Kayode Lateef Ogunsusi, American Express, USA

The emergence of autonomous artificial intelligence (AI) agents in digital commerce introduces cybersecurity risks involving delegated authority, agent impersonation, compromised agents, malicious delegation, and unauthorized payment execution. This study proposes the Autonomous Commerce Agent Guard System (ACAGS), a behavior-aware cybersecurity framework integrating supervised machine learning, historical behavioral analysis, agent-specific authorization controls, and explainable AI for payment authorization. ACAGS was evaluated using a large-scale synthetic financial transaction dataset extended with a simulated agentic-commerce layer representing agent identity, delegated spending limits, authorization scope, trust characteristics, and five adversarial scenarios. XGBoost was selected as the primary classifier following comparative evaluation of four supervised learning models. Across 10 experimental runs, behavioral features increased mean F1-score from 52.12% to 67.49%, while the addition of agent-specific features further increased it to 69.46%. ACAGS detected 99.77% of abnormal agent behavior, 99.73% of agent impersonation, 100% of malicious delegation, and 100% of scope violations, while compromised-agent detection reached 72.27%. The authorization policy translated fraud and agent-policy risk into APPROVE, REVIEW, and REJECT outcomes, while SHAP provided interpretable fraud-risk explanations. These findings provide controlled experimental evidence that combining behavioral fraud detection with delegated-agent authorization controls can strengthen security assessment in simulated agentic-commerce payment environments, while validation using operational agent-initiated transactions remains necessary.

Agentic Commerce, Cybersecurity, Fraud Detection, Payment Authorization, Autonomous AI Agents, Explainable AI, Behavioral Analytics, Machine Learning.