

IT teams are involved with tokenization of data because the amount – and cost – of data breaches are rising at an alarming rate.

There were 1,300 publicly reported data breaches in the United States in 2021, up 17% from the 1,100 breaches reported in 2020. A recent IBM report stated that the average cost of a data breach rose 10% year over year, from $3.9 million in 2020 to $4.2 million in 2021. Working from home and digital transformation, due to the COVID-19 pandemic, drove the total cost of a data breach up by another $1 million, on average.

As enterprises make their way through the labyrinth of digital transformation, and as remote work becomes an everyday fixture, the risk and impact of a data breach is steadily rising.

Concurrently, the regulatory environment surrounding data privacy and protection is becoming stricter than ever. Non-compliance with data protection laws could lead to high penalties, legal litigation, and brand damage.

Luckily, today you can protect your sensitive data and prevent data breaches quite easily – and data tokenization may be your best bet.

Chapter 01

Cyberattacks on the Rise

Of all of the things that keep business leaders up at night, the threat of cyberattacks is at the top of the list. According to PwC’s latest CEO survey, almost half of the 4,446 CEOs, surveyed in 89 countries, named cyber risk as their top concern for the next 12 months.

The worry is justified. The scale, cost, and impact of data breaches is rising at an accelerated pace. In 2021 alone, there were more than 1,800 significant data breaches, up almost 70% from 2020. Among the most notable breaches were Facebook, JBS, Kaseya, and the Colonial Pipeline, whose combined effect was to expose the private information of hundreds of millions of people and, in some cases, brought business operations to a grinding halt.

With data privacy as a top priority for enterprises, enterprises must find ways to protect sensitive data, while granting data consumers authorization to access the data they need to execute operational and analytical workloads. Today, data tokenization is heralded as one of the most secure and cost-effective ways to protect sensitive data.

In this article, we’ll answer the question, “What is tokenization?”, explain how tokenization supports compliance with data privacy laws, and examine the advantages of taking a data product approach to tokenization.

Chapter 02

What is Tokenization?

Like data masking, tokenization is a method of data obfuscation, which involves obscuring the meaning of sensitive data in order to render it useless to potential cyber-attackers.

Unlike data masking tools, tokenization involves irreversibly substituting sensitive data with a non-sensitive equivalent, or “token”, for use in databases or internal systems. The token is a reference that maps back to the original sensitive data through a tokenization system.

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With tokenization, data consumers can freely access data, without risk.

A token consists of a randomized data string that doesn’t have any exploitable value or meaning. Although tokens themselves don’t have any value, they retain certain elements of the original data – such as format or length – to support seamless business operations. Identity and financial information are commonly tokenized, such as Social Security Numbers, passport numbers, bank account numbers, and credit card numbers.

Chapter 03

The Need for Tokenization

Tokenization allows enterprises to protect sensitive data while maintaining its full business utility. Since the process of tokenization doesn’t modify the data, tokenized data can be used by data consumers within the organization without interruption or risk.

Tokenization is essential for today’s businesses, because the majority of data breaches are the result of insider attacks, whether intentional or accidental. Consider these enterprise statistics:

  • 50% believe that detecting insider attacks has become more difficult since migrating to the cloud

  • 60% think that privileged IT users pose the biggest insider security risk to organizations

  • 70% feel vulnerable to insider threats, and confirm greater frequency of attacks

What Data Needs to be Tokenized?

When we talk about sensitive data, we're specifically referring to Personal Health Information (PHI) and Personally Identifying Information (PII) .

Medical records, drug prescriptions, bank accounts, credit card numbers, driver’s license numbers, social security numbers, and more, fall into this category.

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The COVID-19 pandemic highlighted the need to tokenize personal vaccination data.

Companies in the healthcare and financial services industries are currently the biggest users of data tokenization. However, businesses spanning all sectors are starting to appreciate the value of this alternative to data masking. As data privacy regulations become more stringent, and as the punishments for noncompliance become more commonplace, prudent organizations are actively looking for advanced solutions to data protection, that can also maintain full business utility.

Chapter 04

To Tokenize or Not to Tokenize:
That is the Question

If you’re ready to implement a data tokenization solution, consider these 4 questions before you start evaluating solutions.

  1. What are your primary business requirements?
    The most significant consideration—and the one to start with—is defining the business problem that needs to be resolved. After all, the ROI and overall success of a solution is based on its ability to fulfill the business need for which it was purchased. The most common needs are improving cybersecurity posture and making it easier to comply with data privacy regulations, such as Payment Card Industry Data Security Standard (PCI DSS). Vendors vary in their offerings for other types of PII, such as Social Security numbers or medical data.

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    Maintaining medical privacy is just important
    as
    protecting personal identity or financial information

  2. Where is your sensitive data?
    The next step is identifying which systems, platforms, apps, and databases store sensitive data that should be replaced with tokens. It’s also important to understand how this data flows, as data in transit is also vulnerable.

  3. What are your system/token requirements?
    What are the specific requirements for integrating a data tokenization solution to your database and apps? Consider what type of database you use, what language your apps are written in, the degree of distribution of apps and data centers, and how you authenticate users. From there, you can determine whether single-use or multi-use tokens are necessary, and if tokens can be formatted to meet required business use.

  4. Should you custom-build a solution in-house or purchase a commercial product?
    After you know your business needs and requirements for apps, integrations, and tokens, you’ll have a clearer understanding of which vendors can offer you value. 

    Although organizations can, at times, build their own data tokenization solutions internally, tackling this need in-house usually adds greater strain to data engineers and data scientists, who are already stretched thin. Most of the time, the benefits of purchasing a customizable solution, receiving tailored customer support, and alleviating data teams of this task outweighs the costs.

Chapter 05

Tokenization vs Encryption

Encryption is another common method of data obfuscation. During the encryption process, sensitive data is mathematically changed. However, the original pattern remains within the new code, which means it can be decrypted with the appropriate key. To access the key, hackers use a variety of techniques, such as social engineering or brute computing force.

The ability to reverse encrypted data is its biggest weakness. The level of protection of the sensitive data is determined by the complexity of the algorithm used to encrypt it. However, all encrypted data can ultimately be reversed and broken.

Tokenization cannot be reversed. Instead of using a breakable algorithm to protect sensitive data, tokenization permanently substitutes the data with a random, meaningless placeholder.

In the case of tokenization, the original data is stored separately, sometimes evenoutside of your IT environment. Therefore, if a hacker penetrates your IT systems and accesses your tokens, they still cannot access your original data.

Chapter 06

4 Top Benefits of Tokenization

Tokenizing sensitive data offers several key benefits, such as:

  • Reduced risk
    Tokens are irreversible, so even in the case of a breach, personal information is never compromised, and financial fallout is avoidable.

  • Reduced encryption cost
    Only the data within the tokenization system needs encryption, eliminating the need to encrypt all other databases.

  • Reduced privacy efforts
    Data tokenization minimizes the number of systems that manage sensitive data, reducing the effort required for privacy compliance.

  • Business continuity
    Tokens can be “format preserving” to ensure that existing systems continue to function normally.

Chapter 07

Data Tokenization Use Cases

The following are 6 key use cases for data tokenization:

  1. Compliance scope reduction

    With data tokenization, you can reduce the scope of data compliance requirements because tokens replace data irreversibly. For instance, exchanging a Primary Account Number (PAN) with tokenized data results in a smaller data footprint, thus simplifying PCI DSS compliance.

  2. Data access management

    Tokenization gives you more control over data access management, preventing those without appropriate permissions, from de-tokenizing sensitive data. For instance, when data is stored in a data lake or warehouse, tokenization assures that only authorized data consumers have access to sensitive data.

  3. Supply chain security

    Today, many enterprises work with third-party vendors, software, and service providers requiring access to sensitive data. Tokenization minimizes the risk of a data breach originating externally by keeping sensitive data away from such environments.

  4. Data lake/warehouse compliance

    Big data repositories, such as data lakes and data warehouses, persist data in structured and unstructured formats. From a compliance perspective, this flexibility makes data protection controls more challenging. When sensitive data is ingested into a data lake, tokenization hides the original PII, thus reducing compliance issues.

  5. Business analytics

    Analytical workloads, such as Business Intelligence (BI), are common to every business domain, meaning that the need to perform analytics on sensitive data may arise. When the data is tokenized, you can permit other applications and processes to run analytics, knowing that it's fully protected.

  6. Avoid security breaches

    Data tokenization enhances cybersecurity by protecting sensitive data from would-be attackers (including insiders, who were responsible for 60% of the total number of  data breaches in 2020). By exchanging PII data with non-exploitable, randomized elements, you minimize risk, while also maintaining its business utility.





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Data tokenization safeguards personally identifiable information during analytics.

Chapter 08

Conventional Tokenization has its Risks

Despite its many benefits, tokenization also has its limitations. The most significant is the risk associated with storing original sensitive data in one centralized token vault. Storing all customer data in one place leads to:

  • Bottlenecks when scaling up data

  • Risks of a mass breach

  • Difficulties ensuring referential and format integrity of tokens across systems

The way to reap the benefits of tokenization, while avoiding its risks, is by adopting a decentralized system, such as a data mesh.

Chapter 09

Better Compliance through Tokenization

In addition to actually protecting sensitive data from being exposed, tokenization also helps enterprises comply with an increasingly stringent regulatory environment. One of the most pertinent standards that tokenization supports is the Payment Card Industry Data Security Standard (PCI DSS).

Any business that accepts, transmits, or stores credit card information is required to be PCI-compliant, and adhere to PCI DSS to protect against fraud and data breaches. Failure to comply with this standard could lead to devastating financial consequences, such as huge penalties and fines, plus additional costs for legal fees, settlements, and judgements. It can also lead to irrevocable brand damage or even going out of business.

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Tokenization supports PCI DSS, and reduces the amount of stored PAN data.

Tokenization supports PCI DSS compliance by reducing the amount of PAN (Primary Account Number) data stored in an enterprise’s databases. Instead of persisting this sensitive data, the organization need only work with tokens. With a smaller data footprint, businesses have fewer requirements to comply with, reducing compliance risk, and speeding up audits.

Chapter 10

A Better Approach to Tokenization
with Data Products

A data product approach eliminates the need for a single centralized vault. A data product delivers a “ready-to-use,” complete set of data on a specific business entity (such as a customer, credit card, store, payment, or claim) used for both operational and analytical workloads.

59-3-wpData products can tokenize data in real time, or in batch mode.

By organizing and securing the data that is integrated by a data product for a specific business entity in its own, individually-encrypted “micro-database” – as opposed to storing all sensitive data in one location – you can distribute and therefore significantly reduce risk of a breach.

In this way, rather than having one vault for all your customer data (let’s say, 5 million customers), you will have 5 million vaults, one for each individual customer!

When every instance of a data product manages its data in its own individually encrypted and tokenized micro-database, the result is maximum security of all sensitive data.

In the context of data tokenization, data products:

  • Ingest fresh data from source systems continually

  • Identify, unify, and transform data into a self-contained micro-database, without impacting underlying systems

  • Tokenize data in real time, or in batch mode

  • Secure each micro-database with its own encryption key and access controls

  • Preserve format and maintains data consistency based on hashed values

  • Provide tokenization and detokenization APIs

  • Provision tokens in milliseconds

  • Ensure ACID (Atomicity, Consistency, Isolation, Durability) compliance for token management


A data product that manages its data by business entities, where the data for each entity is managed in its own secure vault, delivers the best of all worlds: enhanced protection of sensitive data, total compliance with customer data privacy regulations, and optimal functionality from a business perspective.

Maximize,
whenever you tokenize,
with K2View.