Draft:Artificial intelligence and blockchain

Artificial Intelligence and Blockchain: The Convergence of Intelligent and Decentralized Technologies

Introduction

Artificial intelligence (AI) and blockchain have emerged as two of the most influential technology areas in the development of modern digital systems. Although they were developed to address very different technological problems, research and industry have increasingly explored how the two can be combined. Artificial intelligence focuses on enabling computer systems to perform tasks associated with learning, prediction, reasoning, recognition, and decision-making, while blockchain provides distributed mechanisms for recording, sharing, and verifying information across a network without relying entirely on a single central authority.

The relationship between these technologies can be considered in two directions. Artificial intelligence can be used to analyze blockchain data, improve the operation of blockchain networks, identify suspicious activity, support smart-contract analysis, and optimize certain computational processes. At the same time, blockchain can provide infrastructure for AI systems by supporting data provenance, decentralized coordination, auditability, and mechanisms for sharing information among participants. Research reviews published in recent years have identified applications of this combination in areas including cybersecurity, finance, supply chains, healthcare, manufacturing, transportation, energy, and the Internet of Things.

The integration of AI and blockchain does not mean that one technology replaces the other. Instead, they address different aspects of digital systems. AI is generally associated with extracting patterns and making predictions from data, whereas blockchain is associated with distributed records, verification, and coordination. Their combination therefore creates a technological architecture in which intelligent algorithms can operate on or around distributed data and blockchain mechanisms can provide additional controls for data sharing and verification.

However, integrating the two technologies also introduces challenges. Blockchain networks can impose computational and storage overhead, while AI systems can require substantial amounts of data and computing resources. Issues involving privacy, scalability, interoperability, model transparency, governance, and security remain active areas of research. Consequently, AI and blockchain integration is better understood as an evolving research field rather than a single established technological architecture.

Background

Artificial intelligence

Artificial intelligence refers broadly to computational methods that enable machines or software systems to perform tasks that traditionally require aspects of human intelligence. These tasks can include pattern recognition, prediction, language processing, classification, optimization, planning, and decision-making.

Modern AI development has been strongly influenced by machine learning, in which systems learn patterns from data rather than relying exclusively on manually written rules. Deep learning, a subset of machine learning, uses multi-layered neural networks and has contributed to advances in areas such as computer vision, speech recognition, natural-language processing, and automated decision systems.

The effectiveness of many AI systems depends on the availability, quality, and governance of data. Training data may contain errors, duplication, missing values, biases, or maliciously manipulated information. Data management therefore represents an important consideration when AI systems are deployed in environments where multiple organizations or individuals contribute information.

Blockchain has attracted research interest in this context because it can provide a shared record of transactions or data-related events among participating entities. The combination does not automatically guarantee that data are accurate, but blockchain mechanisms can help establish a record of when information was submitted, how it was modified, and which network participants were involved.

Blockchain

Blockchain is a type of distributed ledger technology in which records are maintained across a network of participating nodes. Transactions or other records can be grouped into blocks and linked using cryptographic mechanisms. Depending on the blockchain design, network participants use a consensus mechanism to agree on the state of the ledger.

The technology became widely known through Bitcoin, but blockchain systems have subsequently been investigated for applications beyond cryptocurrencies. These include supply-chain tracking, digital identity, asset management, financial services, data sharing, Internet of Things systems, and smart contracts.

A smart contract is software deployed on a blockchain that can automatically execute specified operations when predefined conditions are satisfied. Smart contracts can reduce the need for some forms of manual coordination, but they can also introduce software vulnerabilities and limitations because blockchain transactions may be difficult or impossible to reverse once confirmed.

The properties commonly associated with blockchain including distributed operation, transparency in certain network configurations, cryptographic verification, and resistance to unauthorized modification have made the technology relevant to research into trustworthy data and decentralised computing.

Relationship Between Artificial Intelligence and Blockchain

The integration of AI and blockchain can broadly be divided into two categories: using AI to enhance blockchain systems and using blockchain to support AI systems.

AI for blockchain

One research direction focuses on applying machine learning and other AI techniques to blockchain networks. A 2024 review in the Journal of Network and Computer Applications identified research involving AI for security, consensus, smart contracts, data privacy, scalability, and other blockchain-related processes.

Blockchain networks generate large amounts of structured data. Machine learning algorithms can analyze transaction patterns, identify unusual behaviour, classify activities, and support security monitoring. Such systems may be useful for detecting patterns that are difficult to identify through manually defined rules.

AI has also been studied in relation to blockchain consensus mechanisms. Consensus is necessary for participating nodes to agree on the state of a distributed ledger. Researchers have investigated machine-learning techniques for improving aspects of consensus and network operation, although such approaches can introduce additional computational requirements and design complexity.

Another area of research involves smart contracts. Machine learning and newer generative AI systems have been investigated for code generation, vulnerability detection, verification, auditing, monitoring, and management of smart contracts. A 2025 systematic review examined 276 studies published between 2015 and 2024 and identified these areas as important themes in research on AI-enhanced smart contracts.

Blockchain for AI

The second direction involves using blockchain to support AI systems. AI development often depends on data generated by multiple organizations or users. Blockchain can provide a shared record for selected data transactions, permissions, provenance information, or model-related activities.

Data provenance is particularly relevant. In an AI workflow, it may be useful to know where data originated, when they were collected, whether they were modified, and which entities were responsible for particular actions. A blockchain-based record can provide an auditable history of events, although storing large datasets directly on-chain is generally impractical and many architectures therefore combine blockchain with off-chain storage.

Blockchain has also been studied as an infrastructure for decentralized AI. Instead of concentrating data, computation, and control within a single organization, decentralized architectures can distribute parts of these functions among multiple participants. Research on decentralized AI has examined blockchain, cryptography, distributed computation, and other mechanisms as building blocks for such systems.

Applications

Cybersecurity

Cybersecurity is one of the major research areas for AI and blockchain integration. AI can analyze large volumes of security data and identify patterns associated with attacks, anomalies, fraud, or other suspicious activity. Blockchain, meanwhile, can provide distributed records and mechanisms for establishing the history of selected security-related events.

A 2024 systematic review examined blockchain-enabled decentralized AI specifically in cybersecurity. The review considered the potential relationship between blockchain, decentralized AI, security, privacy, and trust, while also identifying challenges and future research directions.

One potential architecture involves using blockchain to record security events while AI analyses those events. Another involves using blockchain to coordinate multiple AI participants. Such architectures may be relevant in environments where different organizations need to cooperate without giving a single party complete control over all security information.

However, blockchain does not eliminate cybersecurity risks. Smart contracts can contain vulnerabilities, blockchain networks can be attacked, and AI models can be manipulated through malicious or misleading data. Combining the two technologies therefore creates additional security requirements rather than automatically producing a secure system.

Smart contracts

Smart contracts represent an important intersection between AI and blockchain. Traditional smart contracts execute predefined rules. AI techniques can potentially make parts of the development, analysis, monitoring, and management process more sophisticated.

Research has investigated AI-assisted smart-contract code generation, vulnerability identification, formal verification, monitoring, and adaptive management. The 2025 systematic review of 276 studies found that AI and machine learning are being explored throughout different stages of the smart-contract lifecycle.

Large language models have also introduced new possibilities for software development. They can assist developers in generating or analysing code, although automatically generated code can contain errors and security vulnerabilities. For blockchain applications, mistakes can be particularly significant because deployed contracts may control digital assets or execute transactions automatically.

Consequently, AI-assisted smart-contract development requires testing, code review, verification, and security auditing rather than relying solely on generated output.

Supply chain management

Supply chains involve numerous organisations, transactions, documents, and physical movements of products. Blockchain can provide a shared record of selected events, while AI can analyse supply-chain data for prediction, optimisation, anomaly detection, and planning.

Research on AI-powered blockchain in Industry 4.0 has examined applications including supply-chain tracking and tracing, product authenticity, manufacturing analysis, predictive maintenance, and IoT data management.

For example, blockchain can record information about a product as it moves through different stages of a supply chain. AI can subsequently analyse historical information to identify patterns or predict potential disruptions. The usefulness of such a system depends on the accuracy of the information entered into the network. Blockchain can help preserve a record of submitted information, but it cannot independently determine whether a physical-world claim was true at the moment it was entered.

Manufacturing and Industry 4.0

Industry 4.0 involves the integration of digital technologies into manufacturing and industrial processes. These environments generate large quantities of data through sensors, machines, enterprise systems, and connected devices.

AI can analyse manufacturing data to support predictive maintenance, quality control, process optimisation, and demand forecasting. Blockchain can provide a distributed mechanism for recording selected events and transactions among organisations participating in industrial networks.

Research reviewing AI-powered blockchain applications in Industry 4.0 has examined manufacturing, supply chains, IoT data, energy systems, and collaborative production networks.

A combined architecture can, for example, use AI to identify unusual machine behaviour while blockchain records relevant maintenance events. This can create an auditable history that can be shared between manufacturers, suppliers, service providers, and other authorised participants.

Internet of Things

The Internet of Things connects physical devices capable of collecting and exchanging information. IoT systems can generate data continuously, creating opportunities for AI-based analysis but also introducing challenges related to trust, security, and data management.

Blockchain can provide a distributed record of selected device interactions, while AI can analyse IoT data for anomaly detection, prediction, and optimisation. Research has explored blockchain and AI in IoT environments where multiple devices or organisations need to exchange information.

One challenge is that many IoT devices have limited computing power, storage, and energy resources. Running sophisticated AI models or participating directly in blockchain networks may therefore be impractical for some devices. Edge computing and other distributed architectures can be used to move computational workloads closer to the devices while reducing communication requirements.

Healthcare

Healthcare represents another area in which AI and blockchain have been investigated. AI can support medical data analysis, prediction, image interpretation, and decision-support systems. Blockchain can provide mechanisms for managing permissions, recording transactions involving health information, and coordinating data sharing among authorised parties.

The combination raises significant privacy requirements because healthcare data can contain sensitive personal information. A blockchain architecture must therefore carefully distinguish between information that needs to be recorded on a distributed ledger and information that should remain in controlled or encrypted storage.

AI models also require appropriate training and validation. Blockchain can provide a record of data-related events, but it does not by itself guarantee that an AI model is clinically accurate or that the underlying data are unbiased.

Financial services

Financial services have been early adopters of both AI and blockchain-related research. AI is used in areas such as fraud detection, risk analysis, customer-service automation, forecasting, and transaction monitoring. Blockchain systems can support digital assets, distributed records, and automated transactions.

The combination can allow AI systems to analyse blockchain transaction data for suspicious behaviour or market patterns. Conversely, blockchain can be used to provide records associated with AI-generated decisions or data exchanges.

The financial context also illustrates the importance of governance. Automated systems operating around financial transactions must comply with applicable rules and require mechanisms for auditing, monitoring, and managing errors.

Energy systems

AI and blockchain have also been investigated for energy systems. AI can forecast demand, optimise energy distribution, and analyse consumption patterns. Blockchain can support peer-to-peer energy transactions and provide records for selected exchanges.

Research in Industry 4.0 has examined decentralised energy systems in which AI algorithms optimise distribution while blockchain-based smart contracts support transparent transaction mechanisms.

However, energy systems operate under physical and regulatory constraints. A software architecture cannot independently replace grid-management infrastructure, safety requirements, or regulatory oversight.

Decentralised Artificial Intelligence

Decentralised artificial intelligence refers broadly to approaches that distribute aspects of AI computation, data, coordination, or control across multiple participants rather than relying exclusively on a central infrastructure.

One motivation for decentralisation is to reduce dependence on a single organisation or server. Another is to allow participants to collaborate while retaining greater control over their own data.

Blockchain can potentially contribute to such systems by providing distributed records, identity mechanisms, incentives, governance mechanisms, and coordination between participants. Cryptographic technologies can additionally support secure communication and privacy-preserving computation.

Federated learning is another related approach. In federated learning, multiple participants can train a model collaboratively while keeping raw training data on their local systems. Blockchain has been researched as a mechanism for coordinating participants, recording contributions, managing trust, or supporting incentives within federated-learning environments.

Recent research continues to investigate blockchain-enabled defences for federated learning, particularly against threats such as model poisoning, backdoors, Sybil attacks, and inference attacks.

Decentralisation does not automatically solve the problems of AI. Distributed systems can introduce additional coordination costs, latency, governance questions, and security challenges. As a result, researchers continue to investigate which components should be decentralised and which are more efficiently operated through conventional infrastructure.

Data Provenance and Trust

Data provenance describes information about the origin, history, and processing of data. Provenance is important in AI because model outputs can depend heavily on the datasets used during training and inference.

Blockchain can be used to record provenance-related events, such as when a dataset was registered, which organisation submitted it, or when a particular model version was associated with a dataset. Such records can support auditing and accountability.

The approach is particularly relevant where multiple organisations exchange data. A shared ledger can provide a common record without requiring every participant to maintain exactly the same internal database.

Nevertheless, blockchain-based provenance has limitations. Recording an event on a blockchain does not prove that the underlying real-world information was correct. If inaccurate information is entered into the system, blockchain mechanisms can preserve that inaccurate information. This distinction between data integrity and data truth is an important consideration when designing AI-blockchain systems.

Privacy

Privacy is one of the most complicated aspects of integrating AI and blockchain.

AI systems often require substantial amounts of data. Blockchain systems, depending on their design, may make information or transaction histories available to multiple participants. These characteristics can create tension when the underlying information is sensitive.

One approach is to store only hashes, permissions, metadata, or references on-chain while keeping sensitive data in encrypted off-chain systems. Another is to combine blockchain with privacy-preserving technologies such as secure multiparty computation, differential privacy, trusted execution environments, or federated learning.

Blockchain-enabled decentralised AI research has specifically examined security and privacy as important areas for further development.

Privacy therefore needs to be considered at the architecture level. Simply placing personal or confidential information on a blockchain may create problems that cannot easily be reversed later.

Benefits

The potential benefits of AI and blockchain integration vary according to the application.

Improved data management

Blockchain can provide shared records for data-related events, while AI can analyse those records. Together, the technologies may help organisations coordinate information across institutional boundaries.

Automation

AI can support prediction, classification, optimisation, and decision-making, while smart contracts can automatically execute predefined blockchain operations.

Auditability

Blockchain records can provide an auditable history of transactions and selected system events. AI can then analyse these records to identify patterns or anomalies.

Security monitoring

AI can analyse blockchain activity to detect unusual patterns, while blockchain can provide distributed records for security-related events.

Decentralised coordination

Blockchain can coordinate participants without requiring every interaction to depend on a single central database, while AI can help manage or optimise processes within that network.

Transparency

In appropriate blockchain configurations, participants can independently verify selected records. When combined with AI, this can provide additional information about how data or decisions were processed.

These potential benefits should not be interpreted as universal properties. The value of the combination depends on system architecture, governance, data quality, network design, and the specific problem being addressed.

Challenges

Scalability

Both AI and blockchain can be resource-intensive. AI models may require significant processing power and data, while blockchain networks can involve substantial computation, communication, and storage requirements.

When the two are combined, these demands can increase. Research on AI-enhanced blockchain continues to identify scalability and efficiency as major challenges.

Computational overhead

Blockchain-based verification and consensus can require additional computation. AI models can also require intensive training and inference. Designing a system that balances security and performance is therefore an important engineering problem.

Interoperability

AI-blockchain architectures may need to communicate with multiple blockchains, databases, AI frameworks, cloud services, IoT devices, and enterprise systems. Differences in data formats, protocols, identities, and governance can make interoperability difficult.

The problem is particularly important in large organisations where legacy systems must interact with newer decentralised infrastructures.

Data privacy

As discussed earlier, AI needs data while blockchain systems may distribute information among participants. Privacy-preserving mechanisms can reduce risks, but they can also add complexity and computational overhead.

Data quality

Blockchain can preserve records, but it cannot guarantee the truth of information supplied to the network. AI systems trained on incorrect or biased data can produce unreliable results even if the underlying records are immutably stored.

AI model transparency

Many advanced AI models are difficult to interpret. Blockchain can record model-related events, but it does not automatically make the reasoning of an AI model understandable.

Governance

Decentralised systems still require governance. Questions can arise over who can participate, who can update software, how disputes are resolved, how errors are corrected, and how responsibilities are allocated.

Security

Combining two complex technologies creates a larger system with multiple potential attack surfaces. AI models can be attacked or manipulated, while blockchain protocols and smart contracts can contain vulnerabilities.

Security therefore needs to be evaluated across the entire architecture rather than treating blockchain as a standalone security solution.

Ethical Considerations

The combination of AI and blockchain also raises ethical questions.

AI systems can reproduce biases present in training data. Blockchain records can make certain decisions or transactions more persistent and auditable. When automated decision-making is connected to immutable records or automated transactions, errors can have consequences that are difficult to reverse.

Researchers studying AI-enabled blockchain have identified ethical issues alongside technological and business applications. These include questions surrounding privacy, governance, accountability, transparency, and responsible use.

Another issue concerns responsibility. If an AI system makes a decision and a smart contract automatically executes an action based on that decision, determining responsibility for an undesirable outcome can become complicated.

Ethical design therefore requires more than technical integration. Organisations need policies governing data use, model development, access control, monitoring, auditing, and human oversight.

Artificial Intelligence, Blockchain, and Generative AI

The emergence of generative AI and large language models has created another area of research at the intersection of AI and blockchain.

Generative AI systems can produce text, images, software code, and other forms of content. Blockchain can potentially be used to record information about content provenance, model interactions, ownership claims, or verification events.

Research published in Business Horizons has examined blockchain as a possible complementary technology for addressing challenges associated with generative AI, including transparency, verifiability, decentralisation, bias, hallucinations, misuse, and the difficulty of understanding some AI systems.

Blockchain-based systems for large language models are also being explored. Recent research has considered mechanisms such as smart contracts, cryptographic commitments, and decentralised storage for improving data management, inference authentication, explainability, and auditability.

These approaches remain an active research area. Blockchain cannot independently determine whether generated content is correct, nor can it eliminate the possibility of AI-generated misinformation. Its potential contribution is instead related to recording, verification, provenance, and coordination mechanisms.

Future Research

Research on AI and blockchain continues to expand in several directions.

One area is decentralised AI, where researchers are investigating how computing, data, model training, and governance can be distributed across multiple participants. A recent systematic review of decentralised AI examined dozens of studies and identified blockchain and cryptography among the technological building blocks being explored in the field.

Another area is federated learning. Blockchain can potentially help coordinate federated-learning participants and provide records of contributions or interactions. Current research continues to examine security, privacy, scalability, and performance trade-offs in blockchain-enabled federated learning.

Smart contracts are also likely to remain an important research area. AI-assisted code generation, vulnerability detection, formal verification, and automated auditing could change how decentralised applications are developed, but reliability and security remain important concerns.

Research is also examining the relationship between AI, blockchain, edge computing, and Web3 architectures. These technologies can collectively address questions around decentralisation, data ownership, trust, and distributed computation, although integrating them introduces significant architectural complexity.

Future systems may therefore move away from treating AI and blockchain as isolated technologies. Instead, they may combine intelligent computation, distributed data management, privacy-preserving methods, edge computing, cryptography, and automated coordination within larger digital ecosystems.

Conclusion

Artificial intelligence and blockchain address different technological problems, but their combination has become an active area of research. AI provides methods for analysing data, recognising patterns, making predictions, and supporting automated decisions. Blockchain provides mechanisms for distributed record keeping, verification, coordination, and, in some configurations, transparency and auditability.

Research has investigated AI for improving blockchain systems, including security monitoring, consensus optimisation, smart-contract analysis, privacy, and efficiency. At the same time, blockchain has been studied as infrastructure for AI systems, particularly for data provenance, decentralised coordination, privacy, and trust. Applications have been explored in cybersecurity, supply chains, manufacturing, healthcare, finance, energy, transportation, and the Internet of Things.

The integration is not without limitations. Scalability, computational overhead, interoperability, privacy, governance, data quality, security, and model transparency remain important challenges. The technologies also introduce new questions about accountability and responsibility when AI-generated decisions interact with automated blockchain-based processes.

The most significant development is therefore not simply the combination of two popular technologies, but the emergence of architectures in which intelligent computation and decentralised infrastructure can complement one another. Continued research will determine which applications benefit from this combination and which are better served by conventional centralised systems.

As research progresses, the practical value of AI and blockchain integration is likely to depend less on combining technologies for its own sake and more on whether the resulting architecture provides a measurable improvement in security, privacy, efficiency, accountability, or coordination for a particular use case.