Ethical AI needs to thrive in SecOps: 3 key guidelines

Security operations centers (SOCs) increasingly rely on network data flows as they collect telemetry from devices and monitor user behaviors. To make these massive data flows manageable, SOCs turn to rules, machine learning, and artificial (or augmented) intelligence to triage, de-duplicate, and add context to the alerts about potential dangerous or malicious activity. Pushing the boundaries of what machine learning can deliver when nourished by massive data has already led to significant invasions of privacy, especially when the efforts are driven by business demands. More often than not, ethics has taken a back seat when applying machine learning and AI. Companies such as ClearView AI and Cambridge Analytica have vastly overreached in their analysis of consumer data because they could, using consumer data without explicit permission and offering nothing in return.