Cases of privacy violations and data breaches have become more common in recent years. Sergey Kondratenko, an expert in the field of fintech, says that companies that process significant volumes of confidential information are increasingly faced with security threats. This is despite the introduction of consumer data protection laws such as GDPR.

Big Data itself poses potential threats to data security, but the source of problems is most often ineffective information management. No privacy law can fully compensate for poor data governance.

Sergey Kondratenko is a recognized specialist in a wide range of e-commerce services with experience for many years. Now, Sergey is the owner and leader of a group of companies engaged not only in different segments of e-commerce, but also successfully operating in different jurisdictions, represented on all continents of the world. The main goal is to drive new traffic, create and deliver an online experience that will endear users to the brand, and turn visitors into customers while maximizing overall profitability of the online business.

Big Data Security Issues

Sergey Kondratenko highlights several key security issues in the field of Big Data, which can impact both local platforms and cloud solutions. These issues require attention and collaboration with cloud service providers to ensure strong data security.

  1. Vulnerability of new technologies. The introduction of advanced analytics tools for unstructured data and NoSQL databases presents new security challenges. These tools may be less resistant to attacks, so ensuring their security may be difficult.
  2. Variable impact. Mature security controls can protect data entry and storage. However, they may have different impacts on the output and distribution of data from different analytical tools.
  3. Access without permission. Big Data administrators may be able to access data without permission or notice. According to Sergey Kondratenko, this poses a threat to data confidentiality, and security tools should monitor and warn about suspicious access.
  4. Difficulties in security auditing. Large volumes of data, especially on clustered platforms, make regular security audits a challenging task. Vulnerabilities can exist across multiple hosts and servers, requiring a more comprehensive approach to security.
  5. Constant updates.Regular security updates for your big data environment are a must, and failure to do so can increase the risk of leaks and other threats.

Addressing these issues requires close monitoring, adherence to data security best practices, and collaboration with cloud service providers.

Sergey Kondratenko: How can Big Data confidentiality work in favor of the company and clients?

Sergey Kondratenko believes that to ensure data security in the field of Big Data, it is recommended to follow several important strategies:

  • Use real-time monitoring. For better data protection, it is important to have a real-time monitoring system. It allows you to quickly identify and respond to potential threats and data leaks, enabling more effective deployment of data protection strategies.
  • Implement homomorphic encryption. Homomorphic encryption is a powerful tool for processing data without decrypting it. This allows you to store and process information in the cloud without disclosing it to external providers. This technology helps protect data privacy even in third-party processing environments.
  • Avoid excessive data collection. Collecting large amounts of data that is not necessary can increase privacy risks.

– Organizations should only collect data that is truly necessary to achieve their goals. Information such as social security or customer logins can be excluded from collection to reduce risks and better protect customer privacy, says Sergey Kondratenko.

The specialist advice following these strategies; they will help strengthen data security in the field of Big Data and reduce the risks of leaks, and therefore privacy violations.

Sergey Kondratenko: How to get rid of internal threats in Big Data?

Preventing insider threats to data privacy is critical to Big Data security. Sergey Kondratenko suggests paying attention to several steps that organizations can take to reduce the risks associated with insider threats:

  1. Employee training. Data security training should be provided to all employees at all levels of the organization. It should include basic security measures (regularly changing passwords) as well as more advanced aspects such as recognizing phishing attacks and insider threats.
  2. Multi-factor authentication (MFA). It is recommended that you implement and use multi-factor authentication to access systems and data. This provides an additional layer of protection even if an employee’s password is compromised.
  3. Access limitation. Sergey Kondratenko is convinced that it is important to establish strict data access policies. In his opinion, it is necessary to minimize the number of employees with access to confidential data. At the same time, it is necessary to convey to the company’s specialists that such a measure is not a manifestation of mistrust. It is simply inevitable for saving data in the world of a huge flow of information that can penetrate through various, even the most unexpected, sources.
  4. Monitoring and audit. Implementation of a monitoring and audit system that will allow you to track employee actions and detect suspicious activity. This will help identify threats at an early stage.
  5. Frequent password changes. Employees should be trained to follow a policy of frequently changing passwords and using complex password structures. This reduces the risk of data leakage due to weak passwords.
  6. Blocking access from unused devices. You should always ensure that employees log off when finishing work on unused computers and devices.
  7. Managing permissions and safety culture.

– Employee access permissions must be reviewed and updated regularly. At the same time, managers or those responsible for security in fintech companies must ensure that a culture of confidentiality is maintained in accordance with secure practices, Sergey Kondratenko says.

He believes that preventing insider threats is a key component of data security in the Big Data industry. Therefore, active attention to these measures can significantly reduce risks.

Big Data Privacy Tools: What should you pay attention to?

  • Cloud compatible. A key factor when choosing a big data privacy tool is its compatibility with cloud solutions. If a tool is limited to running only on physical servers or computers, it is likely an outdated solution that cannot effectively handle today’s privacy and Big Data security challenges.
  • User-friendly design. Sergey Kondratenko explains that integrating a data privacy tool is a collaborative process. Therefore, it is important that the tool is easy to use at all levels of the organization. Finding a tool that is intuitive and easy to use builds trust and makes it easier to implement within your team.
  • Automation. Manual data processing is great, but often impossible when dealing with large volumes of information. Therefore, choosing a tool that uses machine learning allows you to automate and optimize data processing. It will most effectively protect your privacy and allow you to focus on making decisions based on reliable data.

Large volumes of data are a valuable asset, but if not managed correctly, they can pose a threat to privacy. Sergey Kondratenko believes that the priority of security in a fintech company makes Big Data not only a valuable resource, but also a powerful tool. The specialist is convinced that the use of big data in the right way allows organizations to better understand their customers, develop effective strategies, and make informed decisions at all levels.