Scientific Journal of Artificial Intelligence and Blockchain Technologies (SJAIBT)
Journal particulars: Title: Scientific Journal of Artificial Intelligence and Blockchain Technologies (SJAIBT) ISSN: 3049-4389 I.F: 9.413 (2025) Frequency: Quarterly (4 issues per year) Publisher: ResaGate Global Publisher Address: Raj Nagar Extension, Ghaziabad, Uttar Pradesh (201017) Publisher Email: editor@sjaibt.org | sjaibt@gmail.com Copyright: Remain Journal Starting Year: 2024 Main Subject: Computer Science Scope: Artificial Intelligence & Blockchain Technology Language: English Publication Format: Online (Open Access) Website: https://sjaibt.org Contact : +91 9456916244 Aims and Objectives of SJAIBT: The Scientific Journal of Artificial Intelligence and Blockchain Technologies (SJAIBT) seeks to: Advance the understanding, innovation, and application of Artificial Intelligence (AI) and Blockchain across engineering and scientific domains. Foster collaboration among academia, industry, and government institutions to accelerate the adoption of AI and Blockchain. Encourage interdisciplinary research integrating AI, Blockchain, and other engineering technologies for real-world impact. Provide an open-access platform for researchers, scientists, engineers, and practitioners to disseminate groundbreaking discoveries. Explore the ethical, legal, and policy dimensions of AI and Blockchain adoption. Promote the translation of theoretical advances into scalable industrial solutions for sectors such as finance, healthcare, energy, logistics, education, and manufacturing. Scope of the Journal (SJAIBT) SJAIBT is a leading platform dedicated to exploring the frontiers of Artificial Intelligence, Blockchain, and related engineering technologies. The journal covers theory, algorithms, systems, frameworks, and applications, addressing both academic research and industrial deployment. It emphasizes not only the computational and scientific foundations but also the engineering integration required to build scalable, secure, and sustainable intelligent systems. The journal encourages submissions spanning Computer Science, Engineering, and Technology with a strong emphasis on interdisciplinary innovation that bridges AI, Blockchain, Quantum Computing, Cybersecurity, and Industry 4.0. Subject Areas in SJAIBT: Core Focus Artificial Intelligence (AI): machine learning, deep learning, natural language processing, reinforcement learning, explainable AI, AI in robotics, AI in healthcare, AI in cybersecurity. Blockchain Technology: distributed ledger systems, consensus algorithms, decentralized finance (DeFi), smart contracts, blockchain security, Web3, tokenization, blockchain for supply chains. AI–Blockchain Integration: federated learning with blockchain, privacy-preserving AI, secure multiparty computation, blockchain-based trust models, interoperability frameworks. Engineering & Applied Domains Computer Science & Engineering (CSE): software engineering, big data analytics, cloud-native systems, edge computing, DevOps automation, API ecosystems. Electronics & Communication Engineering (ECE): VLSI design, embedded systems, IoT security, quantum devices, wireless networks, 5G/6G communication with AI and blockchain integration. Electrical Engineering (EE): smart grids, renewable energy optimization, control systems, power electronics, AI for predictive maintenance, blockchain for energy trading. Mechanical & Manufacturing Engineering (ME): industrial automation, robotics, digital twins, additive manufacturing, Industry 4.0 integration, blockchain in supply chain traceability. Civil & Structural Engineering (CE): smart cities, digital infrastructure, AI for urban planning, blockchain for construction contracts, intelligent transportation systems. Chemical & Biochemical Engineering (ChE): process optimization, bioinformatics, blockchain for pharmaceutical supply chains, AI in drug discovery. Aerospace & Automotive Engineering: autonomous vehicles, UAVs, aerospace materials, AI for navigation, blockchain for safety compliance. Biomedical & Health Engineering: AI-driven diagnostics, medical imaging, personalized medicine, blockchain for health data management. Environmental & Agricultural Engineering: AI for climate modeling, blockchain for carbon trading, smart farming, water resource management. Interdisciplinary and Emerging Areas Quantum computing and AI Cybersecurity and Zero-Trust frameworks Digital twins and metaverse ecosystems AI ethics, governance, and regulation Blockchain in law, policy, and governance AI and blockchain applications in education and e-learning
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AI-Powered Adaptive Learning Platforms with Credential Verification on Chain
Adaptive learning platforms personalize instruction by continuously modeling each learner’s knowledge, skills, and pace. Meanwhile, the integrity of academic records and micro-credentials increasingly depends on trustable, portable verification mechanisms across institutions and labor markets. This manuscript proposes and critically examines a reference architecture that unifies AI-powered adaptivity with on-chain credential verification based on decentralized identifiers (DIDs) and the W3C Verifiable Credentials (VC) data model. The design integrates real-time learner modeling (e.g., Bayesian/Deep Knowledge Tracing), reinforcement-learning sequencing, and learning analytics pipelines (xAPI/Caliper) to generate mastery-aligned learning pathways. When mastery thresholds are met, the system issues verifiable micro-credentials whose cryptographic proofs (hashes, revocation registries) are anchored to a permissioned or public blockchain, enabling instant, privacy-respecting verification without exposing sensitive learner data. We situate the architecture in prior research on intelligent tutoring systems, formative feedback, and digital credentialing; articulate governance, privacy, security, and equity considerations; and provide a design-science methodology covering requirements analysis, component design, and evaluative heuristics. A prototypical evaluation framework is outlined with metrics for learning effectiveness (time-to-mastery, normalized gain), instructional efficiency (content reuse and algorithmic inference cost), trust and integrity (verification latency, fraud resistance), privacy (selective disclosure), and operational feasibility (gas/transaction cost and throughput). Anticipated results include improved mastery attainment and mobility of skills signals across ecosystems, while the discussion addresses challenges such as bias mitigation in recommendation policies, long-term key management, revocation semantics, and regulatory compliance. The manuscript concludes with a roadmap for pilots and research, emphasizing interoperable standards, transparent AI, and consortium-based governance to advance learner agency and system trustworthiness.
Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies (SJAIBT) https://creativecommons.org/licenses/by-nc/4.0
Blockchain-AI Frameworks for Crop Yield Prediction and Land Registration
Sustainable agricultural growth depends simultaneously on two notoriously difficult capabilities: (1) accurate, timely crop yield prediction to guide decisions on inputs, risk, and markets, and (2) trustworthy land administration that secures property rights, reduces disputes, and unlocks credit. This manuscript proposes and evaluates an integrated Blockchain–AI (B-AI) framework that addresses both needs in a single socio-technical architecture. On the analytics side, the framework fuses multi-source data—satellite vegetation indices, weather, soil sensors, and agronomic records—through a temporal deep learning stack (CNN–LSTM/Transformer) to forecast yield at field scale. On the governance side, the framework aligns with ISO 19152 Land Administration Domain Model (LADM) and FAO’s Voluntary Guidelines on the Responsible Governance of Tenure (VGGT), using a permissioned blockchain (Hyperledger Fabric) for tamper-evident land records, verifiable credentials for farmer and parcel identities, and smart contracts for provenance, consented data sharing, and claim adjudication. We detail the end-to-end data flow, on-/off-chain partitioning (IPFS for large files), and privacy preservation via zero-knowledge proofs over model inputs and outputs. A prototype is exercised using three growing seasons of open satellite/weather data and simulated IoT soil telemetry, covering 6,000 plots representative of smallholder conditions. Across five states (synthetic administrative clusters), the model reduces RMSE by 18–34% versus strong tabular baselines (Random Forest, XGBoost), and improves field-level R2R^2R2 from 0.52 (RF) to 0.69 (Transformer). A/B policy simulations show that secure land-title anchoring and data-provenance incentives increase voluntary data contribution by 23–31%, which further lifts yield prediction accuracy by ~6% through richer temporal coverage. The combined system supports practical workflows: digitizing titles, linking parcels to sensor streams, issuing season-specific data-use credentials, and triggering weather-index insurance payouts through auditable smart contracts. We discuss deployment considerations—interoperability, cost, and governance—and outline future work on federated learning across jurisdictions, spatial generalization, and integration with climate-resilience programs.
Decentralized Market Access for Farmers Using Blockchain-AI Tools
Smallholder farmers face persistent barriers to fair market access: fragmented and opaque value chains, information asymmetries in pricing, delayed payments, weak bargaining power, and costly intermediation. Digital public infrastructure and recent advances in distributed ledgers and artificial intelligence (AI) offer a path to redesign market linkages around the farmer. This paper proposes and analyzes an integrated Blockchain-AI market access framework that (i) anchors farmer and farm identities to authoritative registries, (ii) tokenizes produce lots with verifiable quality, origin, and compliance metadata, (iii) uses smart contracts for listing, bidding, escrow, logistics, and automated settlement, and (iv) deploys AI for price nowcasting, quality grading, demand matching, and risk scoring for input credit and insurance. We situate the framework within current policy and infrastructure developments (e.g., India’s AgriStack DPI and e-NAM), and align data governance with the Digital Personal Data Protection (DPDP) Act, 2023. Evidence from the agri-food literature on digital market platforms and blockchain traceability (e.g., IBM Food Trust, Walmart pilots, OpenSC) suggests traceability and faster recalls, while mobile connectivity trends indicate a feasible last-mile channel for farmer-facing apps. Methodologically, we outline a multi-site, FPO-centered pilot with pre/post evaluation comparing realized farmgate prices, payment time, rejection rates, and spoilage against baseline channels. Illustrative pilot results show the potential for higher realized prices through improved price discovery, faster payments via escrow smart contracts, and reduced post-harvest losses through timed logistics triggers—subject to connectivity, digital literacy, and governance constraints. We conclude with a roadmap for phased rollout and discuss scope and limitations, including interoperability, standardization, and regulatory compliance.
Quantum-Resistant AI and Blockchain Protocols for Long-Term Security
The emergence of cryptographically relevant quantum computers (CRQCs) would compromise today’s dominant public-key primitives (RSA, DiffieHellman, ECDSA), threatening long-lived confidentiality, authentication, and integrity guarantees across AI pipelines and blockchain networks. This manuscript proposes a unified, quantum-resilient architecture that blends NISTstandardized post-quantum cryptography (PQC)—MLKEM for key establishment and ML-DSA or SLH-DSA for digital signatures—with defense-in-depth controls for AI model protection, data privacy, and blockchain protocol evolution. We first motivate the need for migration by outlining quantum attacks (Shor for factoring and discrete logarithms; Grover for quadratic search speedups) and summarize PQC standardization milestones and transition guidance. We then review the literature on lattice-based and hash-based cryptography, homomorphic encryption for privacy-preserving AI, and secure aggregation/differential privacy as complementary privacy technologies. Building on this foundation, we present a methodology for designing and evaluating quantum-resistant AI and blockchain systems, including key-lifecycle hardening, protocol-level hybrids, and crypto-agility. A simulated deployment (1,000 clients; 16 validator nodes; TLS 1.3 with ML-KEM and hybrid handshakes; blockchain transactions signed with ML-DSA and SLH-DSA) measures handshake and signing overheads, throughput, and model-training privacy-utility trade-offs. Results indicate that: (i) PQC-only handshakes add modest latency yet remain acceptable for online APIs; (ii) hybrid ECDHE+ML-KEM offers near-term defense against “store-now-decrypt-later,” with predictable cost; (iii) ML-DSA is practical for blockchain validation at common block sizes, while SLH-DSA suits firmware/codesigning and archival integrity; and (iv) combining PQC with secure aggregation and differentially private training preserves model utility with bounded overhead.
AI-Driven Weather Forecast Integration with Blockchain Smart Farming Contracts
Smallholder and commercial farms alike are increasingly exposed to weather volatility and market uncertainty. Recent progress in data-driven weather prediction and distributed ledgers enables a new class of “smart farming contracts” that can automatically enact agronomic actions or financial transfers when meteorological conditions cross predefined thresholds. This manuscript proposes and analyzes an AI–blockchain architecture that ingests multi-source forecasts (global numerical weather prediction, regional reanalyses, satellite observations, and on-farm IoT), calibrates them with machine learning for local bias correction, quantifies uncertainty, and delivers signed forecast products to on-chain contracts via decentralized oracles. These contracts schedule irrigation, pesticide and fertilizer windows, harvest logistics, energy usage, and parametric risk transfers (e.g., drought or heat payouts) with verifiable audit trails. We develop a methodology for ensemble model fusion (e.g., gradient-boosted or temporal-fusion networks) and probabilistic post-processing to produce location-specific forecast distributions at daily to hourly horizons. A simulated study spanning three agro-climatic zones evaluates reductions in irrigation water (–12–18%), improvements in forecast RMSE (–22–35% vs. raw models), faster and less-contested insurance settlements (from weeks to hours), and modest yield gains (2–6%) associated with better timing of operations. A statistical analysis illustrates the sensitivity of outcomes to forecast skill, oracle latency, and contract design. We discuss governance, data provenance, privacy-preserving oracles, and compliance for both permissionless and permissioned ledgers. Results indicate that coupling AI-calibrated forecasts with verifiable, automation-ready smart contracts can convert climate uncertainty into programmable risk, advancing resilient, sustainable agriculture.
AI-Assisted Schema Transformation for Automated Legacy-to-Cloud Database Migration
The transition of the legacy database to the cloud environment is one of the key factors for achieving scalability, flexibility, cost efficiency, and data innovation for an organization. One of the most complicated processes in database migration involves the transformation of schemas, meaning their adaptation from the format of the legacy database to a cloud environment. The conventional methods for schema transformation heavily rely on manual work and mapping rules, thus making the process resource-consuming and prone to human errors. The ever-growing complexity of enterprise databases exacerbates these problems, especially in case of heterogeneous systems, unknown schema structure, or constantly changing requirements. Thanks to the recent progress in Artificial Intelligence (AI), there are many opportunities for automating the process of schema transformation within the legacy-to-cloud migration framework. Such methods utilize machine learning, semantic analysis, pattern recognition, knowledge graphs, and language models to identify correspondences between schemas, infer relationships between different data sets, provide suggestions about transformation rules, and create scripts for migration without any human assistance. Such technologies can greatly improve the process of transformation, increase migration accuracy, reduce transformation efforts and facilitate cloud migration. This paper presents an overview of existing literature on AI-assisted schema transformation and automation of the migration process. According to the findings, hybrid approaches, combining traditional matching techniques with advanced AI-assisted semantic analysis, demonstrate greater effectiveness and accuracy of transformation.
Legacy Database Migration , Cloud Database Migration , Schema Transformation , Artificial Intelligence (AI) , Schema Matching , Automated Data Migration , Machine Learning
Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
Generative AI and the Reinvention of Management Education
Generative Artificial Intelligence (GenAI) is rapidly transforming higher education; however, its adoption in management education remains fragmented, with existing studies primarily focusing on conceptual discussions, academic integrity concerns, or isolated classroom applications. Limited research has proposed an integrated framework that combines personalized learning, intelligent tutoring, predictive learning analytics, competency-based assessment, and responsible AI governance within a unified management education ecosystem. To address these gaps, this paper proposes the Generative AI-Enabled Management Education Reinvention Framework (GAI-MERF), an intelligent educational architecture designed to enhance learning outcomes while preserving academic integrity and ethical AI practices. The framework integrates adaptive content generation, AI-driven business case simulations, automated formative assessment, personalized learning recommendations, and predictive analytics to support competency development in strategic thinking, leadership, decision-making, and problem-solving. A quantitative experimental methodology is employed using educational performance data from management students to compare conventional instructional practices with the proposed AI-assisted framework. Multiple evaluation metrics, including academic performance, student engagement, critical thinking, faculty productivity, prediction accuracy, and ethical AI compliance, are used to assess framework effectiveness. Experimental results demonstrate notable improvements across all educational dimensions, indicating that the proposed framework significantly enhances personalized learning, instructional efficiency, and managerial competency development. Furthermore, the integration of transparency, privacy protection, bias mitigation, and human oversight ensures responsible AI adoption within educational environments. The proposed framework provides a scalable and practical foundation for developing intelligent, adaptive, and ethically governed management education systems capable of preparing future business leaders for increasingly AI-driven organizational environments.
Generative Artificial Intelligence (GenAI)
Verifiable AI-Assisted Learning Analytics on Blockchain Networks
Learning analytics has matured into a strategic capability for institutions seeking to improve student success, personalize learning pathways, and optimize teaching practice. Yet the broader adoption of AI-assisted analytics is constrained by persistent trust and governance problems: opaque models, unverifiable data pipelines, fragmented consent management, and limited auditability of how insights are generated and used. This manuscript proposes a verifiable analytics architecture that anchors AI-assisted learning analytics on blockchain networks to provide endto-end integrity, provenance, and policy compliance. The approach integrates four pillars: (1) verifiable data provenance using standards such as xAPI/Caliper and content-addressed storage; (2) privacy-preserving model training and inference using federated learning and differential privacy; (3) cryptographic verification of analytics workflows using commitments, attestation, and zero-knowledge proofs; and (4) institutional governance through smart-contract-based consent, purpose limitation, Date of Publication: 12-06-2026 and verifiable credentials for outcomes. We describe the system model, threat assumptions, and a permissioned blockchain implementation that coordinates actors across learning management systems (LMS), devices, and analytics services. A methodology for evaluating performance, privacy risk, and verifiability is presented, along with illustrative results from a lab-scale prototype using synthetic data. The proposed design demonstrates how educational institutions can deliver actionable analytics while providing students and faculty with audit trails that independently verify the who, what, when, and how of analytics computation—without exposing raw personal data. We conclude with implications for policy, practice, and future research, including formal verification of smart-contract policies, standards alignment, and crossinstitution analytics marketplaces that preserve individual agency.
Learning Analytics, Blockchain, Federated Learning, Differential Privacy, Zero-Knowledge Proofs, Provenance, Verifiable Credentials, Consent Management, Governance, Auditability
Blockchain-Powered Academic Integrity with AI-Based Plagiarism Detection
Academic institutions face intensifying challenges to uphold integrity in an era of abundant digital content, AI-assisted writing tools, remote assessment, and rapidly scaling enrollments. Traditional plagiarism detection pipelines— based largely on centralized databases and surface-level similarity measures—struggle with paraphrase obfuscation, cross-lingual borrowing, ghostwriting, and evolving AI-generated text. This manuscript proposes a reference architecture that fuses permissioned blockchain with advanced AI-based plagiarism detection to create verifiable, privacy-preserving, and scalable integrity services for higher education. The approach records tamper-evident submission events, content fingerprints, and adjudication trails on-chain while storing full artifacts off-chain. It deploys modern NLP and program-analysis models (transformers, stylometry, AST- and token-based code analysis) for robust semantic similarity and Date of Publication: 12-06-2026 authorship profiling, augmented by privacy-preserving techniques such as federated learning and differential privacy. The design extends to decentralized identifiers (DIDs) and verifiable credentials (VCs) for identity assurance, and to zero-knowledge proofs (ZKPs) for selective disclosure of evidence during disputes. We present a methodological plan and an illustrative statistical analysis that compares a baseline detector with the proposed AI+blockchain system, showing higher recall with maintained precision, lower false-positive rates, and auditable decisions. We discuss governance, interoperability, and cost-performance trade-offs, and we delineate scope and limitations—including model drift, paraphrase arms races, throughput constraints, and crossinstitution adoption. The resulting architecture reframes plagiarism detection as a transparent, rights-respecting, and institutionally portable service, enabling universities to deter misconduct, accelerate fair resolution, and cultivate trust across the academic ecosystem.
Academic Integrity, Blockchain, Plagiarism Detection, AI/NLP, Federated Learning, Differential Privacy, Verifiable Credentials, Provenance, Auditability
Smart Contract Automation for Logistics Insurance Claims
Global logistics involves complex, multi-party exchanges of documents, data, and risk. When losses or delays occur, insurance claims are often slow, manual, and opaque due to fragmented evidence, cross-jurisdictional documentation, and adversarial incentives. This manuscript proposes a comprehensive, end-to-end design for automating logistics insurance claims using smart contracts on permissioned blockchains, augmented by standardized trade documentation (e.g., eBL), trusted data oracles (e.g., IoT/telemetry, weather, carrier status), and parametric triggers. We synthesize literature on blockchain in supply chains and insurance, analyze oracle architectures for trustworthy event ingestion, and integrate recent advances in standards that make digital title and documentation legally effective. Building on this, we specify a reference methodology: (i) policy encoding and coverage modeling; (ii) event detection and attestation via decentralized oracle networks and verifiable off-chain computation; (iii) claims triage with parametric and non-parametric pathways; (iv) Date of Publication: 12-06-2026 privacy-preserving evidence exchange using private data collections and encrypted attachments; and (v) on-chain settlement and audit. A hypothetical pilot design and evaluation framework is presented with operational Key Performance Indicators (KPIs) such as cycle time, touchpoints, loss adjustment expense, and dispute rates. Our results section illustrates—using realistic but nonproprietary scenarios—how hybrid smart contracts can reduce claim adjudication latency, standardize evidence requirements, deter fraud, and create continuous auditability without eroding due-process protections. We conclude with governance guidance for carriers, brokers, and (re)insurers, highlighting legal enablers, adoption barriers, and a roadmap to scale through regulatory sandboxes, industry consortia, and standards alignment. The contribution is a practical, system-level blueprint joining legal, technical, and operational layers to make logistics claims faster, fairer, and more transparent.
Logistics Insurance, Smart Contracts, Parametric Claims, Blockchain, E-Bill of Lading (eBL), Data Oracles, Hyperledger Fabric, Chainlink, MLETR, Fraud Mitigation
Perishable Goods Monitoring with AI-Powered IoT and Blockchain
Perishable goods—from fresh produce and dairy to seafood and biologics—degrade rapidly when storage and transport conditions deviate from their narrow optimal ranges. Temperature excursions, humidity swings, ethylene accumulation, shocks, and delays compound along complex, multi-actor supply chains, contributing to large-scale food loss, diminished quality, and safety incidents. Contemporary monitoring is fragmented, post-hoc, and siloed; data fidelity and trust are frequently contested, hampering coordinated response. This manuscript proposes an integrated architecture that combines (i) lowpower IoT sensing across storage and transit nodes, (ii) ondevice and edge AI for anomaly detection and Remaining Shelf Life (RSL) prediction, and (iii) a permissioned blockchain that notarizes critical events, enforces smartcontract guardrails, and anchors verifiable off-chain telemetry. We review relevant literature on cold-chain IoT, intelligent packaging (e.g., time-temperature indicators and Date of Publication: 10-06-2026 e-noses), shelf-life modeling with machine learning, and blockchain traceability. We then outline a methodology for deploying a hybrid edge–cloud pipeline: TinyML models perform real-time detection on microcontrollers; cloud services run multi-task learning for RSL and risk scoring; hashed sensor batches are committed to a Hyperledger Fabric ledger; bulky streams are stored via contentaddressed IPFS with on-chain references. A simulation study models 10,000 shipments across farm–DC–retail legs, benchmarking baseline operations vs. the proposed system under realistic latency, packet loss, and workload profiles. Results (simulation) suggest reductions in temperatureexcursion minutes, faster root-cause analysis, and improved traceability with low verification overhead. We conclude with implementation guidance, statistical measures for validation, and research opportunities in privacypreserving analytics and policy-aligned data governance. Key background figures on global food loss and the benefits of traceable cold chains motivate the approach.
Perishable Goods, Cold Chain, IoT Sensors, TinyML, Anomaly Detection, Remaining Shelf Life, Blockchain, Hyperledger Fabric, IPFS, Traceability
Blockchain for Real-Time Supply Chain Tracking with Predictive AI
Globalized supply chains face persistent problems of poor visibility, delayed exception handling, counterfeit risk, and brittle planning under uncertainty. Real-time tracking is frequently hampered by siloed information systems and data integrity issues that erode trust among trading partners. This manuscript proposes a reference architecture that integrates permissioned blockchain ledgers with predictive AI to create a shared, tamperevident backbone for event data (e.g., EPCIS-compliant commissioning, aggregation, shipping, receiving, and sensor telemetry) and to enable continuous forecasting of estimated time of arrival (ETA), stockouts, and cold-chain excursions. The blockchain layer establishes data lineage, non-repudiation, and programmable compliance via smart contracts, while the AI layer ingests the same high-quality, time-stamped events to learn patterns and anticipate disruptions. We report results from a controlled simulation study reflecting 12 months of operations across four lanes Date of Publication: 10-06-2026 and two temperature-controlled product families. Relative to a baseline of conventional EDI and siloed databases, the integrated approach reduces ETA mean absolute error by 56%, halves stockout rates, cuts recall resolution time by 75%, and meaningfully lowers cold-chain excursions. The paper details data models, on-chain/off-chain partitioning, privacy controls (channels and selective disclosure), governance, and model lifecycle operations (MLOps) aligned to immutable audit trails. We also present a statistical summary of measured improvements and discuss practical deployment guidance, including standards alignment (GS1 EPCIS), interoperability, and organizational incentives. The findings suggest that combining blockchain’s shared truth with predictive AI’s anticipatory capabilities yields measurable operational gains and risk reduction in complex supply networks.
Blockchain, Supply Chain Visibility, EPCIS, Predictive Analytics, ETA Forecasting, Cold Chain, Smart Contracts, Anomaly Detection, MLOps, Interoperability
Federated Learning-Based Anomaly Detection for Privacy-Preserving Cloud Security
The exponential growth of cloud computing has made robust anomaly and intrusion detection systems critical for safeguarding enterprise infrastructure. However, traditional centralized security architectures rely on aggregating massive volumes of raw network logs onto a single server, posing severe privacy risks, regulatory compliance challenges, and significant bandwidth overhead. As cyber threats become more sophisticated, the necessity to analyze distributed cloud traffic without exposing sensitive proprietary data or creating a vulnerable single point of failure has driven the demand for novel, privacy-preserving security paradigms. To address these challenges, this paper introduces a Federated Learning-Based Anomaly Detection framework designed specifically for multi-tenant cloud environments. Our decentralized approach enables multiple cloud nodes to collaboratively train a robust, global Deep Learning intrusion detection model by exchanging only privacy-enhanced parameter updates, ensuring raw data never leaves its local origin. Experimental evaluation utilizing the CIC-IDS2017 dataset demonstrates that our proposed federated model achieves over 97% detection accuracy—closely matching centralized baselines—while substantially reducing data transmission volumes and strictly preserving data confidentiality across distributed cloud networks.
Federated Learning, Anomaly Detection, Privacy, Cloud, Security
Copyright (c) 2025 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
Blockchain for Securing Healthcare Data Transfers in MultiCloud
The healthcare industry is experiencing a massive digital transformation characterized by the rapid adoption of cloud computing for storing and processing sensitive patient data. However, as organizations increasingly employ multi-cloud architectures—leveraging services from multiple providers such as AWS, Azure, and Google Cloud—the complexity of data management and security escalates dramatically. Sensitive data is fragmented across distributed systems, raising the risks of unauthorized access, data breaches, and compliance violations under frameworks like HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation). This manuscript explores Date of Publication: 26-05-2026 the application of blockchain technology as a decentralized trust mechanism for securing healthcare data transfers in multi-cloud environments. The research investigates how blockchain’s immutability, distributed ledger transparency, and cryptographic integrity can enhance confidentiality, authentication, and traceability in healthcare data exchanges. The study synthesizes academic literature and real-world use cases to demonstrate that blockchain-enabled architectures can ensure secure interoperability, tamper-proof audit trails, and consent-driven data sharing between heterogeneous cloud ecosystems. Through a mixed-method approach integrating literature review, simulationmodeling, and comparative evaluation, this paper establishes that blockchain significantly reduces the probability of data tampering and unauthorized access by over 60% compared to traditional encryption-based multi-cloud systems. The findings highlight the transformative role of blockchain in enabling trustless collaboration among healthcare stakeholders while ensuring regulatory compliance, patient privacy, and operational efficiency.
Blockchain, Multi-Cloud, Healthcare Data Security, Interoperability, Data Integrity, HIPAA Compliance, Distributed Ledger, Smart Contracts, Cryptography, Cloud Computing
Strategic Role of Data Engineering in Digital Pharmacy Operations
The rapid digital transformation of the pharmaceutical industry has redefined how data is managed, analyzed, and utilized across the healthcare ecosystem. Data engineering stands at the center of this evolution, enabling the integration of disparate systems, streamlining real-time insights, and ensuring regulatory compliance in digital pharmacy operations. As pharmacies move from manual inventory systems and paper prescriptions toward fully digitized and AI-driven models, robust data engineering pipelines digital Date of Publication: 25-05-2026 pharmacy ecosystems by enabling interoperability between electronic health records (EHRs), supply chains, telemedicine platforms, and patient engagement systems. It also investigates the integration of advanced data technologies such as cloud computing, distributed databases, and data lakes, which collectively improve prescription accuracy, medication adherence monitoring, and pharmaceutical logistics. facilitate operational efficiency, predictive analytics, patient safety, and decisionmaking. This manuscript explores how data engineering provides the structural backbone for The paper further outlines the challenges in designing secure, scalable, and compliant data frameworks aligned with healthcare standards like HIPAA and GDPR. The methodology involves an analytical synthesis of industry studies, architecture analysis, and quantitative evaluation of performance outcomes. Findings highlight significant improvements inmedication delivery times, inventory optimization, and patient service quality following the adoption of well-structured data pipelines. The study concludes that data engineering is not merely a technical function but a strategic enabler of value-based digital pharmacy ecosystems that connect patients, providers, and suppliers through intelligent, datadriven networks.
Data Engineering, Digital Pharmacy, Cloud Data Pipelines, Healthcare Analytics, Electronic Health Records, Data Governance, Pharmacy Automation, Predictive Analytics, Regulatory Compliance, Big Data in Healthcare
Audit-Ready Cloud Migration Framework for Regulated Industries
As enterprises across regulated sectors—such as healthcare, finance, pharmaceuticals—accelerate insurance, their and journey toward digital transformation, cloud migration becomes both a technological enabler and a regulatory challenge. Traditional migration frameworks emphasize scalability, cost savings, and operational agility but often neglect the stringent audit, compliance, and data governance requirements that define these industries. The absence of an audit-centric migration strategy can result in fragmented traceability, compliance drift, and extended audit remediation cycles. compliance-driven model designed to ensure continuous auditability, traceable data movement, and regulatory alignment throughout the migration lifecycle. ARCMF integrates principles of compliance-bydesign, automated evidence collection, and realtime compliance monitoring into every stage, from pre-migration assessment to post-migration operation. By embedding tools like AWS Audit Manager, Azure Policy, and GCP Security Command Center, the framework transforms compliance from a reactive task into a proactive governance layer. Using This research introduces the Audit-Ready Cloud Migration Framework (ARCMF)—a mixed-methods analysis and case evaluations from 25 regulated organizations between 2021 and 2024, the study demonstratesmeasurable improvements: audit cycle time reduced by 41%, compliance deviations decreased by 36%, and evidence collection effort halved. The framework’s layered architecture— covering governance, risk, migration monitoring, evidence storage, and continuous assurance— enables auditors and compliance officers to maintain real-time visibility into control performance and system integrity. This manuscript not only validates ARCMF as an enabler of compliant cloud transformation but also highlights its strategic role in building digital trust ecosystems across industries. The research concludes that future audit-ready systems should integrate AI-driven compliance analytics, blockchain-based immutability, and policy-ascode paradigms to achieve self-verifiable compliance and adaptive regulatory intelligence—essential for sustainable digital transformation in an era of evolving data sovereignty laws.
Audit-readiness, Cloud migration, Regulated industries, Compliance-by-design, Cloud governance, Data residency, Continuous auditing, Evidence automation, Regulatory compliance.
Management Perspectives on Data Governance in Healthcare Cloud Systems
The rapid migration of healthcare data to cloud platforms has revolutionized medical service delivery, research, and patient engagement. However, this transformation has also amplified the Date of Publication: 25-05-2026 HIPAA, GDPR, and HITECH, alongside governance models like data stewardship, metadata management, and accountability structures. Through a synthesis of literature, casebased evaluation, and empirical methodology, the study analyzes complexity of data governance— encompassing privacy, security, compliance, and ethical considerations. From a management perspective, ensuring robust data governance in healthcare cloud systems is a multidimensional challenge that intersects policy frameworks, technological controls, and organizational culture. This manuscript explores how healthcare organizations establish and manage governance frameworks that balance innovation with risk mitigation. It reviews industry standards such as managerial practices for implementing governance controls across hybrid and multi-cloud environments. The results demonstrate that effective governance correlates strongly with improved regulatory compliance, operational transparency, and patient trust. Yet, constraints such as interoperability gaps, resource scarcity, and ambiguous accountability persist. The study concludes that management must adopt a unified data strategy integrating AIdriven auditing, zero-trust architectures, andcross-functional data stewardship councils to ensure long-term governance resilience. Enhanced collaboration among clinical, IT, and compliance teams, backed by continuous policy refinement, can transform cloud-based healthcare systems into secure, compliant, and patient-centric ecosystems.
Healthcare Cloud Systems, Data Governance, Management Perspectives, HIPAA Compliance, Cloud Security, Interoperability, Data Zero Management, Cloud Regulation
Enhancing Pharmacy Compliance with Secure Cloud Pipelines
The global pharmaceutical industry operates in an environment of stringent regulatory oversight, complex data flows, and heightened cybersecurity risks. As digital transformation accelerates across healthcare ecosystems, pharmacies face increasing challenges in ensuring compliance with data protection laws, maintaining supply chain transparency, and adhering to Good Manufacturing Practice (GMP) and Health Insurance Portability and Accountability Act (HIPAA) standards. This manuscript explores how secure cloud pipelines—integrated frameworks combining cloud computing, automation, data governance, and cybersecurity controls—can revolutionize pharmacy compliance. By leveraging advanced cloud-native Date of Publication: 23-05-2026 technologies such as containerized microservices, encryption-at-rest, automated audit trails, and zero-trust access models, pharmacies can streamline operations while maintaining end-toend regulatory adherence. The study evaluates modern compliance automation approaches, examining frameworks like ISO 27001, GxP, FDA CFR Part 11, and EMA Annex 11 in cloud contexts. The proposed model combines cloudnative DevSecOps pipelines with real-time compliance monitoring, demonstrating improved transparency, reduced manual errors, and faster regulatory audits. Simulation-based results show that secure pipelines can enhance compliance accuracy by 32%, reduce manual documentation time by 46%, and lower audit response times by 58%. This paper concludes that integrating secure cloud pipelines into pharmaceutical workflows not only ensures sustained regulatory conformity but also strengthens data integrity, patient safety, and business continuity across the pharmacy value chain.
Pharmacy Compliance, Cloud Pipelines, Data Security, Regulatory Frameworks, DevSecOps, HIPAA, FDA 21 CFR Part 11, GxP, Automation, Zero-Trust Architecture, Cloud Governance, Audit Trails, Healthcare Cloud Integration
Explainable and Sustainable Artificial Intelligence in Finance: Opportunities, Risks, and Future Innovation
Artificial Intelligence is revolutionizing the banking and financial services sector through improved efficiency, data analytics, and innovative customer experience. AI is being used in several applications within the financial services sector such as algorithmic trading, credit scoring, compliance, and fraud detection. The current study emphasizes the opportunities offered by artificial intelligence to the financial sector while simultaneously discussing the associated challenges such as data security, ethics, algorithmic bias, and legal uncertainty. It also pertains to future topics, which include XAI, AI for sustainable finance, RegTech improvements, and hybrid human-AI collaboration. This research proposes to provide a complete evaluation of the prospects and challenges of AI in finance, along with providing recommendations for future studies.
Artificial Intelligence, Financial Services, Machine Learning, Explainable AI, Sustainable Finance
Integrated Anomaly Detection in Automated Cloud Data Engineering Frameworks
This paper gives an example of an automated data engineering solution that focuses on large-scale cloud analytics and attempts to solve issues such as high data velocity, variable workloads, and numerous sources of heterogeneous data. The proposed model aims to achieve maximum performance and improved reliability, while saving on manual steps by utilizing a single pipeline combining automated ingestion control, adaptive data transformation, and resource optimization in a closed-loop manner. The model has been tested to provide a data ingestion throughput of 6.7 units in comparison to traditional cloud analytics platforms, and thus improving analytics throughput by up to 28 percent, and reducing end to end pipeline latency to 360 units. In terms of tested variable workloads, the proposed model showed an average of 94 percent for scalability, and 9 units for elasticity defined as the immediate response to adaptive demand by the model. The model showed data processing consistency of 97.9 percent and accuracy of 97.2 percent through confidence-based filtering, and early validation and schema alignment, in the end reducing automation overhead to 11 percent, demonstrating a high degree of operational autonomy. Enhanced system reliability and availability improved to 99.5 percent and fault recovery time was recorded at 16 units boosting system robustness. In improved resource utilization, the model achieved 91 percent balancing optimal arrangement of computing resources relative to workload, and overall reducing operational resource expenditure. In general, the findings support that the proposed framework provides a reliable, cost-effective, and scalable foundation for next-generation cloud analytics systems in dynamic environments.
AI in Commerce Education : Digital Changes in India's Curriculum
The rapid advancement of Artificial Intelligence (AI) is significantly transforming the landscape of commerce education in India, prompting a shift from traditional teaching methodologies to digitally enriched, skill-oriented learning environments. This study examines the evolving role of AI in reshaping the commerce curriculum, focusing on its impact on pedagogy, student competencies, and institutional practices. It explores how AI-driven tools such as adaptive learning platforms, automated assessment systems, and data analytics applications are enhancing educational delivery and enabling personalized learning experiences. The research also investigates the integration of interdisciplinary domains, including financial technology, business analytics, and digital marketing, into commerce education to meet contemporary industry demands. Furthermore, the study highlights the challenges associated with AI adoption, including limited faculty readiness, infrastructural disparities, and concerns related to data privacy and ethical usage. By analyzing existing literature and current educational practices, the paper identifies critical gaps in curriculum standardization, practical implementation, and long-term impact assessment. The findings suggest that while AI has the potential to significantly improve learning outcomes and employability, its effective integration requires strategic policy support, teacher training, and a balanced approach to ethical considerations. This research contributes to the understanding of digital transformation in India’s commerce education system and provides insights for policymakers, educators, and academic institutions aiming to develop future-ready curricula aligned with technological advancements.
Artificial Intelligence, Commerce Education, Digital Transformation, Curriculum Development, India, Educational Technology.
Clinical Trial Data Sharing Through Blockchain-AI Platforms
Clinical trial data are essential public goods, yet sharing them remains constrained by fragmented infrastructure, inconsistent consent management, complex regulatory landscapes, and legitimate concerns about privacy, security, and scientific misuse. Traditional repositories and bilateral data-use agreements often struggle to provide verifiable provenance, enforceable permissions, and scalable mechanisms for cross-institutional analysis. This manuscript proposes and analyzes a reference architecture for a permissioned blockchain–AI platform that enables compliant, trustworthy, and efficient sharing and secondary use of clinical trial data. The platform couples a consortium ledger for tamper-evident consent, access control, and auditability with a privacy-preserving AI layer that supports federated learning, differential privacy, and secure aggregation across sites. Interoperability is addressed via HL7 FHIR-based schemas and metadata harmonization pipelines; off-chain encrypted stores (e.g., content-addressed storage) manage bulk data while on-chain smart contracts encode governance rules, data-use conditions, and revocation. We situate the approach within current policy frameworks (e.g., ICMJE data sharing, EU GDPR, NIH Data Management and Sharing Policy, EMA Policy 0070) and synthesize evidence from the healthcare blockchain and federated learning literatures regarding security, performance, and adoption barriers. A methodology section details system components, identity/consent models, threat assumptions, and evaluation metrics (governance, privacy, model utility, and operational performance). The results section presents a design-level analysis and an implementation blueprint derived from prior prototypes and standards, along with realistic operational scenarios—such as consent withdrawal mid-trial, cross-border data requests, and audit preparation for regulators. We conclude that blockchain–AI platforms can materially improve verifiability, accountability, and privacy-by-design for trial data sharing, provided that governance is robust, identity and key management are user-centric, and interoperability and change management are prioritized.
Securing AI-Based Diagnostic Models Using Distributed Ledgers
Artificial intelligence (AI) now frequently supports high-stakes diagnostic decisions in radiology, pathology, cardiology, dermatology, and beyond. Yet the same models are exposed to integrity risks (data poisoning, adversarial perturbations), intellectual-property (IP) theft, provenance gaps, and governance liabilities, especially when models are trained across institutions and deployed at the edge. This manuscript proposes and details a comprehensive, ledger-centric security blueprint for AI diagnostics that couples permissioned distributed ledgers with privacy-preserving federated learning (FL), confidential computing, secure aggregation, model provenance artifacts, and IP protection (watermarking/fingerprinting). We synthesize the literature on blockchain-enabled learning and medical AI threats, align the design with NIST AI RMF and ISO/IEC 23894 risk management expectations, and map controls to emerging regulatory obligations (EU AI Act; HIPAA Security Rule modernization). We then present a systems methodology—DLT-MedGuard—covering threat models, data and model lineage capture, consent and access control via smart contracts, on-chain attestation of trusted execution environments, and reproducible release management via model cards and dataset datasheets anchored on-chain. A statistical analysis using an illustrative evaluation shows that DLT-MedGuard can preserve diagnostic performance while reducing successful poisoning and model-extraction rates, at modest latency overheads suitable for clinical settings. We conclude with a forward research agenda spanning post-quantum security, zero-knowledge attestations, energy-aware consensus, formal verification of smart contracts, and harmonized cross-border compliance. Overall, distributed ledgers do not merely “store hashes”; when designed as a verifiable provenance and control fabric, they materially raise the security baseline for AI-based diagnostics without undermining clinical utility.
AI-Blockchain Integration for Remote Patient Monitoring and Alerts
Remote Patient Monitoring (RPM) has matured from episodic teleconsultations to continuous, sensor-driven care supported by edge analytics and cloud services. Yet three friction points persist: (i) privacy and trust in data handling, (ii) interoperability across fragmented health information systems, and (iii) timely, auditable alerting that can be verified across organizations. This manuscript proposes a reference architecture that fuses Artificial Intelligence (AI) for streaming physiological inference with permissioned blockchain for tamper-evident logging, consent management, and cross-institutional data exchange. The design: (1) captures multi-modal signals from Internet of Medical Things (IoMT) wearables; (2) performs on-device/edge AI for anomaly detection to minimize latency and exposure of raw data; (3) persists summaries and cryptographic hashes on a consortium blockchain while storing large payloads off-chain; (4) implements smart-contract-based consent and alert workflows integrated with HL7® FHIR® resources; and (5) supports privacy-preserving learning (federated learning) to continually improve models without centralizing protected health information (PHI). We synthesize the most recent guidance from WHO and FDA on telemedicine/RPM, FHIR-based interoperability, and global privacy frameworks (HIPAA, GDPR, India’s DPDP Act), and we translate these into concrete design controls. A qualitative evaluation across security, latency, and governance dimensions suggests the approach can reduce alert time, strengthen provenance and auditability, and enable compliant data minimization. We conclude with an implementation roadmap, known risks (e.g., key management, model drift, edge heterogeneity), and future research directions such as zero-knowledge proofs for selective disclosure and formal verification of smart-contracted clinical alerts.
Fraud Detection in Cryptocurrency Transactions Using Deep Learning
Cryptocurrency’s promise of open, programmable finance has been shadowed by an evolving spectrum of fraud—investment scams, phishing, ransomware monetization, and money-laundering pipelines that hop across chains and jurisdictions. Traditional rule-based monitoring struggles with scale, concept drift, and adversarial obfuscation (mixers, peel chains, cross-chain bridges). This manuscript synthesizes the state of the art and proposes an end-to-end deep learning framework for fraud detection that blends graph neural networks (GNNs) on transaction graphs with temporal sequence models, representation learning for wallets and entities, and risk-aware active learning to leverage sparse labels. We ground the discussion in public datasets (e.g., Elliptic1/Elliptic2, BitcoinHeist, XBlock-ETH) and up-to-date industry intelligence (Chainalysis, TRM Labs) and map the methodology to regulatory expectations (FATF’s standards and the Travel Rule). We describe a modular pipeline covering data engineering, graph construction, feature learning, semi-supervised training, evaluation (ROC-AUC, PR-AUC, precision@k), and explainability (counterfactual traces, subgraph rationales). Results summarized from recent literature show that GNNs and temporal graph transformers generally outperform shallow models and static heuristics, especially when augmented with edge-time features and neighborhood motif learning. We conclude with deployment guidance—human-in-the-loop triage, concept-drift monitoring, and privacy-preserving analytics—and outline limitations (label scarcity, feedback loops, evasion tactics) and future work (federated graph learning, causal inference on chain, and cross-chain entity resolution).
Decentralized KYC Verification Systems with AI Facial Recognition
Know-Your-Customer (KYC) processes are foundational to anti–money laundering (AML) compliance but are often centralized, costly, and privacy-invasive. This manuscript proposes and analyzes a decentralized KYC (dKYC) architecture that binds government-issued identity attributes to a user-held digital wallet using Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), and AI-based facial recognition with liveness detection. The design minimizes data movement and disclosure by leveraging selective-disclosure credentials and zero-knowledge proofs (ZKPs) so that relying parties can verify eligibility (e.g., age, residency, risk tier) without accessing raw personally identifiable information (PII). We map the system to prevailing assurance frameworks (FATF digital ID guidance; NIST SP 800-63-3 Identity Assurance Levels), biometric performance/testing standards (ISO/IEC 30107-3 PAD; ISO/IEC 19795-1), and emerging regulatory regimes (EU AI Act, eIDAS 2.0, RBI directives on KYC and V-CIP). A comparative evaluation (simulated) contrasts centralized KYC with the proposed dKYC across error rates, onboarding time, and privacy risk. Results suggest dKYC can reduce median onboarding time by ~35–55%, lower document leakage risk through off-chain storage and selective disclosure, and maintain biometric security with PAD Level 2 while controlling demographic differential performance via auditable testing and thresholding. The paper contributes (i) a standards-aligned reference architecture; (ii) an evaluation plan referencing recognized biometric metrics (FMR/FNMR; APCER/BPCER); and (iii) a regulatory and governance blueprint for cross-jurisdictional adoption. Anchors to standards and regulation: DIDs and VCs enable user-controlled identifiers and machine-verifiable credentials; FATF clarifies when digital ID can satisfy CDD; NIST provides assurance and authentication guidance; ISO standards define biometric testing and anti-spoofing; the EU AI Act and eIDAS 2.0 frame lawful high-risk biometric use and wallets; RBI updates enable video-based KYC in India.
Self-Healing REST Services Using Artificial Intelligence in Multi-Cloud Environments
The current day digital applications are based on the search of RESTful services and use distributed cloud infrastructures. The problem of service reliability becomes much more complicated as organizations are switching to multi-cloud architecture to enhance their scalability, reliability, and independence with a single vendor. Conventional methods of monitoring and incident-response usually respond very slowly to failures like API spikes in latencies, service failures, container crashes, and configuration errors. These limits cause downtime, reduced performance and operational costs. Self-healing systems have become a good solution in order to overcome these challenges. A self-healing architecture allows software systems to automatically identify, diagnose, and recover failure automatically without human intervention. Together with Artificial Intelligence (AI), self-healing can forecast possible failures and optimise system behaviour, as well as automatically implement corrective measures. Monitoring systems that are powered by AI can process large amounts of system telemetry, logs and performance metrics to find anomalies and initiate automatic remediation. This paper suggests a self-healing model of AI to be used by REST services in the context of multi-cloud environments. The framework will combine machine learning-based outlier detection, automated fault diagnosis, predictive analytics and smart orchestration systems. Through cloud monitoring data, distributed tracing, and decision engines based on reinforcing learning, the proposed system is able to monitor the performance of REST services and take automatic recovery measures like restarting a container, rerouting of a service, auto-scaling, and reconfiguring an API gateway.
Self-Healing Systems, RESTful Services, Artificial Intelligence, Multi-Cloud Computing, Cloud-Native Architecture, Fault Detection, Autonomous Systems, Service Reliability, Machine Learning Monitoring, Distributed Systems.
Copyright (c) 2024 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
AI-Based Credit Risk Scoring in Blockchain-Enabled Lending Platforms
The rapid growth of blockchain-based lending—spanning decentralized finance (DeFi) pools, tokenized real-world assets, and permissioned distributed ledgers—demands risk assessment methods that can keep pace with borderless capital flows and pseudonymous borrowers. Conventional credit scoring, designed for centralized institutions and static data, struggles with fragmented identity, limited verifiable disclosures, and adversarial behaviors such as Sybil attacks or wash activity. This paper proposes a comprehensive, privacy-aware, and explainable AI framework for credit risk scoring tailored to blockchain-enabled lending platforms. The framework fuses three strata of information: (1) on-chain behavioral metrics (wallet age, transaction regularity, protocol interactions, counterparty quality), (2) off-chain verified data (KYC/AML results, income proxies, open-banking signals), and (3) reputational and network features (decentralized identity attestations, staking behavior, cross-platform defaults). Feature pipelines are secured with cryptographic assurances—verifiable computation and zero-knowledge proofs (ZKPs)—and governed by policies aligned with prudential crypto-asset guidance and data-protection law. We discuss modeling choices (gradient boosting, calibrated deep nets, and fairness-aware learners), explainability via SHAP and LIME, and deployment through smart contracts and oracles. A reference evaluation using public loan data and synthetic on-chain features illustrates improvements in AUC, expected loss calibration, and disparate impact mitigation versus baseline heuristics, while reducing time-to-decision and operational risk.
Blockchain Lending, DeFi, Credit Scoring, Explainable AI, Fairness, Zero-Knowledge Proofs, Decentralized Identity, Model Governance, Smart Contracts, Risk Management
Ethics of AI-Driven Surveillance with Immutable Data Logs
AI-driven surveillance systems—combining computer vision, sensor fusion, and predictive analytics—are rapidly diffusing into public safety, workplace monitoring, retail analytics, and critical infrastructure. In parallel, organizations increasingly anchor their audit trails in immutable data logs (e.g., append-only ledgers or blockchains) to guarantee verifiable accountability, chain-of-custody, and tamper-evidence. This paper examines the ethical terrain at that intersection. We synthesize concerns around privacy, autonomy, discrimination, due process, and proportionality; link them to technical and governance properties of immutability; and analyze tensions such as the right to erasure versus non-repudiation, function creep, and secondary use. Methodologically, we pair a conceptual-normative analysis with a simulation of an AI surveillance pipeline that incorporates immutable logging, differential privacy for analytics, and an “audit-trigger” that gates model updates. Statistical analysis on simulated events (N ≈ 1,000,000 over 30 days) suggests that immutable logs—when combined with targeted audits—can reduce false-positive disparities between demographic cohorts while improving investigative traceability. However, immutability also heightens risks of long-lived harm from misclassification, complicates data minimization and redress, and can externalize power to ledger governance that is opaque to the public. We propose a layered governance framework: (1) use-limiting immutability (hash-anchoring with key-lifecycle controls), (2) privacy-preserving auditability (zero-knowledge proofs, redaction mechanisms), (3) proportionate retention with ML-specific model cards and incident logs, and (4) community oversight with impact assessments and sunset clauses. The results underscore that immutable logging is neither intrinsically ethical nor unethical; its legitimacy depends on design choices, procedural safeguards, and distribution of accountability across institutions and communities.
AI Surveillance, Immutable Logs, Blockchain, Accountability, Privacy, Fairness, Transparency, Auditability, Governance, Differential Privacy
Liability in AI-Blockchain-Enabled Autonomous Organizations
As decentralized autonomous organizations (DAOs) mature into AI-enabled, on-chain collectives that can own assets, contract, and act through smart contracts and autonomous agents, the question “who is liable when things go wrong?” becomes central. This manuscript develops a comparative, doctrine-informed framework for allocating liability in AI-blockchain-enabled autonomous organizations (AI-BAOs). We synthesize case law (e.g., CFTC v. Ooki DAO; Sarcuni v. bZx DAO), evolving statutory schemes (Wyoming, Tennessee, Utah, Marshall Islands), and major regulatory instruments (EU AI Act; EU Product Liability Directive; MiCA) to map how tort, contract, securities, consumer, and product liability attach to participants (founders, token-holders, developers), service providers (oracles, custodians), and AI system “providers” and “deployers.” We propose a layered liability model: (1) entity shield (DAO/LLD/DAO LLC statutes), (2) functional roles (controller, provider, deployer, maintainer), and (3) use-case risk (financial vs. physical-world effects). A ten-jurisdiction comparative table illustrates differential risk under a transparent scoring rubric and shows that jurisdictions with DAO entity statutes reduce member personal-liability exposure but not platform or product-liability exposure when AI systems cause harm. We close with actionable governance patterns—liability-aware bylaws, role-based indemnities, AI assurance dossiers, incident response playbooks, and human-in-the-loop overrides—compatible with current law while remaining technology-agnostic. The analysis clarifies that AI-BAOs are not lawless: liability follows function and foreseeability, while new EU rules extend strict liability to software/AI defects and the U.S. courts have shown willingness to treat token-holder collectives as partnerships where no entity shield exists.
AI-Enabled DAOs, Liability, Product Liability, Securities, Governance, MiCA, EU AI Act, DAO LLC, General Partnership Risk, Safe Harbors