Big Data

The Five Biggest Threats to Data Democratization

(Aleksandr Ozerov/Shutterstock)

Data democratization is a top priority for many enterprises. When employees have easy access to data, they can make decisions quicker, collaborate better, and gain important insights. Companies around the world are coming to understand just how valuable their data can be, but many of them are using less than half of their data because they don’t have an efficient way to access it.

Enterprises are struggling to make data accessible while safeguarding confidential information. New security threats are creating barriers to accessing data, putting the brakes on opening up data to everyone.  Here are a few examples of how these security threats are limiting enterprises’ ability to leverage their data to their fullest:

1. Security Threats from ChatGPT

Even though generative AI is opening up a whole new horizon of opportunities, it also poses significant risks to organizations by threatening the security and privacy of sensitive data. Several ChatGPT data breaches reported this year are raising security concerns that AI technology is at risk of cyberattacks. Since ChatGPT can’t be downloaded but is instead accessed through a web browser, sensitive information or user data stored in conversation logs can be exposed to hackers.


Every time a user enters data into a prompt for ChatGPT, the information is ingested into the service’s Large Language Models (LLMs) used to train the next version of language processing algorithm. The information could be retrieved by unauthorized persons if proper data security isn’t in place.  Enterprises are scrambling to finds ways to leverage the power of the tool without increasing the risk of a data breach.

2. Finding All the Data

An organization’s actual data footprint is often much larger than expected and can change from minute to minute. Data infrastructures that handle data consumption, storage, transformation, and output in one central repository are becoming replaced by multiple data stores, pipelines, and private and off-premises clouds so identifying all the different data sources is becoming a huge challenge.   Because data infrastructure is more complex, it’s becoming increasingly difficult for enterprises to identify the data they have, where it’s located, and who should be granted access.  Companies need to evaluate the needs for managing their data products, data governance, the use of data platforms, and how business domains will be managed across the data ecosystem.

3. Fast Changing Data Privacy Regulations

Businesses all around the world are grappling with a changing regulatory landscape and new compliance responsibilities. The sheer volume and complexity of regulations present one of the most pressing challenges for businesses to remain compliant.


In addition, specific business units within a business organization may be subject to their own regulations or industry-specific compliance standards. Global companies are challenged to follow state, federal and international data privacy regulations.  Over 130 countries have their own legislation for data privacy. Companies that attempt to comply to each regulation individually can become overwhelmed quickly.

4. Lack of Responsibility for Compliance and Data Privacy

Protecting personal data can fall under the jurisdiction of the security department, IT, legal, the compliance group, or all of the above, which requires a high degree of transparency and collaboration. Employees that have responsibility for data security include data engineers, analysts, analytics engineers, data scientists, product managers, business analysts, citizen data scientists, and more.  In addition, when data is stored in the cloud the responsibility is shared between the organization, the cloud provider, and all its users, which increases the risk of errors or miscommunications which could result in data loss.

5. Monitor and Track Data Usage

Today additional information needs to be collected, stored, and analyzed about how data is accessed and used to confirm compliance. Taking into account the huge quantity of data leveraged for business intelligence this task is becoming more and more challenging. This includes monitoring all actions performed on the data by users such as querying, analyzing, reporting, or exporting.  Information stored in data catalogs, data lineage, data quality, data analytics, and data alerts needs to be tracked. In many cases, enterprises must retain appropriate audit-related data for up to seven years, depending on the type of regulations.  This data requires storage and processing for most organizations, putting strain on infrastructure while adding additional tasks for teams responsible for compliance.

Examining big data to hidden patterns, correlations, market trends and customer preferences is becoming a must for most enterprises. With the increasingly complex data landscape and dynamic regulatory environment organizations need to put in place a strong data governance. The rapid rate of change, growing data volumes, and new technologies is adding complexity that requires more sophisticated means of managing data access.   Intelligent automation can keep up with the pace of highly dynamic teams, regulations, and privacy policies ensuring employees can receive the data they need for better business intelligence while ensuring that companies stay compliant.

About the author: Adi Hod is the co-founder and CEO of Velotix, a data security provider. Adi specialized in optimization solutions in a variety of markets such as banking, utilities, telecommunications, and healthcare. He transforms strategic plans into tactical reality through building enterprises across different regions and cultures. Adi holds a BSc, MSc, EMBA and PhD in Engineering, Operations, Research and Algorithms from the Technion, Haifa and MIT. Adi is also a professional piano player and art collector. In addition, Adi volunteered two years in Africa building elementary schools.

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