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Security & Access Control Modelling is the practice of defining structured rules and frameworks that determine who can access what data, under which conditions, and in what manner. It formalizes data protection through roles, permissions, entitlements, and policies aligned with organizational security requirements and regulatory obligations.

The primary goal of this modelling approach is to ensure that sensitive information is accessible only to authorized users while still enabling legitimate business access. It balances protection and productivity by translating business security rules into enforceable system controls.

At its core, Security & Access Control Modelling defines entities such as:

• Users (individual identities)
• Roles (grouped responsibilities or job functions)
• Permissions (allowed actions, such as read, write, approve)
• Policies (conditional rules governing access)

Typically, users are assigned to roles, roles carry permissions, and policies define constraints such as time of access, geographic location, or device type.

In today’s rapidly evolving digital landscape, security threats are more sophisticated and pervasive than ever. Traditional perimeter-based security is no longer sufficient to protect your organization’s most valuable asset – its data. This is where Zero Trust Architecture (ZTA) comes into play.

Data breaches can have catastrophic consequences, both financially and reputationally. Zero Trust Architecture ensures that no entity – human or machine – is trusted by default, minimizing the risk of unauthorized access. With millions of IoT devices and diverse infrastructures spanning on-premises and cloud environments, ZTA offers a robust security framework that addresses these complexities through stringent authentication, authorization, and entitlement mechanisms.

Starting with a zero-trust baseline where no one has access, the architecture gradually opens doors through verified credentials and continuous monitoring. This adaptive approach, combined with data encryption, masking, and anonymization, ensures that your defenses are resilient against emerging threats. Implementing Zero Trust Architecture significantly reduces the risk of data breaches, ensures regulatory compliance, streamlines security operations, and future-proofs your security strategy.

Data Anonymization, Data Pseudonymization or Data De-identification all worked the same way and related to Data Privacy.

Data Anonymization is about removing or modifying personal information before sharing with others so those people whom the data about remains anonymized.

‘Data Anonymization is about hiding the identity of a person to be undetectable.’

For example, while implementing a data solution it is required to share data with development team, so before sharing one must make sure data is anonymized, persons details are not exposed to development team members e.g., name, contact number, address, credit card number etc.

Data Anonymization is normally enforced by Regulatory Bodies and not following can result in heavy penalties and a bad reputation for any organization.

Techniques [All these terms are covered separately]

• Data Masking
• Data Pseudonymization
• Data Generalization
• Data Swapping
• Data Perturbation
• Data Synthetic
• Data Shuffling
• Data Scrambling
• Data Encoding
• Data De-identification

Data Identification is the pre-requisite for Data Anonymization. How?

Data Identification is opposite to Data Anonymization, in other words, first one must identify data before conducting Data Anonymization.

Data Identification is about those set of details which assist in identifying an entity or individual. These details can be direct details like name, contact number, address, credit card number etc. or can be indirect details like unique identification number which assist to connect with other datasets to identify an entity or individual.

Data Identification is a very crucial exercise in data migration project where the process is to first conduct data discovery then data identification and then data migration can take place.

Data Identification can be done in all kinds of datasets i.e., structure, semi or unstructured but recommended practice is to do it in structured datasets.

Other Benefits

Data Generalization/ Blurring is normally confused with Data Anonymization which is about removing or modifying personal information so the identity of a person can be undetectable. Data Generalization aim is same but using with different approach.

‘Data Generalization/ Blurring and Specialization is something like Zoom-Out vs Zoom-In or Precise vs Less-Precise.’

One of the main requirements for Data Generalization comes into play when you must maintain Data Sovereignty (explain in a separate topic) but keep the data originality. It is used to cater for data privacy.

‘Data Generalization summarized data in such a way that the usefulness of the data remains intact.’

Data Specialization is the right opposite of Data Generalization as it divides entity into multiple entities to further make sense of the data.

Example of Data Generalization: Referring to the image, Students and Patients both have Name and Mobile numbers, we can have summarized entity like Person which can host both Students and Patients details i.e., Roll_No and PID numbers. This way, there will be records of all the students and patients, but no one will be able to identify any student or patient.

Data Perturbation/ Swapping/ Shuffling/ Data Scrambling/ Data Obfuscation all use the same technique to hide the identity of an entity but keeping the essence of the data.

Data Swapping is about replacing the values of data at the same level of detail as compared to original data. In other words, cardinality (explained in a separate topic) of the replaced data is maintained.

• In Data Anonymization, identifiable data was removed.
• In Data Generalization, identifiable data is summarized.
• In Data Swapping, the level of identifiable data is replaced.

‘Data Swapping technique is majorly used by Data Scientists or Data Citizen who require data at the lowest level to create and train their Machine Learning Models.’

Let’s refer to the same Age example used in topic: Data Generalization/ Blurring and Specialization. For example, there can be an Age column with value: 26, 28, 31, 33, 37, 42, 42, 46, 48, 49, 54, 57, 57, 58, 59. In Data Swapping, to maintain the same level of detail i.e., ages can be swapped 26 with 59, 28 with 58, 32 with 57 and so on.

Static Data Masking (SDM) is also known as Data Masking and is one of the most underrated terms in Data but at the same time it's one of the most wanted terms for Data Privacy.

Data Masking is from the same family i.e., Data Perturbation/ Swapping/ Shuffling/ Data Scrambling/ Data Obfuscation.

Data Masking is a technique to convert actual data into dummy data for the sake of hiding its originality. If the Credit Card number is 3345-0987-2234-2342, it will be converted into 1234-5678-9101-1123. So, if someone can view my data, my identity will not be disclosed. I am sure understanding Data Masking is not difficult but the implications of not having or having bad techniques are debatable.

Please note, there are many domains where Regulatory bodies don't allow organizations to disclose certain sets of data to anyone which creates challenges in the development environment or at the time of sharing the data to third/ external parties.

Dynamic Data Masking (DDM) behaves just like SDM (Static Data Masking) or in other words like standard Data Masking.

We all know how Data Masking works so we won’t be discussing it, rather we will discuss why DDM came into being and why it’s getting very popular.

As we all know previously, we were working on structured data only which is only 20% whereas in new era, we also have 80% more data i.e., semi and unstructured datasets. Not only datasets, but the data science practices have also matured in the last 10 years due to which more and more Production ready data is required by Data Scientists to develop, train, and publish their Data Science Models.

With the introduction of 80% more data i.e., semi and unstructured datasets, it became more troublesome to anonymize data within given SLA. Static Data Masking is a very slow process where one must create a new dataset with masked data, but using Dynamic Data Masking, Production data is masked on the fly before sharing with relevant parties.

Data Tokenization is mostly mixed up with Data Masking but there are major maintenance differences.

‘Data Masking with Mappability’

We know how data masking works i.e., it changes data with random values, means to hide original data so if data reaches into wrong hands, one should not be able to identify the individual to whom the record refers.

Data Tokenization also replaces sensitive data with unique system generated value called Token but during the process original data + token along with mapping details are stored in a Token Vault somewhere externally for safe keeping.

As Token replaces original data in actual data store, its mapping details are still safe in Token Vault so actual data is always reversible in contrast with Data Masking where after sensitive data is randomly changed it’s no longer reversible.

A quick example for experienced Data Warehousing folk is Surrogate Key where original business keys are replaced with unique autogenerate values, but original data is not touched. In Data Warehousing Surrogate Key resides along with actual data whereas in Data Tokenization, Surrogate Key resides in Token Vault.

Data Redaction is confused with data masking, neither it doesn’t work like SMD, but it works pretty much like DDM.

We know how data masking works i.e., it changes original data with random values so if data reaches into wrong hands, the identity of relevant person should not compromise.

Data Redaction also blacks out the data ON THE FLY which may or may not even make sense to the users using it.

There are four types of Data Redaction.

1. Random: Data is masked randomly
2. RedExp: Expressions are masked.
3. Partial: Some of the data is masked
4. Full: Everything is masked

We deal with Data Redaction on daily basis 😊. For example, when we do a Credit/ Debit Card transaction, a message comes on our mobiles. You must have noticed, except last few numbers all card numbers are always STARS i.e., ******* so for layperson those stars are Redacted values where partial data is masked.