United States: Deeper Dive: Security Is A Big Deal For Big Data

In the rapidly expanding landscape of Internet-based data analytic services, companies across all industries with a significant online presence have faced or will face a data breach resulting from their collection and use of Big Data. As more consumer information is digitized and collected by companies for data analytics, the potential for cyberattacks also increases. While businesses often find Big Data analytics valuable for research and marketing, most organizations do not have the security assets needed to keep such data safe. As can be imagined, large quantities of consolidated data can be extremely tempting for cybercriminals, especially when such data may contain a company's proprietary information or customers' personal and/or financial information. Big Data security breaches can result in serious legal consequences and reputational damage for companies, often more severe than those caused by breaches of traditional data.

Big Data Has Big Security Challenges

Big Data has several unique security challenges, of which companies unfamiliar with the complexities of Big Data analytics may not be aware. Variety, volume and velocity are the three primary terms used to characterize Big Data, and each individually contributes to the security challenges native to Big Data analytics and must be considered equally.

The first term, variety, defines the multiple classes or data types captured across a company's given enterprise. Variety is quickly becoming the single biggest driver of investments in Big Data. At any given time, a company may be collecting and/or storing data from multiple business areas (e.g., customer data, employee personal information, intellectual property) in a variety of formats. To adequately combat threat actors targeting valuable Big Data repositories, companies must fully understand all data types collected and used in their business before engaging in or contracting for Big Data service. Companies must also balance their desire to rapidly extract and analyze Big Data with the need to adequately secure such data. 

The second "v," volume, references the amount of data companies collect and use for Big Data analytics. The total amount of Big Data generated by companies and governments is expected to double every two years, reaching more than 40,000 exabytes in 2020. While an increase in the number of distinct data sources collected by a company broadens its ability to utilize various analytics, the resulting massive data collections are extremely valuable targets for threat actors. The sheer volume of these data repositories, coupled with the numerous data privacy and security regulations governing their contents, inevitably increases a company's compliance and security costs, as well as potential liability for subsequent data breaches.

Velocity, the third "v," refers to the increasing speed at which data is created, processed, stored and analyzed. Streaming data acquisition adds additional complexity to Big Data security. Companies desire real-time data analytic platforms that provide nearly instantaneous connectivity across disparate data sources. Analytic service providers have been racing not only to keep up with the growing demands of customers, but also to stay ahead of cyber criminals in their efforts to secure their Big Data offerings. However, Big Data technology providers will soon face additional challenges, as machine learning and artificial intelligence computing platforms, as well as IoT devices, are integrated into Big Data solutions.

Protecting Your Company's Big Data

While virtually all companies collect at least limited amounts of data about their customers, most organizations traditionally outsource Big Data analytics to third-party service providers. As is evident above, the legal issues related to the security of Big Data are myriad and require careful analysis before a third party is engaged to provide such services. Focusing on the unique privacy and data security issues related to Big Data at the onset of contract negotiations is far easier and less expensive than navigating its multifaceted complexities for the first time during a data security incident. Companies planning to enter into agreements for Big Data analytics and/or related services should consider, at a minimum:

  • Who owns the data collected and analyzed? Although this seems obvious, data ownership and the vendor's ability to use and/or resell a company's data may not be entirely clear. Ownership should be negotiated by the parties at the onset of the contracting process, as should the guidelines for return or destruction of such data upon termination of the applicable service agreement.
  • Who can access my data? Companies should ensure that vendors maintain robust access control policies that limit access to the company's data to vendor personnel on a need-to-know basis. Companies should also inquire whether access to their data is monitored in "real time" to reduce the risks of prolonged data breaches.
  • Is this vendor safe? Companies should also insist third-party service providers perform intermittent security audits of their internal data management systems. Awareness of cybersecurity attacks requires continuous collection and review of audit information. Companies should also require that their service providers agree to comply with industry-based security frameworks (e.g., ISO 27001, AICPA SOC2).

Each year a significant number of data security incidents are directly caused by or related to the practices of third-party providers. Our 2017 Data Security Incident Response Report provides additional guidance on steps you can take to minimize risk when managing vendors.

The collection, use and analysis of Big Data raise many issues under various state and federal laws and regulations. Therefore, before initiating or utilizing Big Data analytics, companies should understand not only what types of data they plan to collect and analyze, but also the legal obligations associated with the use of such data. Companies should also plan ahead for the complexities of integrating Big Data security protocols into their enterprise IT systems and standards, as well as into their incident response plans.

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.

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