November 17, 2016 By Gregg Barrow 3 min read

Big data has become a critical business tool and a transformative force for enterprises across multiple industries and geographies. Vast amounts of data are now organized, available and ready to be analyzed, leading to advanced tactics and strategies that were previously impossible.

But prior to adopting a big data and analytics solution, business leaders should answer a few fundamental questions: How will big data solutions affect my organization’s security profile? What governance is needed? Are my existing technology solutions sufficient?

Big Data Solutions: Handy Tools and Juicy Targets

Data proliferation has led to greater amounts of data passing through networks. Through big data solutions, organizations can aggregate, index and analyze many types of data. These solutions allow organizations to find patterns and correlations in the data that can potentially reveal new business insights.

The ability to consume and process this data makes big data solutions appealing to many organizations. However, what makes these solutions attractive to business leaders also makes them attractive to bad actors. Think of big data as a digital library that provides organizations with an index to easily locate and access files. If a cybercriminal were to gain access to this index, he or she would have a direct line to the organization’s most sensitive information.

Big data environments are tempting targets, and defending them puts additional stress on the security personnel and systems tasked with data protection. In addition, the exponential growth of data is leading to challenges beyond security, including governance issues related to data accuracy, accessibility, completeness and consistency. Organizations can avoid feeling overwhelmed when implementing a big data solution by effectively managing and protecting their environments with an integrated governance and technology strategy.

Governance and Data Reservoirs

With respect to governance, big data solutions call for an agile approach to profiling and understanding data as it is ingested. This enables organizations to implement appropriate controls as the data is profiled without inhibiting the speed and flexibility of technologies.

Data lakes, for example, present a unique security challenge since they allow organizations to access and process many types of data within a distributed environment. To address these challenges, organizations can utilize enhanced, agile governance to better organize data lakes, creating what is known as a data reservoir.

Within a data reservoir, organizations ensure that data is properly cataloged and protected as it is ingested by the data lake. To do so, a data owner classifies the information sources that feed the reservoir and determines how the data should be managed, including access control, quality control, masking of sensitive data and data retention periods. No data should enter the reservoir without being cataloged upfront, which enables the immediate application of appropriate security controls. This agile governance approach should be applied across all big data solutions.

Technology Considerations

From a technology standpoint, organizations should leverage existing platforms where possible and supplement with additional tools as required. At a minimum, organizations should consider coverage of the following areas:

  • Configuration and vulnerability management: Are traditional security tools sufficient to protect and secure the data?
  • Identity and access management (IAM): Are the requests for sensitive information authorized and valid?
  • Network traffic encryption: Are attackers able to intercept and access the data in motion?
  • Metadata management: Is your metadata sufficient to let you know where and how that information came into existence? Is your data usable?
  • Encryption and masking for structured data and redaction for unstructured data: Are the sensitive information assets protected from unprivileged users?
  • Data activity monitoring: Are there unusual error patterns indicating a possible attack?
  • Blocking and prevention: Are there new requests for analysis that were not scheduled or known?

The effort to strike the right balance of governance and technology is a continuous process and will be unique to each organization. However, by focusing first on governance and fundamental security components, an enterprise will be well on its way to securing its big data solution.

Read the solution brief: Top tips for Big Data Security

More from Data Protection

Addressing growing concerns about cybersecurity in manufacturing

4 min read - Manufacturing has become increasingly reliant on modern technology, including industrial control systems (ICS), Internet of Things (IoT) devices and operational technology (OT). While these innovations boost productivity and streamline operations, they’ve vastly expanded the cyberattack surface.According to the 2024 IBM Cost of a Data Breach report, the average total cost of a data breach in the industrial sector was $5.56 million. This reflects an 18% increase for the sector compared to 2023.Apparently, the data being stored in industrial control systems is…

3 proven use cases for AI in preventative cybersecurity

3 min read - IBM’s Cost of a Data Breach Report 2024 highlights a ground-breaking finding: The application of AI-powered automation in prevention has saved organizations an average of $2.2 million.Enterprises have been using AI for years in detection, investigation and response. However, as attack surfaces expand, security leaders must adopt a more proactive stance.Here are three ways how AI is helping to make that possible:1. Attack surface management: Proactive defense with AIIncreased complexity and interconnectedness are a growing headache for security teams, and…

What NIST’s post-quantum cryptography standards mean for data security

2 min read - Data security is the cornerstone of every business operation. Today, the security of sensitive data and communication depends on traditional cryptography methods, such as the RSA algorithm. While such algorithms secure against today’s threats, organizations must continue to look forward and begin to prepare against upcoming risk factors.The National Institute of Standards and Technology (NIST) published its first set of post-quantum cryptography (PQC) standards. This landmark announcement is an important marker in the modern cybersecurity landscape, cementing the indeterminate future…

Topic updates

Get email updates and stay ahead of the latest threats to the security landscape, thought leadership and research.
Subscribe today