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The Role of Big Data in Security

Using BigData for Enterprise Security

While Security Monitoring Generates Big Data, in its Raw Form it Remains Only a Means to an End…

Using BigData for Enterprise Security

While Security Monitoring Generates Big Data, in its Raw Form it Remains Only a Means to an End…

The volume of data required for information security to effectively detect advanced attacks, and at the same time, support new business initiatives is growing exponentially. Meanwhile, security operations staff is often required to piece together data from different sources, connect the dots, and detect suspicious patterns that would indicate a cyber-attack or data breach. Relying on manual processes to comb through mountains of logs is one of the main reasons that critical issues are not being addressed in a timely fashion. This explains why “Big Data in Security” is often considered a curse, despite its potentially powerful role within enterprise security.

There is a lot of hype surrounding Big Data in security. While it is often used in the context of boosting an organization’s revenue, Big Data represents a huge challenge for security practitioners. Mushrooming regulations (e.g., PCI DSS 3.0, NIST, FISMA, etc.) that mandate more frequent security posture assessments and mounting cyber-attacks are making matters worse. According to Gartner (see Information Security Is Becoming a Big Data Analytics Problem, written by Neil MacDonald, March 2012) “the amount of data analyzed by enterprise information security organizations will double every year through 2016. By 2016, 40% of enterprises will actively analyze at least 10 terabytes of data for information security intelligence, up from less than 3% in 2011.”

To ensure proper coverage, many organizations are relying on multiple, best-of-bread, silo-based tools (e.g., fraud and data loss prevention, vulnerability management, or SIEM) to produce the necessary security data. This only adds to the volume, velocity, and complexity of data feeds that must be analyzed, normalized, and prioritized. Unlike adaptive authentication, which is being used to automate behavioral pattern analysis for fraud prevention in the payments industry, many commonly used security tools lack the capability to provide self-analysis. The scale of security data that needs analysis has simply become too big and complex to handle. It is now taking months and even years to piece together an actionable picture.

Security Data InformationUnfortunately, relying on manual processes to comb through mountains of logs is one of the main reasons that critical issues are not being addressed in a timely fashion. According to the Verizon 2013 Data Breach Investigations Report, 69% of breaches were discovered by a third party and not through internal resources.

At the end of the day, the ultimate goal is to shorten the window attackers have to exploit a software or network configuration flaw. Big data sets can assist in putting specific behavior into context, but there are some real technological challenges to overcome. Traditional security tools operate in a silo and where not designed to also take business criticality into account to help prioritize remediation actions when dealing with huge data sets. This raises the question, how can organizations take advantage of big security data without having to hire a legion of new employees?

While security monitoring generates big data, in its raw form it remains only a means to an end. Ultimately, information security decision making should be based on prioritized, actionable insight derived from the data. To achieve this, big security data needs to be correlated with its business criticality or risk to the organization. Without a risk-based approach to security, organizations can waste valuable IT resources mitigating vulnerabilities that in reality pose little or no threat to the business. Furthermore, big security data needs to be filtered to just the information that is relevant to specific stakeholders’ roles and responsibilities. Not everyone has the same needs and objectives when it comes to leveraging big data.

To deal with big security data and achieve continuous diagnostics, progressive organizations are leveraging Big Data Risk Management systems to automate many manual, labor-intensive tasks. These systems take a preventive, pro-active approach by interconnecting otherwise silo-based security and IT tools and continuously correlating and assessing the data they generate. In turn, this enables organizations to achieve a closed-loop, automated remediation process, which is based on risk. This results in tremendous time and costs savings, increased accuracy, shorten remediation cycles, and overall improved operational efficiency.

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Big Data Risk Management systems empower organizations to make threats and vulnerabilities visible and actionable, while enabling them to prioritize and address high risk security exposures before breaches occur. Ultimately, they can protect against and minimize the consequences of cyber-attacks.

Written By

Torsten George is a cybersecurity evangelist at Absolute Software, which helps organizations establish resilient security controls on endpoints. He also serves as strategic advisory board member at vulnerability risk management software vendor, NopSec. He is an internationally recognized IT security expert, author, and speaker. Torsten has been part of the global IT security community for more than 27 years and regularly provides commentary and publishes articles on data breaches, insider threats, compliance frameworks, and IT security best practices. He is also the co-author of the Zero Trust Privilege For Dummies book. Torsten has held executive level positions with Centrify, RiskSense, RiskVision (acquired by Resolver, Inc.), ActivIdentity (acquired by HID® Global, an ASSA ABLOY™ Group brand), Digital Link, and Everdream Corporation (acquired by Dell).

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