Big Data Safety Challenges

Big Data Safety Challenges

Big Data, no longer just a buzzword or a promise of exciting things to come, is something that many businesses leverage daily in this modern age. 

With the advent of the digital age, high volumes of data are generated daily from the internet, social networks, healthcare applications, sensors, and various other sources.

Big Data refers to datasets that are varied and complex to such a high degree that they cannot be manipulated with traditional data processing solutions. This explosion of diverse data is what businesses routinely use to create, fine-tune, and implement various plans and strategies.

While things are still relatively at an early stage with Big Data, some challenges and issues need solutions going forward. A lot of these challenges have to deal with issues of privacy and security. Coping with these safety challenges can have a significant impact on the future use and potential of Big Data.

Understanding Safety Risks

At the outset, it is essential to understand some crucial characteristics of Big Data projects that can explain the safety challenges that can come with the territory. Here are some important points.

  1. Projects can often involve components that are heterogeneous with no single standardized security scheme designed for that purpose.
  2. Batch processing or online transaction processing systems have focused on the development of privacy and security methods.
  3. Use cases often require employing multiple Big Data sources that were not intended to be used together, creating further possible safety vulnerabilities.
  4. Increasing streams from sensors in IoT applications can create internet connectivity, transport, and aggregation vulnerabilities.
  5. A lot of Big Data sources, like video imaging and geospatial data, were previously considered too large for analysis.
  6. Big Data inherently magnifies issues with jurisdiction, provenance, context, and integrity.
  7. Volatility becomes significant in scenarios where data is considered permanent.
  8. While most practices and standards have traditionally operated within the framework of a single organization, Big Data opens up possibilities of sharing data at high volumes across different organizations.

It is essential to recognize the safety challenges that can arise out of these factors and acknowledge that with the exponential increase in the generation of data, more and more of it would need to be protected.

Challenges with Storage and Management

Since the advent of Big Data, the scale of it has faced constant change. As the scale of data generation keeps increasing exponentially, an event like a leak can bring catastrophic consequences.

The challenges of storing and managing Big Data come mainly from the appropriateness of the storage solutions. In many cases, this data is stored using solutions that employ a horizontally scalable, distributed storage platform. 

Mostly in cloud storage applications, solutions like Tachyon, QFS, HDFS, Ceph, GlusterFS can provide the storage volumes and scalability needed. However, this does not necessarily mean that they satisfy the security and concurrency requirements that are ideal.

Furthermore, storing data in the cloud can itself present safety challenges. In many cases, data needs to be moved across cloud and local storage for processing, and this opens up opportunities for unauthorized access.

The owner of any data is liable to lose control over the information when a cloud service is used for storage. The right storage solutions need to implement reliable means for the data owner to test the data's safety and integrity, using solutions like checksums, digital signatures, trapdoor hash functions, message authentication codes, or Reed-Solomon codes.

Challenges with Transmission and Sharing

While large enterprises control a lot of Big Data, data is also frequently shared across businesses regularly. However, there is very little in terms of supervision or specifications regarding the sharing and use of that data. The self-discipline of enterprises often becomes the only determining factor behind safe practices.

The sheer scale of Big Data can further compound safety challenges with long transmission times across possible vulnerable pipelines. Many enterprises process Big Data in place and transmit only the analytical results or classify the data into smaller parts and transmit only the data relevant for analysis downstream.

For competitive advantage, companies can share business and individual data. This can increase the risk of disclosure of personal or proprietary information. The need of the hour can be reliable technology and a safe, secure environment.

Overcoming Safety Challenges in Big Data

There is a lot that organizations can do to identify key problem areas in Big Data safety and implement the appropriate solutions. The implementation of encryption can be a simple move that has far-reaching consequences with solving significant problems. Securing endpoints, networks, applications, and physical sites can bring the table an all-around approach to overcoming these safety challenges.

A lot of data security technologies have also evolved with time, becoming more scalable and flexible in dealing with the requirements of Big Data operations. Here are some techniques worth taking a look at.

  • User Access Control 'Traditionally, minimal levels of user access control have been used by many companies to account for high management overheads. With Big Data, however, user access control solutions have evolved to take a more policy-based approach. Through user-based and role-based settings, access can now be automated to a high degree. Multiple levels of user control and multilayered administration settings can be used to provide better granular protection of Big Data.
  • Intrusion Detection 'The distributed nature of Big Data storage architecture can often make them particularly vulnerable to intrusion. Employing intrusion prevention systems can help minimize the chance of intrusions. Should an intrusion attempt still get through, intrusion detection systems can help detect the anomaly and quarantine the data to prevent impact.
  • Robust Encryption 'Modern encryption solutions meant for use in Big Data scenarios can handle huge data volumes and protect whether the data is in rest or motion. They can also be employed to process structured, semi-structured, and non-structured data stored in relational and non-relational database management systems.

Download Example (1000 Synthetic Data) for testing

Click here to download csv

Signup for Our Blog

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Request for Trail

Start Trial
No items found.

Prevent millions of $ of privacy risks. Learn how.

We take privacy seriously.  While we promise not to sell your personal data, we may send product and company updates periodically. You can opt-out or make changes to our communication updates at any time.