Dataversity: Cybersecurity Data Science – Minding the Growing Gap

March 16, 2019


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Following cybersecurity Data Science best practices can help beleaguered and resource-strapped security teams transform Big Data into smart data for better anomaly detection and enterprise protection.

Future Shock: Growing Vulnerabilities and Liabilities

The consequences of ignoring security challenges are rising. According to the Cisco 2018 Annual Cybersecurity Report, over half of cyberattacks resulted in damages of greater than $500K, with nearly 20 percent costing more than $2.5M. Meanwhile regulators, seeking to spur heightened oversight, have become more aggressive in levying fines and holding corporate boards accountable.

A rapidly developing field, Cybersecurity Data Science (CSDS) brings hope to organizations challenged by evolving cyber threats. CSDS utilizes advanced analytics to address common security challenges – data overload, limited resources, overabundant false alerts, and more – in an increasingly data-driven, interconnected world.

Cybersecurity Data Science in a Nutshell

CSDS offers a practical path forward for organizations besieged by unknown-unknowns. The discipline unites a range of analytical methods to achieve cybersecurity monitoring, detection, and prevention goals. When operationalized, the result is an end-to-end organizational process orchestrating people, methods, and technologies.

Cybersecurity Data Science (CDSD) drives value through:

  • Aligning data engineering objectives.
  • Refining fast and big data into “smart data.”
  • Orchestrating a cyclical process of discovery and detection.
  • Facilitating the development of analytical models for pattern extraction and event detection.
  • Leveraging data analytics tools and methods to produce targeted, evidence-based alerts.
  • Routing focused incidents to the right resources at the right time for rapid review and remediation.

See full article on Dataversity 

About SARK7

Scott Allen Mongeau (@SARK7), an INFORMS Certified Analytics Professional (CAP), is a researcher, lecturer, and consulting Data Scientist. Scott has over 30 years of project-focused experience in data analytics across a range of industries, including IT, biotech, pharma, materials, insurance, law enforcement, financial services, and start-ups. Scott is a part-time lecturer and PhD (abd) researcher at Nyenrode Business University on the topic of data science. He holds a Global Executive MBA (OneMBA) and Masters in Financial Management from Erasmus Rotterdam School of Management (RSM). He has a Certificate in Finance from University of California at Berkeley Extension, a MA in Communication from the University of Texas at Austin, and a Graduate Degree (GD) in Applied Information Systems Management from the Royal Melbourne Institute of Technology (RMIT). He holds a BPhil from Miami University of Ohio. Having lived and worked in a number of countries, Scott is a dual American and Dutch citizen. He may be contacted at: LinkedIn: Twitter: @sark7 Blog: Web: All posts are copyright © 2020 SARK7 All external materials utilized imply no ownership rights and are presented purely for educational purposes.

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