Rethinking Insider Risk in an AI-Driven Workplace

As layoffs and AI-driven workflows reshape workplace security, insider risk is becoming more complex. Organizations must navigate shifting workforce dynamics, financial stressors and new security threats as well as ensure proactive strategies to manage insider risk, said Dan Costa, technical manager for the CERT division at Carnegie Mellon University’s Software Engineering Institute.

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Many organizations still define success in insider risk management by stopping data theft or catching bad actors. But Costa argues that a truly proactive approach focuses on addressing underlying stressors before they escalate.

“A happy outcome there is getting somebody hooked up with your credit counseling services you already offer your workforce,” Costa said. “The same with kind of managing co-worker and colleague conflicts. A positive outcome there isn’t firing somebody; it’s finding a way to improve that relationship and make sure that all individuals involved stay connected to the organization and feel supported by the organization.”

In this video interview with Information Security Media Group, Costa also discussed:

• Why organizations need an access-based approach to defining insider risk;
• How behavioral analytics and positive incentives can reduce risk;
• The role of AI in insider risk detection and where to draw the line on surveillance.

Costa leads the research and engineering efforts of the insider risk and applied network defense teams at Carnegie Mellon University, which conducts empirical research and develops solutions that enable organizations to effectively manage insider risks and advance the state of the practice in technical cybersecurity assessments. He has more than 17 years of experience in cybersecurity and software engineering. He is also an adjunct instructor in the Heinz College of Information Systems and Public Policy at Carnegie Mellon University.