What is Data Integrity?
In the hindsight, it is a very simple concept. You always want to your data accurately describe (something) as it is intended to.
Thus, the data integrity is about the accuracy and consistent 'validity' of your data over its entire lifecycle. If at any point your data loses its accuracy and validity, then what is the use of this data? Nothing, it's junk!
There have been 2-ways of looking at data integrity:
1. Data Integrity as a state
It is a state of your data set if it is 'valid' and 'accurate,' at a given point in time.
2. Data Integrity as a process
It is a information security process too and it is about taking effective measures, placing restraints, adopting best practices with the clear intention of ensuring the 'validity' and 'accuracy' of a data set, or all data contained in a database, or data in other forms or constructs over their entire lifecycle.
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Data integrity can be compromised in several ways:
The biggest source of all major data integrity risks to your organisation, is human error. These errors would occur when your users or employees are attempting to enter duplicate data or incorrect data. These errors also happen when they are attempting to delete the data without properly following you organisation's protocol to delete the data. These errors also occur when they are making mistakes with your information security procedures which your company has put in place.
All sorts of cyber-threats of hackers, malwares, spyware, and viruses etc can potentially attack on computers and systems of your organisation. Not only they can steal your data, they can change, amend or delete your data too.
If your hardware has been compromised just because of any reason, they can result in sudden shutdowns, crashing of your servers, other data-failures and malfunctions. All these instances could consequently result in data-integrity issues. For example, your data may get rendered incompletely or incorrectly. Your data access may get removed or severely limited. In some situations, your data can become very hard for users to work with...
If data is unable to transfer between database locations, it means there has been a transfer error. These transfer errors occur when pieces of data are in the destination table but not the source table of a relational database, or vice a versa.
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Why is Data Integrity valued so highly in InfoSec?
Most organisations of modern times, run and feed on information, that is nothing but the data coming to you from various sources. You also have your own specialized sets of data too, such as customers database, financial transaction, production database, employees data, and so on. This list is endless...
Maintaining data integrity also ensures greater efficiency throughout the lifetime of the data that comes in the form of increased:
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Recoverability
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Searchability
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Traceability (to origin)
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Connectivity
The importance of data integrity in continuously increasing as data volumes continue to increase exponentially. Very much like most organisations, your company would be becoming heavily reliant on 'Data Integration' tools, in the forms of ERM, CRM, and so many other applications. Your company also need to maintain its ABILITY to interpret information accurately. Otherwise, your company won't be able to predict the behavior of your customers, to evaluate or assess its market(-ing) activities. Even for the purpose of mitigating your data-security RISKS, you need data streams coming to you on consistent basis.
Data Integrity is crucial to data mining too, otherwise data scientists won't be able to work with the right information.
The point is this --
Data increasingly drives the decision-making of most enterprises. Your data is found in many forms and it is precious. But it must undergo a variety of changes and processes to go from raw form to formats which are more practical for identifying relationships and facilitating informed decisions. That's why, data integrity is a top priority for modern enterprises. Remember the CIA triad...
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What are types of Data Integrity?
Your organisation can accomplish data-integrity by ensuring integrity at 2-levels:
1. Physical Integrity
When you are talking about physical integrity, it means you are talking about PROTECTING the accuracy, correctness, and wholesomeness of data when & where it being stored and retrieved.
The integrity of your data may well be compromised by so many of physical factors, e.g., Power-supply failures or outage, HDDs erosions, Natural Disaster striking the facility, or any other breach at the facility, etc.
Very recently I have shared a post on Datacenter Security. In that post, I have pointed about some measures which have been deployed by Google on one of its 'Datacenter.' They have created a form of 6-Layers Deep Defense to that. You can certainly learn a bit about that, in that post.
2. Logical Integrity
Most data is stored and handled with the help of RDBMS. In this context, you would look at the logical integrity of your data. Logical integrity is concerned with ensuring that all data remains unchanged while being used in different ways through relational databases. It is about limiting the chances of human-errors corrupting the databases.
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What is the difference between Data Integrity & Data Security?
Data Integrity and Data Security are related terms, but they are not same. Data Security is much wider in scope and refers to the PROTECTION of data against unauthorized access or corruption or theft and is necessary to ensure data integrity. In a way, you can say that Data Integrity is the benefit or outcome of Data Security practices.
However, it is important for you to understand that Data Integrity is concerned only with the ACCURACY and VALIDITY of data, rather than the actions taken for its protection. For modern enterprises, data integrity is essential for the accuracy and efficiency of business processes as well as decision making. It’s also a central focus of many data security programs.
In order to achieve Data Integrity, you not doubt implement some of protection mechanisms such as backup and replication. However, you would also undertake a number of additional steps for Data Integrity, e.g., Database Integrity constraints, Data Validation processes, and other systems and protocols.
There is one more concept that is closely related to Data Integrity, i.e., DATA QUALITY. It is known as a crucial piece of the data integrity puzzle. Data Quality is about meeting some certain data standards of your organisation and ensuring that the information perfectly aligns with the requirements of your organisation. Data Quality demands you to employ a variety of processes that measure the age, accuracy, completeness, relevance, and reliability of your data. Data quality goes a step further by implementing processes and rules that govern data entry, storage, and transformation.
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DATA INTEGRITY FOR DATABASES
Database integrity refers to the validity and consistency of stored data. Integrity is usually expressed in terms of CONSTRAINTS, which are consistency RULES that the database is not permitted to violate. Constraints may apply to each attribute or they may apply to relationships between tables.
Integrity constraints ensure that any change (e.g., update, deletion, insertion) made to the database by authorized users DO NOT result in a loss of data consistency. Thus, integrity constraints guard against accidental damage to the database.
For databases, there are four types of logical integrity (constraints):
1. Domain Integrity:
Domain integrity means the definition of a valid set of values for an attribute. You define the following:
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Data type
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Length or size
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Is null value allowed?
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Is the value unique or not for an attribute?
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The default value
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The range (values in between) and/or specific values for the attribute.
2. Entity Integrity
You already know that in a database, there are number of tables. And in each table there are rows and columns. Then there will be a PRIMARY KEY in each table. The entity Integrity constraint is a rule that dictates that -- the primary key should never be the same and none of the elements (represented by primary key) should be NULL. For example, suppose you have a database of your employees. There you would have a column of their NAMES and another column for their' EMPLOYEE ID Number' and you have designated a primary key on EMPLOYEE ID Number attribute.
It means that you should never have same employee ID number and it never should be NULL too.
3. Referential Integrity
It states that if a foreign key exists in a relation, then either the foreign key value must match a primary key value of some tuple in its home relation or the foreign key value must be null.
There some more rules to establish this integrity or constraint:
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You can't delete a record from a 'primary' table if matching records exist in a related table.
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You can't change a primary key value in the 'primary' table if that record has related records.
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You can't enter a value in the 'foreign key' field of the related table that doesn't exist in the primary key of the primary table.
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However, you can enter a Null value in the foreign key, specifying that the records are unrelated.
The idea behind these rules is to eliminate the entry of duplicate data, and to guarantee that data entry is accurate, and/or disallow the entry of data that doesn’t apply.
4. User-Defined Integrity
There are sets of data rules, created by users, outside of entity, referential and domain integrity. User-defined integrity means that rules and constraints around data are created by you to align with your specific requirements.
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General Step To Preserve Data Integrity
There are number of steps you should take on regular basis:
1. Always validate the Inputs
2. Data should not be Duplicated in any other forms (e.g., emails, spreadsheets, folders, documents, etc.)
3. Consistently take the backup of your critical data (Daily, if possible)
4. Implement the 'least-privilege' approach to data access
5. Always keep an audit-trail. An audit trail allows you to track what happened and how it was transformed and used, and then find the source of the attack.
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With thanks,
Meena R.
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This Article Was Written & published by Meena R, Senior Manager - IT, at Luminis Consulting Services Pvt. Ltd, India.
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