Social/Ethical Issues in Predictive Insider Threat Monitoring

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Author(s): Frank L. Greitzer (Pacific Northwest National Laboratory, USA), Deborah Frincke (Pacific Northwest National Laboratory, USA)and Mariah Zabriskie (Pacific Northwest National Laboratory, USA)
Copyright: 2011
Pages: 30
Source title: Information Assurance and Security Ethics in Complex Systems: Interdisciplinary Perspectives
Source Author(s)/Editor(s): Melissa Jane Dark (Purdue University, USA)
DOI: 10.4018/978-1-61692-245-0.ch007

Keywords: Information Science Reference / Information Security & Privacy / IT Security and Ethics / Security & Forensics

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Abstract

Combining traditionally monitored cybersecurity data with other kinds of organizational data is one option for inferring the motivations of individuals, which may in turn allow early prediction and mitigation of insider threats. While unproven, some researchers believe that this combination of data may yield better results than either cybersecurity or organizational data would in isolation. However, this nontraditional approach yields inevitable conflicts between security interests of the organization and privacy interests of individuals. There are many facets to debate. Should warning signs of a potential malicious insider be addressed before a malicious event has occurred to prevent harm to the organization and discourage the insider from violating the organization’s rules? Would intervention violate employee trust or legal guidelines? What about the possibilities of misuse? Predictive approaches cannot be validated a priori; false accusations may harm the career of the accused; and collection/monitoring of certain types of data may adversely affect employee morale. In this chapter, we explore some of the social and ethical issues stemming from predictive insider threat monitoring and discuss ways that a predictive modeling approach brings to the forefront social and ethical issues that should be considered and resolved by stakeholders and communities of interest.

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Social/Ethical Issues in Predictive Insider Threat Monitoring

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