Runtime Monitoring Systems for Smart Contract Integrity (Blockchain, Web3 & Digital Trust Journal p-ISSN 3117-597X and e-ISSN 3117-5988, 2025)
Pre-deployment verification catches bugs before they are deployed. But deployed contracts live in a world that verification cannot fully model -- changing market conditions, novel protocol compositions, flash loan liquidity that did not exist when the contract was audited, and governance changes that alter trust assumptions after deployment. Runtime monitoring is the last line of defence: systems that observe contract execution in real time, check it against integrity invariants, and trigger protective actions (transaction reversion, circuit breaker activation, or alert escalation) when violations are detected. I present the Runtime Monitoring Assessment Framework (RMAF), evaluating five monitoring architectures -- on-chain invariant guards, off-chain mempool surveillance, block-level anomaly detection, cross-protocol dependency monitoring, and AI-powered predictive alerting -- on a test harness that replays 28 historical DeFi exploits against monitored contracts and measures detection latency, prevention rate, false alarm rate, and gas overhead. The Monitoring Quality Score (MQS) shows that cross-protocol dependency monitoring achieves the highest overall quality (0.904) by catching exploit sequences that span multiple contracts. On-chain invariant guards achieve the fastest response (sub-transaction, before state change commits) but catch only single-contract violations.