Cybersecurity Risk Management in Artificial Intelligence–Enabled Digital Ecosystems

Srdjan Capkun

Cybersecurity Risk Management in Artificial Intelligence–Enabled Digital Ecosystems

Author(s)	Srdjan Capkun
Country	United States
Abstract	Artificial intelligence–enabled digital ecosystems increasingly integrate machine-learning models, cloud services, application programming interfaces, connected devices, data pipelines, automated decision systems, and third-party technological components. This interconnected architecture expands organizational capability but simultaneously creates cybersecurity risks that extend beyond conventional information-system protection. Artificial intelligence introduces additional attack surfaces associated with adversarial inputs, data poisoning, model extraction, privacy inference, compromised training pipelines, unauthorized application programming interface access, and vulnerabilities within the AI supply chain. This study examines cybersecurity risk management in AI-enabled digital ecosystems through an integrated conceptual framework and a reproducible synthetic Monte Carlo simulation. Five principal risk domains are evaluated: data poisoning, adversarial input manipulation, model and privacy attacks, API and identity abuse, and AI supply-chain compromise. Ten thousand synthetic observations are generated for each risk domain using a fixed random seed, with uncertainty incorporated into likelihood, impact, and control-effectiveness parameters. The analysis compares inherent risk with residual risk following layered security controls. Simulated results show risk reductions ranging from 46.94% for AI supply-chain threats to 58.19% for API and identity abuse. Adversarial-input risk records the highest inherent score of 36.60, while layered controls reduce it to 18.28. The findings indicate that effective cybersecurity governance in AI environments requires lifecycle-based security rather than isolated model protection. Data governance, model testing, strong identity controls, secure interfaces, provenance verification, continuous monitoring, incident response, and supply-chain assurance must operate together. The study concludes that AI cybersecurity should be treated as an enterprise-wide risk-management problem in which technical robustness, information security, privacy, operational resilience, and accountable governance are integrated across the complete AI lifecycle.
Keywords	artificial intelligence, cybersecurity risk management, adversarial machine learning, data poisoning, model security, digital ecosystems, AI supply chain, cyber resilience
Field	Engineering
Published In	Volume 1, Issue 3, May-June 2019
Published On	2019-06-11

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Cybersecurity Risk Management in Artificial Intelligence–Enabled Digital Ecosystems

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