In this ever-evolving era of digital transformation, the way businesses thrive is changing every second. There is a massive influx in the volume of data owing to rising computing technologies like machine learning, artificial intelligence, remote servers, and most importantly big data.
Big data, as the name suggests, controls and manages a large amount of data. This data, either fully structured, partially structured, or unstructured, is of immense worth. Organizations are always competing to get this information to use in advanced analytics applications such as predictive modeling and machine learning projects.
In 2001, Doug Laney, then an analyst, characterized big data by three Vs:
- Volume—the vast amount of data in different environments.
- Variety—the extensive variety of data types.
- Velocity—the swift velocity at which this data is generated and processed.
According to a new market study published by Global Industry Analytics Inc, the global big data market is predicted to reach $234.6 billion by 2026. The U.S. market is expected to be leading at $ 50.1 billion in 2021, while China is forecasted to reach $27.9 billion by 2026 trailing a CAGR of 12.1%.
Big data is at the heart of digitized scenarios, and data security is an unalterable part of it. Even though data analytics has created several opportunities for businesses and the government, it has also opened avenues for cyber attacks that are only increasing with time.
Why Is Security Vital for Big Data Startups?
With the increasing collection of user data, the potential for misuse of data has also risen proportionally. Over the last few years, the public outcry about data breaches due to insufficient security measures have become louder. Thus, it becomes even more important for big data startups to start maintaining proper cybersecurity posture.
Owing to this, the need for a robust and holistic cybersecurity setup is greater than ever. Moreover, it has become essential, especially for big data startups, to analytically comprehend the gravity of the situation.
They must be ever-ready to utilize the acquired information positively while keeping it clear from nefarious activities. Even seemingly minor trends from the rise of password managers to gaming VPNs show a global tightening of security around systems and data.
Not a week goes by without a substantial cyber breach making headlines. Moreover, the current pandemic has had a devastating impact on numerous organizations in different sectors and compliance laws as well, such as the impact of COVID-19 on HIPAA compliance laws.
Organizations struggle to tackle the developing security threats and concerns. It is high time they recognize the most critical threats first to mitigate and respond effectively. These threats include:
- Fake data generation
- Multi-cloud computing
- Data cleansing issues
- Real-time security compliance
- Data mining issues
- Lack of security spending and audits
When it comes to the security setup of big data startups, organizations are most likely to face at least a few of the above-mentioned challenges. Since big data contains a huge amount of data sets having confidential and private information, the evolving data privacy becomes a major concern for organizations.
Conveniently, there are cybersecurity measures for overcoming big data startup security challenges. The following methods and techniques serve as a bulwark against critical cyber breaches, reputational damages, and legal ramifications.
#1 Set Up Security Foundations
Big data startups must establish clear-cut strategic priorities and organizational guidelines for selecting linked technology. The goal is to ascertain absolute agility across all security systems in terms of performance, reliability, scalability, and reliability.
Once you have established security strategies and values, it is pivotal to deduce crystal clear security guidelines, policies, user agreements, and contractual clauses while outsourcing. Keeping this in view, big data classification and management principles must determine highly sensitive information by defining owners, clearance, requirements, and responsibilities.
The security strategy must be based on managing different big data processes, such as generation and storage of data, its transfer, access and deletion, and so on. Security experts recommend identifying attributes of data first to encrypt them at the starting of the conception phase.
#2 Anonymize Confidential Data
Data anonymization is one of the most practical techniques that has been in use across the distributed and cloud systems. There are multiple solutions to actualize this technique:
- Sub-tree data anonymization
To equip your organization with top-notch anonymity, experts suggest a hybrid approach to the mentioned solutions. This entails a combination of top-down specialization (TDS) as well as bottom-up generalization (BUG). This hybrid approach has proved efficient in terms of scalability to anonymize huge chunks of data without compromising on performance.
Since big data analysis is time-consuming based on several iterations for precise and reliable results, this hybrid solution has shown a considerable reduction in computation time with one iteration for generalization of operations over the cloud system.
#3 Encrypt Via Data Cryptography
Over the years, data encryption has proved to be the textbook solution for guaranteeing confidentiality in big data. Researchers are always vying to upgrade the reliability and performance of existing data encryption techniques.
Based on that, several security analysts came up with a promising technique called homomorphic cryptography. Unlike traditional methods, homomorphic cryptography is unique in that it allows computation on encrypted data.
Another associated approach to handle MapReduce computations is the cloud background hierarchical key exchange (CBHKE). It is one of the most secure solutions that is swifter and more agile than its predecessors.
Moreover, this approach also allows the extraction of valuable insights through possible computations and analysis of already encrypted data. This technique does not compromise the integrity of data.
#4 Have Centralized Security Management
One of the best practices for managing cybersecurity is to rely on the cloud for data storage. This will allow you to benefit from the normalized compliance infrastructure and standardized cloud security mechanisms. Although the cloud platform undergoes rigorous and regular monitoring for robust security, “zero risks” is difficult to achieve.
Therefore, it is recommended to adopt a centralized security system to counter big data security hurdles. This involves the proactive involvement of all relevant stakeholders, making each party responsible and accountable for the adoption of confidentiality policies and regulations.
To ensure the security of the organization as well as its customers, integrate privacy and security prerequisites all through the development life cycle.
The Bottom Line
Big data provides valuable insights to enhance the competitive advantage of numerous organizations in this competitive era. Likewise, it also promises fascinating opportunities for many startups in several sectors. Big data has the potential to optimize production processes tailored to customers’ needs and requirements.
However, big data analysis and sharing also gives rise to several cybersecurity and privacy threats, especially for startups. This post has endeavored to explain the evolving cybersecurity risks and related solutions to better prepare organizations for every situation.
Failing to comprehend the warning signs and prepare accordingly might leave your organization vulnerable to serious threats. Thus, especially when you are new in this line of business, it’s essential to abide by security rules and regulations to keep valuable user information sound and secure.