Cybersecurity is a pressing concern in today’s world. It has the ability to boost the digital presence of a business to sky-high levels. While IT (Information Technology) made us capable of delivering a new array of real-time services and goods to our clients, security makes it possible for businesses to use these innovations by guaranteeing that data stays secured and protected.
Yet data integrity is just as vital. Any modern business hoping to keep precision and accuracy of its procedures needs to ensure data integrity in the first place; otherwise, they could find themselves in a lot of trouble.
What is Data Integrity?
Data integrity is one of the fundamental components of IS (information security). Typically, data integrity refers to the consistency and accuracy of data stored in any warehouse, data mart, database or other constructs. This term describes either a process, a function, or a state and is frequently used as a synonym for “data quality”, even though they are not strictly similar.
When we say a certain data has “integrity” we are referring to its veracity, meaning it is complete, consistent, and trustworthy. To achieve integrity, all of the data’s values must be standardized following a specific data model or data type. Also, all of its characteristics – including business relations, rules, definitions, lineage, and dates – must be correct.
Companies usually impose data integrity within a database through implementing designs and processes that require some sort of authentication. This can be achieved by using error-checking and other validation routines. For instance, if we aim to maintain data integrity on a numeric spreadsheet, no columns and cells must accept the incorporation of alphabetic data.
Common Data Integrity Challenges
Reliable, clean, consistent, and correct information is the ultimate dream for any data-driven company. It might be even the biggest asset an organization can have. However, to get to that point, you’ll first have to surpass plenty of obstacles. Here’s a list of some ordinary data integrity challenges every company needs to worry about:
- Multiple Sources of Data: Information entering your business from all sorts of places such as public records and the company’s intranet.
- Multiple BI Tools: Aside from pulling data from multiple sources, most companies usually have too many analyzing tools with overlapping functionalities. This is both a waste of money and effort and can also lead to data alteration.
- Disorganized Data Workflow: This is a prevalent mistake that needs to be addressed quickly. Companies ought to maintain a standardized workflow for both data collection and processing; otherwise, interdepartmental issues will arise and valuable information will be lost.
- Manual Data Transfers: Human work will always be tied to the possibility of error. In fact, most data integrity issues are caused by manual data pulls; therefore, an automated data flow will certainly come in handy if you’re looking to prevent future data-driven mistakes.
A Pressing Issue
It can be tough to avoid or even identify a data breach as they can go unnoticed for months and frequently the victim has no knowledge about the loss up until it’s found by a 3rd party, such as national security organizations. Identifying the corruption or modification of data can be much more challenging. If the data stays in the original format, changes in it can be less obvious than theft, even though the data’s value can be incredibly impacted.
Businesses need to worry about data integrity in all forms and presentations, not just in applications and databases, but also in backups and other types of recovery information created in case of a disaster. According to the NCCE (one of the main organization in the U.S. that deals with cybersecurity), there are many efforts taken to deal with the more pressing issues of cybersecurity, such as collaborations between the industry and organizations such as the NIST (National Institute of Standards and Technology). Among these difficulties is ensuring the precision of recovery data. Some of the main concerns being resolved by the NCCoE consist of:
- Ways to identify by who, when, and how data is corrupted.
- What was the magnitude and effects of the corrupted data?
- The best backup process for data recovery.
Hashing
Methods like hashing have proven to be an effective method to ensure data integrity. The process consists of using an especial cryptographic algorithm that compresses a file or any data element into a brief row of numbers, similar to a message ‘digest’. Done correctly, these numbers are unique to the information that was compressed so any modification in the data will immediately produce a different digest. Therefore, any modification will come up as soon as you run a contrast of these numbers.
In 2015, the NIST approved the new standard for hash contrast called the SHA-3 (Secure Hash Algorithm 3), which was presented as “the new tool for protecting the integrity of cyber information”. This brand-new algorithm didn’t actually change significantly compared to the previous version, which appears to be the law of the land right now, but it was created as a backup in case the SHA-2 ends up being susceptible to attacks.
More than confidence at stake
Any business incapable of ensuring its data integrity is one that will never know if the vital operations, choices, or even services offered are being performed correctly. All of these can directly affect its future because they can lead to errors, missed opportunities, and immediate loss of money and earnings. But beyond the instant losses lie the wider issues of public self-confidence and brand credibility. A business that does not serve efficiently its customers can lose their confidence quickly, leading to long-lasting damages to its reputation.
Data integrity has also recently become one of the leading concerns in the public and political sector with evidence pointing to a possible Russian tampering in the 2016 U.S. election. These issues led to a caution from the Director of National Intelligence and the Department of Homeland Security, prompting “state and regional election authorities to be alert and seek for cybersecurity help from the DHS,” to ensure election data integrity.
Fundamental cybersecurity practices, consisting of file encryption, tracking and gaining access to control, can also help ensuring data integrity in your system, but the bottom line is that cybersecurity needs to address data integrity along with its privacy and accessibility concerns.
