Security risks

Identifying Potential Security Risks and Taking Steps To Protect Your Data With VDR

With a virtual data room, you can set up a highly secure collaborative workspace for your project in just a few minutes. This article will explain how businesses can apply this digital platform to identify potential security risks.

A risk-based approach to corporate information security

With the development of information technologies, the automation of business processes, and the transition to electronic document management at enterprises, the value of information stored in electronic form is growing. Therefore the issue of investments in information protection technologies is becoming increasingly acute.

Risks for information security can be divided into external threats from outside the organization and internal threats from within the organization. External threats include, for example, viruses and other malicious programs introduced into an organization’s network through e-mail or web browsing, hacking attacks, and social engineering by third parties. Insider threats include sabotage or fraud affecting information systems by company employees, vendors, or contractors, as well as errors and negligence, including failure to follow established procedures. Therefore, implementing an effective security program can be very expensive, especially for large organizations, due to the need to purchase hardware and software to implement technical security measures and personnel to manage security’s administrative and technical aspects. Therefore, security management’s task is to maintain an appropriate balance between the cost of implemented security controls and the risks associated with protected data and systems. In this case, a virtual data room (VDR) is the best solution. It is a state-of-the-art cloud-based data management platform tailored to businesses in any industry. It is especially widely used in M&A deals, IPOs transactions, fundraising, startups, etc. Because during such confidential transactions, large arrays of sensitive data are processed.

How to protect business data with the VDR: simple steps

From high-speed transaction processing to predictive analytics, data rooms have been around for decades and have become the de facto storage standard for business management at the corporate level.

The primary trend in the VDR market for building data warehouses is the concept of logical storage. It covers repositories, data integration/virtualization technologies, and distributed processing methods through which ordinary users can access consolidated data extracted from various sources without the need to contact IT specialists or developers. The database that is interacted with in the data room system is relational; thus, the data is structured – stored in tables consisting of columns and rows. Such tables are organized according to the schema determined at the recording time. The logistics of collecting data from various business units to extract helpful information can scale as the business grows. With data warehouses, companies can securely consolidate this information into a single database and data model. The modern market offers a dozen of VDR vendors. Among them are Merill datasite, Intralinks, iDeals, Ansarada, etc.

Typical areas of application for data rooms comprise:

  • Online transaction processing: a data room warehouse can be optimized for data integrity and high query speed to handle a large volume of short data transactions. An example is transactions that are executed on a high-frequency trading platform.
  • Online analytical processing: businesses can optimize their data repositories to speed up complex queries with a relatively small amount of transactions. Typically, analysts use the VDR to generate business intelligence reports.
  • Analysis of security risks: the system can be optimized to predict future events and create what-if scenarios for a company – in many cases, using machine learning algorithms.

The data room provides the data management needed to streamline data sharing across multiple endpoints. This way, the software consolidates data lakes and warehouses into a single access layer. Data processing is abstracted from the data hub, so organizations can centrally extract and collaborate on essential business data.