Protecting Big Data Systems Against Cybercriminals

Data is a major asset for businesses in today’s digital world. Organizations are adopting modern technologies for data collection and processing. Big data systems are being leveraged to transform industries, improve business operations and predict trends. However, lack of a robust data security can open vulnerabilities that hackers can disrupt.

Big data security encompasses every security measure and tool used in analytics and data processes. Cyberattacks on big data systems, including ransomware, DDoS and other malicious activities, emerge from online as well as offline spheres. For organizations that store confidential data, the repercussions of information theft are huge. It is necessary for organizations to have a thorough understanding of ways to protect their big data systems against cybercriminals.

Major Areas to be Focused On

Hackers continue to find ways to infiltrate big data systems and steal valuable data. Business leaders must remain proactive, rather than being reactive, in case of data security breaches. These are some key areas that organizations must focus on to reduce their big data security risks.

Distributed Data

The data processing tasks are distributed by big data frameworks across several systems for rapid analysis. These distributed systems avoid creating a single point of failure and balance the load. However, these systems are highly vulnerable to security threats and hackers can infiltrate the entire network by attacking just one system. Security measures such as intrusion prevention systems enable the security team of an organization to safeguard their big data platforms against such threats via network traffic examination. In addition, encryption tools can be used to operate on various big data storage formats.

Endpoint Vulnerabilities

The data on endpoint devices can be manipulated by cybercriminals and false data can be transmitted to data lakes. It is necessary that organizations adopt security solutions that perform analysis on logs from endpoints and validate the legitimacy of the endpoints. Fraud detection technologies enable organizations to prevent such vulnerabilities. These keep hackers from accessing systems and disrupt malfunctions in processes.

Cloud Security

Data created by big data systems are usually stored on the cloud, which are potentially at risk. Inaccurate encryption of the data and improper security measures make it easier for hackers to access sensitive data, In order to fix these issues, organizations must encrypt every sensitive data and implement IPS for detection of network intruders. In addition, organizations can implement multi-factor authentication along with biometric data and passwords. These authentication mechanisms will help organizations protect the sensitive data against cyberattacks.

To Sum Up

Most organizations are leveraging big data analytics tools for improving business strategies and this has opened possibilities for cybercriminals to infiltrate the big data architecture. The list of big data security issues continues to grow, which have given rise to several government regulations and privacy concerns regarding big data systems.

In the world of modern businesses, data is the fuel while the challenge lies in consistent access to the right data at the right time. Meanwhile, organizations must see that their data remains kept from falling into wrong hands. Organizations across the globe are continuously searching for a balance between data agility and self service analytics. All this while taking consistent actions to reduce risks to their big data systems.

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Shared Responsibility Model in Azure – Why Is It Important?

As the industry footprint and customer database of Azure expand, the leading cloud technology provider is exploring possibilities that the cloud can benefit core IT functions. However, businesses are at the risk of data breaches and other security issues whenever they implement new cloud services. This has led to the requirement for a stringent process and delineation of accountable incidents. Shared responsibility model has emerged as an effective solution to well-managed security.

Microsoft understands how different service models influence ways in which responsibilities are shared among customers and CSPs. These contribute to achieving a secure and compliant computing environment in Azure cloud. The customer is responsible for ensuring that data classification is done right, and that solutions are compliant with regulatory obligations. On the other hand, CSPs are responsible for physical security.

Shared Responsibility Model for Azure Cloud

It is vital that customers considering public cloud services understand how security tasks are shared between them and their CSPs. Workload responsibilities differ on the basis of where it is hosted – SaaS, IaaS, PaaS, or on-premises.

How Security is Divided

Security controls have been designed for ensuring that technology solutions are developed and maintained to make security and function coexist successfully. This is a strong ideal of Azure, where constant monitoring and vetting the implementation of security controls is imperative. Also, the service teams of Azure are continuously putting efforts to innovate new functionalities in the cloud. 

In the cloud transformation journey, it is important that responsibilities of cloud customers are enunciated to ensure proper data classification. Customers are responsible for securing the way their employees interact with their cloud environment. CSPs are held accountable for infrastructure security. Organizations are generally responsible for data encryption, site-to-cloud traffic, identity & access management and configuration of storage containers.

Obligations of an organization might change based on the service being used – IaaS, PaaS or SaaS – as they all need time and resources of both the CSP and organizations. This particularly amounts to organizations that assume complete legal responsibility around their sensitive data. On the other hand, organizations are capable of dedicating their resources and energy to ensure data integrity and protection, provided that Azure is obligated to protect their infrastructure integrity.

Importance of Shared Responsibility Model

Gartner estimates that the public cloud market will grow 17% in 2020 and expectations related to outcomes with cloud investments will also be higher. Businesses are earnestly investing into the value of the cloud, whether it is private, public or a hybrid architecture. Organizations continue to gain unparalleled agility in their IT operations, which has helped them maintain their edge in the competition.

In their digital transformation journey, many organizations are forgetting their responsibility of data protection stored off-site. This absentmindedness results in risks to storage components, making them vulnerable to cyberattacks. The problem lies in the futile aims of organizations to get the bare minimum of their cloud migration strategies.

To sump up, data security in cloud computing is not a myth. Organizations accomplishing security will dedicate their success to security best practices and compliance with the shared responsibility model.

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