Intelligent Tokenization Techniques & Examples

Intelligent Tokenization Techniques & Examples

As Artificial Intelligence (AI) advances at an unprecedented pace, traditional data protection methods have become inadequate. With this growing demand for data privacy solutions built for AI, such as Intelligent Tokenization, tailored for AI becomes the game-changer.

What is Intelligent Tokenization?

When it comes to data security, tokenizing data plays an important role. But, in the tokenization of data, consistent tokenization is also important since the type of data should be apparent to those handling data. 

The idea may seem contradictory at first, but wouldn’t randomness and unpredictability work better for privacy and security? The answer is, that it doesn’t quite work that way. Employees in the company may need to go through your pseudonymized data and perform data analysis on it to detect trends, find patterns and so much more. In a world run by data, pseudonymizing the data will work against you and may need more time to debug the data, and retrieve your data from the vault, which may create many weak links through which malicious users may be able to exploit and harvest your data.

Example of Tokenization

For example, if your information is stored for “” as “2zfbecan23_0124”, employees working on data analysis may not be able to decipher what part of the user data this comes from in the vast slew of tokenized data. This may waste a lot of their time trying to go back and forth trying to find the data. Instead, some common identifiers can be used to give employees an emphasis on what type of data it is. Instead, they can be tokenized as “2zef23@ibsf_0011”.

Intelligent Tokenization techniques

There are many ways and techniques through which user’s sensitive data can be tokenized. Some of them are as mentioned below:

Granular Access

Protecto’s pioneering solution makes it so that only the highest level and vetted administrators will be able to view the actual data. By tokenizing the data, no sensitive information is leaked since the person with access to the data is guaranteed to be trustworthy.

Generate Tokenization algorithms using LLMs

With the high processing and computing power of Large Language Models (LLMs), the traditional approach is to introduce pre-defined features to the model which will then tokenize the data based on these features. But, the modern approach is to make the LLM go through the raw, unfiltered data to generate custom functions to tokenize data. Since LLMs are black boxes, no one knows on what basis these are getting tokenized, and also the LLMs are trained to make sure that these tokenizations are made in a way that the features and different types of data such as the username and so on are discernible.

Enhanced Flexibility

Protecto’s tokenizing model guarantees flexibility such that in case your data token is compromised in one way or the other, it will be able to delete the existing token along with your data information and provide your data with a new token generated from a new algorithm. This flexibility is crucial when dealing with extremely sensitive data such as healthcare information.

Protecto’s services comply with HIPAA and GDPR policies of data protection.

Why Traditional Methods Fall Short:

AI is a behemoth that thrives on data - the more nuanced, the better. Traditional data masking was built for a past world that wanted to mask financial data for PCI compliance. While masking was effective in simpler times, now struggles to keep up:

  1. Loss of Data Utility: Traditional masking, in its bid to protect, often renders data too generic, stripping AI of the rich, granular information it needs to learn effectively. 
  2. Inconsistency: Without a set standard, different data sets might see the same information masked differently. Imagine the name "John Doe" appearing in other datasets. Without a set standard, one dataset might mask it as "J1234 D4237", while another is masked as "XYUI KL89". These inconsistencies muddle AI's training phase.
  3. Structure Amnesia: Traditional techniques can distort the natural structure of data, depriving AI models of valuable context. For instance, if you have an email like "", traditional masking methods might mask it entirely as "HJK1289", losing context like the domain, which is valuable for AI insights.

Enter Intelligent Tokenization:

Built with AI's unique requirements in mind, Intelligent Tokenization comes as a breath of fresh air:

  1. Maximized Data Utility: While ensuring utmost privacy, Intelligent Tokenization maintains the data's inherent essence, allowing AI models to delve deep and extract patterns.
  2. Consistency as a Standard: No matter where it's applied, the same piece of information is tokenized identically, ensuring a uniform data language for AI. For recurring data points like "John Doe", Intelligent Tokenization ensures the name is represented with the exact same token across different datasets, facilitating a seamless AI training experience.
  3. Preservation of Structure: Data formats are retained, so AI models can still recognize patterns in an email domain or a phone number structure. Instead of completely masking "", it might be transformed to "bbbb@aaaa," helping AI recognize patterns related to domains or naming structures.

Role-Based Access: Protecto's Pioneering Solution:

Historically, AI models operate devoid of roles and provide results indiscriminately. However, with the integration of Intelligent Tokenization, Protecto introduces the concept of role-based access within AI, a groundbreaking move. Using Protecto's tokenization,  specific outputs can be made visible only to designated users or roles, enhancing both data security and flexibility. For instance, a customer service rep might only see a tokenized phone number in the results from an AI model or fine-tuned LargeLangugae Model (LLM), but a higher-level system administrator can view the actual details interacting with the same model.

This innovation by Protecto allows businesses to introduce layers of security and access within AI's outputs, a feature unprecedented in traditional AI systems.

The Future of Data Protection:

As AI continues its upward trajectory, it's clear that the future demands advanced data protection mechanisms. The transition from traditional masking to Intelligent Tokenization isn't just an upgrade—it's a paradigm shift. With Protecto offering unmatched technology, businesses and institutions across the globe can tread confidently into an AI-rich future, assured of data privacy and utility. 

Protecto's Intelligent Tokenization offers a harmonious blend of data protection and utility, heralding a new era in AI and data privacy. Test drive our Intelligent Tokenization for free. 

Learn more about data tokenization with The Ultimate Guide

Download Example (1000 Synthetic Data) for testing

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Amar Kanagaraj

Founder and CEO of Protecto

Amar Kanagaraj, Founder and CEO of Protecto, is a visionary leader in privacy, data security, and trust in the emerging AI-centric world, with over 20 years of experience in technology and business leadership.Prior to Protecto, Amar co-founded Filecloud, an enterprise B2B software startup, where he put it on a trajectory to hit $10M in revenue as CMO.

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