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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.
