Zorina works with global companies to find solutions that speed up operations and enhance processes using Artificial Intelligence and Machine Learning. Zorina is also an Adjunct Professor at Georgetown University SCS, as a creator and instructor for the “AI for Leaders” courses within the Project Management Masters program.Zorina is involved in AI for Good initiatives and working with non-profit organizations to address major social and environmental challenges using AI/ML. She also volunteers with the Zonta organization and as the Chair of the Artificial Intelligence Committee at AnitaB.org.

Network Centrality, Adversarial Trading, and Crisis Deterrence: Evidence from a Multi-Agent Market Crisis Simulation What would happen if AI agents were given real autonomy to trade during a financial crisis?This paper takes a first step toward answering that question. We investigate whether graph-aware defensive market-making agents can neutralise the destabilising effects of adversarialtraders who exploit a financial contagion network’s topology, using a multi-agent simulationplatform calibrated to the March 2023 Silicon Valley Bank (SVB) crisis. By observing howAI agents — adversarial, defensive, and neutral — behave autonomously across 200 MonteCarlo iterations, we generate empirical evidence about their emergent strategies, failuremodes, and mutual deterrence dynamics. We design two experiments: a competition amongthree graph exploiters against a single centrality-weighted defender, and a confrontation inwhich a single graph exploiter faces a coalition of three specialist defenders. Each scenarioruns 100 stochastic iterations of 100 discrete time steps. Our results show that the three-defender coalition reduces mean realised volatility by 90.6% (from 0.228% to 0.021%;Welch’s t = 10.00, p < 0.001, Cohen’s d = 1.44) and mean price drawdown by 86.7%(Welch’s t = 7.61, p < 0.001, d = 1.11). Market activity collapses by 97.6% (from 19.0 to 0.45mean trades per iteration), driven by a 51.6% widening of bid-ask spreads. All three defenderstrategies record zero realised PnL, revealing a deterrence mechanism: defenders neutraliseadversaries not through active counter-trading but by raising the cost of adversarial executionbeyond viability. We term this the deterrence paradox: the most effective defence is one thatis never invoked. Critically, all adversarial agents are loss-making even without dedicateddefence, demonstrating that graph-aware exploitation is self-defeating under realistictransaction costs — an important behavioural finding for AI safety in financial markets.These results contribute to the project of understanding AI agent behaviour well enough todeploy such agents safely, transparently, and with predictable outcomes in real financialcrises.