Summary
Keeping computer systems safe is hard because they rely on data and automated decisions that can be tampered with or fail. The authors bring together studies that show how blockchain technology, which records information in a secure and unchangeable way, can help make AI systems more trustworthy. They look at different challenges like protecting training data, spotting problems quickly, and fixing software automatically. Their work suggests building layered systems that use blockchain to track data and models, alongside AI agents that monitor and fix issues without relying on a central authority. However, they also note there are still open problems before these ideas work well in the real world.
What this means in practice
- •For cloud security teams: Use blockchain-anchored AI methods to detect and respond to anomalies in cloud environments without relying on a single trusted party.
- •For software development teams: Coordinate multi-agent AI systems governed by smart contracts to automate vulnerability patching securely and transparently.
A position paper. It proposes an approach and reports no results.
Abstract
Blockchain and artificial intelligence (AI) are converging into a single infrastructural layer for securing data sharing, model integrity, and autonomous decision-making across distributed systems. This paper presents a meta-synthesis that draws together four constituent studies covering adversarial machine learning, AI-powered anomaly detection in cloud environments, automated vulnerability patching by multi-agent large language model (LLM) pipelines, and the broader landscape of securing AI systems across their lifecycle and situates their findings within the emerging literature on blockchain-enabled AI and autonomous AI agents. Each constituent study addresses a distinct point of failure in modern AI-driven security operations: the integrity of training data and model behavior, the reliability of real-time monitoring, and the trustworthiness of automated code remediation. We argue that blockchain's properties of immutability, decentralized consensus, and verifiable provenance directly address a gap common to all three: the difficulty of establishing trust in data, models, and autonomous agents that operate without a central authority. Building on real-world research on blockchain-secured data sharing, federated learning, and multi-agent coordination, we propose a layered reference architecture that couples adversarially hardened models, blockchain-anchored data provenance, AI-driven anomaly detection, and smart-contract-governed multi-agent remediation. We conclude by identifying open problems in scalability, privacy-transparency trade-offs, and the governance of autonomous agents that must be resolved before such integrated systems can be trusted in production-critical environments.