Organizations collect and store vast amounts of personal, financial, and confidential information, making them vulnerable to data breaches and cyberattacks. Data tokenization is a technique that has gained significant attention as a way to enhance data security and privacy.
Similar to data masking, data tokenization serves as a technique for anonymizing data by obscuring sensitive information, making it unusable for potential attackers.
In contrast to data masking tools, data tokenization involves the substitution of sensitive data with a non-sensitive counterpart, referred to as a “token,” within databases or internal systems. These tokens are references that can be mapped back to the original sensitive data using a tokenization system. While tokens themselves lack intrinsic worth, they retain specific attributes of the original data, such as its format or length, to ensure smooth business operations.
Commonly tokenized data includes PII and financial information like Social Security Numbers, passport numbers, bank account details, and credit card numbers. The data tokenization process, categorized as a form of “pseudonymization,” is intentionally designed to be reversible.
Data tokenization, a process that involves substituting sensitive data with unique tokens, has found applications across industries, ranging from financial transactions and healthcare to cloud computing and beyond. Organizations are engaging in data tokenization due to the alarming increasein both the volume and expenses associated with data breaches.
By delving into the statistics surrounding data breaches, we can uncover the need for enhanced security, ensuring compliance, and fostering trust in an era where data privacy reigns supreme.
The primary adopters of data tokenization are companies operating within the healthcare and financial services sectors. Nevertheless, enterprises across diverse industries are increasingly recognizing the benefits of this substitute for data masking. As data privacy regulations continue to tighten and penalties for noncompliance become more frequent, forward-thinking organizations are actively seeking advanced data protection solutions that simultaneously preserve the full functionality of their business operations.
Several factors are poised to accelerate the effectiveness and widespread adoption of data tokenization.
The exponential growth in data collection, particularly personal data, is driving the need for effective data tokenization. As organizations gather more information about individuals, there’s a heightened focus on protecting this sensitive data from unauthorized access or breaches.
The constant rise in cyber threats and data breaches underscores the urgency of adopting robust data protection measures like tokenization. Businesses are acutely aware of the risks posed by cyberattacks and are seeking proactive solutions to safeguard their data.
The proliferation of privacy and dataprotection regulations, such as GDPR and CCPA, compels organizationsto implement data tokenization to comply with these laws. Tokenization provides a secure way to handle data while adhering to the strict requirements of these regulations.
The growing need to share data across organizations and collaborate with partners requires secure data handling. Data tokenization enables businesses to share information safely while preserving its integrity, ensuring that only authorized parties can access and utilize it.
The increasing adoption of cloud computing services necessitates enhanced data security. Data tokenization plays a crucial role in securing data stored and processed in the cloud, assuring businesses that their information remains protected even in remote environments.
The surge in ecommerce activities and contactless payment methods has generated a wealth of sensitive financial data. Data tokenization is pivotal in safeguarding this information, making online transactions more secure and reducing the risk of financial fraud.
The prevalence of data breachesin recent years serves as a stark reminder of the importance of data security. The rising number of breaches is compelling organizations to invest in technologies like data tokenization to fortify their defenses against such incidents.
A token is a random, non-sensitive placeholder that is used to represent sensitive data within an organization’s systems. Tokens are generated using encryption or secure algorithms and are devoid of any inherent meaning or value.
This is the software or infrastructure responsible for generating tokens, managing token mappings, and handling the tokenization and de-tokenization processes. It needs to be robust, secure, and properly managed to ensure the integrity of the tokenized data.
A token mapping is a secure database or table that links each generated token to its corresponding sensitive data. It’s essential for the organization to maintain this mapping accurately and securely to retrieve the original data when necessary.
This is the process of reversing tokenization by using the token mapping to retrieve the original sensitive data from a token.
A set of security standards designed to ensure the security of credit card transactions and the protection of cardholder data. Data tokenization is commonly used to help organizations achieve PCI DSS compliance.
Information that can be usedto identify an individual, such as name, social security number, address, etc. Tokenization helps protect PII from unauthorized access.
The unauthorized access, acquisition, or exposure of sensitive data. Data tokenization can mitigate the impact of data breaches by ensuring that stolen tokens are of no value without the corresponding mapping.
Format-Preserving Tokenization (FPT) is a technique used to tokenize sensitive data while preserving its original format, length, and structure. This is particularly useful when you need to replace sensitive data (like credit card numbers, social security numbers) with tokens, but you want the tokens to resemble the original data so that they can still be used within the same context. FPT maintains the pattern of the original data, making it suitable for systems that expect certain formats.
For example, if you have a 16-digit credit card number “1234-5678-9012-3456”, format-preserving tokenization might transform it into something like “4321-8765-2109-6543”. This way, the token still has the same structure andformat as a credit card number, but it’s not the actual sensitive data.
Secure Hash Tokenization involves applying a cryptographic hash function tosensitive data to produce a fixed-length string of characters, known as a hash value or token. Hash functions, like SHA-256 or SHA-512, generate a unique hash value for each unique input, making it nearly impossible to reverse-engineer the original data from the hash.
The key advantage of secure hash tokenization is that it’s irreversible. Given a hash value, you can’t determine the original data without using a precomputed table of hashes (rainbow tables) or attempting a brute-force attack, which is computationally expensive and time-consuming. Secure hash tokenization is commonly used for storing passwords securely, where the system only stores the hash of the password, not the password itself.
Randomized Tokenization involves replacing sensitive data with random tokens that have no direct relation to the original data. Unlike format-preserving tokenization, randomized tokenization doesn’t retain the format or structure of the original data. This can enhance security since there’s no discernible pattern between the original data and the token.
For example, the sensitive data “John Doe” could be replaced with a random token like “xRt9K2sL”, which bears no obvious connection to the original name. Randomized tokenization is commonly used when the data format is not important, and the primary focus is on ensuring that the tokenized data is as unpredictable as possible.
In summary, these three types of data tokenization provide varying levelsof security and functionality:
This initial step involves identifying and categorizing sensitive data within an organization’s systems. Sensitive data can include personal identifiable information (PII), credit card numbers, health records, and other confidential information. Properly classifying the data helps determine which information needs to be tokenized to ensure security compliance.
During this phase, organizations perform a thorough analysis of their data storage systems, databases, and applications to identify the types of sensitive data they store. They create a data inventory that catalogs the types of data, the systems they’re stored in, and their levels of sensitivity. Data discovery tools and data loss prevention (DLP) software can aid in this process.
Once the sensitive data is identified, the tokenization process involves generating unique tokens to replace the original sensitive information. Tokens are random and have no inherent meaning. Token generation employs cryptographic algorithms to ensure that the tokens are unpredictable and practically impossible to reverse-engineer to obtain the original data.
The token generation process typically involves the following steps:
For example, if a credit card number “1234 5678 9012 3456” is tokenized, it might be replaced with a token like “A1B2C3D4E5F6G7H8.” This token carries no information about the actual credit card number it represents.
After generating tokens for sensitive data, the tokens need to be stored securely in a privacy vault, while maintaining a proper mapping between the tokens and the original data. This mapping is essential for later processes where the organization needs to retrieve the original data based on tokens, such as when processing transactions or providing authorized access.
The tokenization system maintains a secure database or mapping mechanism that connects the tokens to the original data without exposing the actual sensitive information. Security measures, like access controls, are applied to the token mapping to prevent unauthorized access.
In case the original data needs to be accessed for authorized purposes, the tokenization system can retrieve the original data by looking up the mappingassociated with a specific token.
Tokenization enhances data privacy by reducing the exposure of sensitive information. When sensitive data like credit card numbers, social security numbers, or personal identification details are tokenized, the actual data is no longer present in systems that handle transactions or analytics. Instead, only the token is used for processing, reducing the risk of unauthorized access to sensitive information. Even if an attacker gains access to the tokens, they would be meaningless without access to the original data.
Compliance with regulations such as the Payment Card Industry Data Security Standard (PCI DSS) and the General Data Protection Regulation (GDPR) can be complex and demanding. Data tokenization can help simplify compliance efforts. For example, PCI DSS requires stringent security measures for handling credit card data. Tokenization can help businesses adhere to these requirements by ensuring that cardholder data is replaced with tokens, reducing the scope of the systems subject to compliance audits.
Similarly, GDPR mandates strict rules for the protection of personal data. By tokenizing personal information, organizations can minimize the amount of personal data they store, reducing the risk of violating GDPR principles while still maintaining the utility of the data for analytics and operational purposes.
Data breaches are a significant concern for organizations, as they can lead toreputational damage, financial losses, and legal repercussions. Tokenization mitigates the risk associated with data breaches because even if an attacker gains access to tokenized data, they would only see meaningless tokens rather than the actual sensitive information. This substantially reduces the value of the stolen data and limits the potential harm.
While tokenization hides sensitive information, it doesn’t render the data useless for analysis. Tokenized data can still retain its structure and relationships, making it suitable for various analytics and reporting purposes.Organizations can perform meaningful data analysis without the need to expose sensitive details. This is particularly beneficial in scenarios where insights need to be extracted from large datasets without compromising datasecurity.
Cloud service providers can use tokenization to enhance security in multi-tenant environments. Each customer’s sensitive data is tokenized, ensuring that even if data is stored on the same physical infrastructure, it remains isolated and protected. When moving data between different cloud environments or systems, tokenization can be used to protect sensitive information. This reduces the risk of exposing data during the migration process.
Guaranteeing adherence to regulations and minimizing privacy vulnerabilities has emerged as a key concern for enterprises. Data tokenization masks personally identifiable information (PII) and sensitive data, safeguards from potential data privacy risks, and facilitates data retention, all aimed at protecting privacy and attaining regulatory conformity.
Utilizing publicly accessible Large Language Models (LLMs) and generative AImodels can introduce substantial privacy and security challenges for organizations. Data tokenization enables you to share data with machines without compromising privacy. It ensures data comprehension while mitigating the possibility of associating it with specific individuals, thereby offering you the highest levels of privacy and security.
Data tokenization technology substitutes personally identifiable information (PII) with non-sensitive, simulated data, safeguarding data privacy during data sharing. This enables comprehensive data utilization while upholding customer privacy, empowering you to securely share data in compliance withdata sovereignty regulations.
Utilizing cloud services for handling sensitive workloads presents several benefits, yet it also gives rise to apprehensions regarding privacy and security. Implementing a strong data tokenization solution enables organizations to effectively address and reduce the risks associated with privacy and security in the cloud.
Generating authentic test data is crucial for efficient software testing and development. Nevertheless, duplicating production data for this objective can introduce substantial security and privacy vulnerabilities. Data tokenization allows you to produce pseudonymous test data derived from your production data, all while guaranteeing data privacy.
Data retention regulations frequently mandate the removal of personal data after a designated timeframe, like the 7-year period stipulated by GDPR. Nevertheless, this can lead to valuable information loss for businesses. Data tokenization offers a solution by converting personal data into anonymized formats, enabling companies to fulfill data retention obligations without erasing the information.
In the realm of e-commerce and payment processing, data tokenization is crucial for complying with PCI standards. It helps merchants securely handle credit card data by replacing actual card numbers with tokens. This reduces the risk of data breaches and theft while allowing companies to continue processing transactions seamlessly. Tokenization can be used to tokenize sensitive payment data during online transactions. This ensures that the actual credit card numbers are never stored in the merchant’s systems, minimizing the impact of potential breaches.
Tokenization plays a vital role in healthcare to protect patient data, including personal information and medical records. By tokenizing sensitive identifiers like Social Security Numbers and patient IDs, healthcare providers can maintain data usability while significantly reducing the risk of data exposure. Healthcare organizations often deal with insurance claims and medical billing data. Tokenization can secure patient payment information and insurance details, allowing accurate processing while safeguarding sensitive information.
Law firms and legal departments dealing with sensitive legal documents can use tokenization to share information with other parties securely. Instead of sending actual documents, they can share tokens that retain the document’sstructure without revealing its content.
In each of these use cases, data tokenization helps strike a balance between data security and usability. By replacing sensitive information with tokens, organizations can mitigate the risk of data breaches, comply with regulations, and provide enhanced security measures for their customers and clients.
When performing ML or NLP processes in the cloud, tokenization is performed for the computer to make the machine understand the POS (Parts of Speech) of the English language. When it comes to tokenizing a large corpus of words, tagging them locally will not be sufficient. Hence, they may need to be switched to a level 6 cloud platform where both the storage and processes will be done in the cloud.
Cloud Data Tokenization is a large-scale industry requirement to tokenize and pseudonymize data over the cloud so that the cloud can comply with data privacy regulations. Especially, in current times, data privacy is of utmost importance.Many companies offer Tokenization as a Service (TaaS) to cloud service providers so that they can offload this work to a third-party company since cloud data tokenization while also adhering to data protection regulations (like HIPAA, and GDPR) is no simple task.
Data tokenization in the cloud is more important since it involves multi-cloud platform solutions. Data may need to be stored in many cloud platforms instead of a singular platform to encourage the usage of multiple cloud platforms. Cloud Data tokenization is particularly useful here as it doesn’t compromise the security of the data despite it being stored in multiple cloud platforms.
In the realm of cybersecurity, there are many methods used to protect sensitive data. Some of those methods are Tokenization, Encryption, and Data Masking. While all three methods serve the same purpose, they have polarising differences in their methodologies and use cases.
| Factors | Tokenization | Encryption | Masking |
|---|---|---|---|
| Process | A unique identifier replaces each value/variable. These identifiers are mapped with the actual data. After that, the original data is stored in secure databases called “vaults”. | Using algorithms they convert readable documents to incomprehensible documents. | Characters are partially obscured showing only part of it. |
| Purpose | To protect data, the data is tokenized so that even if this data is intercepted by hackers, it will be meaningless to them. | For the sake of privacy and to keep the contents of a document a secret, it is encrypted so that no one will know the contents. | Partially obscures data from viewers by masking it with meaningless characters to prevent unauthorised access. |
| Use case | Helps with data protection regulations. Businesses reduce the sensitivity of data using tokenization. | Helps with data and advanced network security. It is difficult to get the message of an encrypted message without the decryption key. | Makes it so that others cannot view your personal information by directly looking at the screen when you are entering the details. |
| Reversibility | Tokens are not reversible. The original data cannot be reconstructed from this since the original data is only mapped to the token, not changed into a token itself. | It is possible to get back the original data using a decryption key regardless of the techniques used to encrypt the given data. | With greater authorisation access it is possible to view the full data including the masked data. No changes are made to the data, only they are partially blurred to prevent any personal information from being seen. |
| Data Storage | The original data is stored in a secured system or token vault where all the tokens are mapped to their respective original data. | Both encrypted and decrypted data are stored locally in the system in a secure location. | It is stored in a common secure database, only when viewing, the data is automatically masked when you view it. |
| Key Management | The algorithm manages the mapping of tokens with the original data. No key is used here. | In the cases of both Symmetric and Asymmetric Decryption, encryption and decryption keys are used for encryption and decryption of data respectively. | No change of data is done, data is partially censored since you need not see all the details of your information. Hence, no key is used here. |
| Usage | Example: Used in payment transactions where card information is replaced with tokens to protect user privacy |
Example: Used to ensure the security of data while transferring it digitally through networks. |
Example: Used in use cases where sensitive information needs to be displayed in front of a crowd for bank transactions or getting a One Time Password (OTP). |
From this, we can conclude that all three methods mentioned above are important for varying use cases. For different levels of data privacy, these methods are used. Tokenization is the process of replacing sensitive data with tokens and mapping these tokens to the original data stored elsewhere, while encryption changes the whole original data into ciphertext with an encryption key developed by the developer. The original data can be brought back with a decryption key developed similarly. Masking doesn’t change the overall data as tokenization and encryption do but partially obscures data while also showing the original data.All three of the methods are used commonly in many different scenarios.
Tokenization in the context of blockchain and cryptocurrencies refers to the process of representing real-world assets or data as digital tokens on a blockchain. This practice has gained significant attention due to its potential to revolutionize various industries.
Tokenization in the context of Internet of Things (IoT) devices involves creating digital representations of physical objects or devices and associating them with tokens on a blockchain. This practice holds great promise for enhancing the functionality and security of IoT ecosystems.
Tokenization in both blockchain and IoT contexts is set to reshape various industries by enabling new possibilities for ownership, data management, and secure interactions. However, realizing these trends requires addressing technical, regulatory, and societal challenges while fostering innovation and collaboration.
Data tokenization stands as a pivotal solution for safeguarding sensitive information in a world where data breaches and privacy concerns are constant threats. By understanding the intricacies of data tokenization, its benefits, challenges, and best practices, you’ll be well-equipped to implement effective data protection strategies for your organization.
Whether you’re in the finance, healthcare, or any other industry dealing with sensitive data, some important questions that every organization needs to deal with include:
Protecto is transforming the way enterprises safeguard sensitive information with cutting-edge technology. We are taking a giant leap forward on helping enterprises protect and unlock their data with intelligent data tokenization. Identify PII, monitor and mitigate data privacy risks with the power and simplicity of Protecto.
In summary, both tokenization and encryption are valuable techniques for protecting sensitive data, but they serve different purposes and have different implications in terms of data security, storage, and processing. Tokenization focuses on substituting sensitive data with tokens, whereas encryption focuses on transforming data into a reversible, encrypted form.
Protecto’s data tokenization tool ensures that your data is secure with their SAAS-based hosting services which are meant to hold large volumes of your data. Protecto performs pseudonymization of data so that no real person’s data is put at risk of being stolen or sold. This pseudonymization will not affect data visualizations and analysis in such a way that your privacy is guaranteed.
Protecto also limits views over regular data by only allowing authorised people to view it. Also, providing granular access ensures data protection by only letting those who are trustworthy to view the data. Moreover, migrating your data to the cloud has never been easier due to Protecto’s agentless migration capabilities.
Data tokenization has become one of the most necessary methods to ensure data privacy and security. By data tokenization, you make the act of stealing data almost meaningless. Here are some benefits explained in detail.
With Protecto’s agentless tokenization of data, they ensure state-of-the-art tokenization algorithms for your data. Tokenized data will be stored in Protecto’s SaaS or self-hosted secure environment.
Protecto’s data tokenization algorithms are built forlarge-volume data. It is very easy to scale your E-commerce site to accommodate more customers while also ensuring the highest levels of data security.
Protecto tokenizes your data in such a way that you can use the tokenized data to perform data analytics and see trends and patterns with little to no loss of insights due to identity removal from the data. Your business will be able to extract meaningful patterns and trends while also ensuring data privacy.
With Protecto’s robust tokenization solution, you can mitigate your data privacy and security risks across cloud platforms. These data points can also be stored across multiple cloud service platforms so that you can reap the benefits of these platforms based on the compatibility of the user’s devices with that specific cloud for data collection and tokenization.
Check out what we have to offer in terms of masking and unmasking your sensitive data. Sign up for a free trial.
