How AI can help make blockchain more secure

Artificial intelligence offers considerable potential for strengthening the security of blockchain technologies. Through innovative approaches and advanced solutions, AI can help protect blockchain networks from a variety of threats, while improving the efficiency and reliability of operations. This article explores several concrete ways in which AI contributes to securing blockchains.

‍Fraud and anomaly detection

One of the major contributions of AI to blockchain is its ability to detect fraud and anomalous behaviour automatically. Using machine learning algorithms, AI can analyse transaction patterns to identify suspicious activity that could indicate an attempt at fraud or an attack on the network. For example, unusual changes in transaction volume or transfers of funds to newly created addresses can be indicators of malicious activity. AI can alert network operators in real time, enabling a rapid response to investigate and, if necessary, block suspicious transactions.

‍Optimising consensus protocols

Consensus protocols such as Proof of Work and Proof of Stake are essential for the security and integrity of blockchains. AI can be used to optimise these protocols, reducing the risk of certain attacks such as the 51% attack. Through predictive analytics, AI could anticipate potential points of vulnerability and propose adjustments to consensus algorithms to prevent attacks before they occur.

‍Safer smart contracts

Smart contracts are at the heart of many blockchain applications, but they are also prone to bugs and security flaws. AI can help improve the security of these smart contracts through automated code analysis and verification before they are deployed. AI tools can be trained to scan the code of smart contracts, detecting errors, security flaws or potentially malicious behaviour, thus avoiding considerable financial losses.

‍Continuous network monitoring

AI can be used for continuous monitoring of the blockchain network, analysing operations to ensure that everything is working as expected. AI systems can be configured to monitor network performance, node health, and even predict potential failures before they occur, enabling proactive maintenance.