Big Data Security

Big Data Security

The need to collect, store, share, analyse and retrieve valuable data from the rising amounts of massive data every minute for devising vital business strategies and pacing ahead in the competitive business environment has caused the evolution of Big Data technology. As more and more industries are understanding the potential of this technology in delivering valuable insights, more and more businesses are mulling to implement Big Data projects. 

By effectively dealing with the volume, variety, veracity, variability and value aspects of data, scientists, product managers, executives, marketers and various other decision making authorities are crafting more accurate plans. Moreover, they are discovering new opportunities to tap new avenues of business and are bringing in more innovative products and services for expanding their outreach.

But Big Data projects vary from enterprise to enterprise and their implementation must undergo certain process to ensure that it is fulfilling its purpose. Security or data encryption is one of the major determinants, which contribute to a successful Big Data project. However, any lapse on Big Data security part can rope in big issues. In order to establish appropriate Big Data security, following areas need to be tackled with:-

1. Sources Of Data Collection

Structured data in the range of heterogeneous databases and applications along with unstructured data in different file types are being harnessed by organisations to fully utilise the Big Data technology. Sources like photos, videos, enterprise resource planning systems, social media content or feeds, customer relationship management platforms, etc. can make way through the process of collecting data. With increasing diversity in data and their sourcing patterns, confidential data like payment card details, personally identifiable information, health records, etc. get compiled, thus claiming their need for security. This is why, the compiled data sources must be secured to be in line with the security policies. 

2. Big Data Frameworks

At any given time, the Big Data environment encompasses the processing of huge volumes of sensitive data via Hadoop, NoSQL, Teradata or MongoDB. Since these sensitive data may find a place in Big Data nodes, system logs, error logs, configuration files, etc., the Big Data security gets challenged.

3. Big Data Output In The Form Of Analytics

The whole Big Data project implementation demands an output in the form of analytics, which has the potential to aid businesses in optimising their processes and stimulate innovative work systems. This critical business information can be presented in the form of reports, while on-demand queries offer instant access to this data. Such information delivered by Big Data analytics have high scope of comprising sensitive data such as major business moves or strategies for product launches. Exposure to this kind of sensitive business asset to various people who have easy access to the systems can pose a tough challenge to organisations.

While it is pertinent to make Big Data technology attain the wider organisational goals, but at the same time it is accountable to make it valuable for the general public whose information is being assessed. Effective security measures must be imbibed in Big Data techniques and systems, which are for any business, a crucial and challenging task.

Many enterprises depend upon the cloud-based services and platforms for operating on their Big Data projects and this is why, the onus of managing Big Data security is more difficult for them. Threats and thefts of sensitive data via cloud computing can put any business at big risk.

How Traditional Encryption Approaches Fail To Meet Big Data Security Requirements?

There are various encryption services available and the biggest challenge to Big Data encryption is the choice to be made between these services. In case, you opt for implementing transparent data encryption capabilities from your database provider, still your data will be at a risk when it gets exported into Big Data environments where various other data sources amalgamate and other systems operate. 

Some vendors even offer Big Data encryption capabilities, but that are limited only to specific data nodes and the security doesn’t cover the original data sources or the analytics or the log files and configuration information. 

How COSO Is Endeavouring To Offer Secured Big Data Environments?

COSO highly prioritises the maximisation of the benefits of Big Data for organisations while imposing stringent security measures. They are committed to offer advanced security solutions for the sensitive data and addresses the requirements of the compliance office to offer best value for enterprises for investing their time, money and efforts in deriving benefits from Big Data.

Big Data Security With Identity & Access Management (IAM)

The Identity & Access Management (IAM) can be defined as a set of system framework that has been designed to allow the management of electronic identities and includes the technology required for supporting identity management. This technology works effectively in initiating, capturing, recording and managing identities of users and their access permissions automatically. Therefore, as per the interpretation of policy made for granting access privileges, every individual and service is appropriately authenticated, authorised and audited.

Potential Of IAM For Organisations With Big Data Projects

The implementation of IAM doesn’t have a direct impact on the growth or profitability aspects of a business. But their non-implementation can imply lack of efficiency on management of user access and identities, which can raise questions of the company’s credibility for risking the security of sensitive data. 

What has always remained a challenge for IT administrators is keeping the desired flow of data work going amidst monitoring the access protocols to this data. Since the IT environment in every business will find increasing tasks with higher complexity due to the advancement in technology, there will be more such security challenges evolving owing to greater use of cloud computing, mobile apps and rising mobile workforce. With increasing number of devices and services that need management, the security enforcement techniques must also improve with the same pace.

Within an organisation, when employees seek a different role, then managing their access permissions may become difficult. In addition, managing requests requiring access like other employees other than specific access can result in accumulation of privileges called privilege creep. This privilege creep is expected to endanger the security of data in two ways – First, employees having privileges more than the permitted ones may access the data and applications being an unauthorised individual and second, if a third person gets access of an employee with many privileges, then there are high chances of data misuse, data loss or potential threat to the working of the whole Big Data system.

It has been noted that accumulation privileges are not much beneficial for the employee or the enterprise. But this in fact, makes easier for people with intentions to manipulate data or the system to get access from an employee having excess privileges. Also, an inefficient Identity Access Management also overlooks the fact that employees retain access to the crucial business data even after leaving the organisation.

Federated Identity Management (FIM)

The enterprises and network subscribers can derive economic advantages and convenience owing to FIM. For the effectiveness of the FIM, trust between partners is essential as authorisation messages get transmitted among partners through this system. The messages are sent in Security Assertion Markup Language (SAML) or a similar XML standard to allow users to access for the affiliated but different networks or websites.  

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