Scientific Journal of Artificial Intelligence and Blockchain Technologies
Journal particulars: Title: Scientific Journal of Artificial Intelligence and Blockchain Technologies 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 : https://wa.me/919456916244 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-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
Copyright (c) 2026 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
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
Decentralized AI and the Future of AI Rights Management
As artificial intelligence (AI) systems scale across sectors, the regimes governing who may access, use, audit, license, and benefit from models, weights, datasets, prompts, and outputs remain fragmented. “AI rights management” (AIRM) refers to the technical, legal, and institutional mechanisms that allocate these rights and duties across creators, data contributors, model publishers, deployers, and end-users. Centralized approaches—terms of service enforced by platforms, siloed registries, and proprietary compliance APIs—struggle to provide transparency, composability, and cross-jurisdictional enforceability, especially when models and content are remixed across organizational and national boundaries. This manuscript argues that decentralized identity, verifiable provenance, content authenticity, and programmable licensing—coordinated by ledgers and decentralized storage—offer a credible path to future-proof AIRM. We synthesize insights from emerging regulations (e.g., the EU AI Act, the EU Data Act), public-law guidance (e.g., U.S. Copyright Office positions on human authorship), standards (W3C Verifiable Credentials, Decentralized Identifiers, PROV), and industry efforts (C2PA, IPFS, Solid pods, open and responsible AI licensing). Building on this base, we propose a layered architecture: (1) decentralized identity and role binding; (2) machine-verifiable rights assertions and provenance; (3) tokenized or programmable licensing and revenue distribution; (4) auditability and dispute resolution through cryptographic attestations and governed data trusts. Methodologically, we employ a design-science synthesis with normative legal analysis and scenario-based evaluation in creative media and dataset contribution contexts. The results show how decentralized primitives can make AI rights legible and enforceable ex ante (through embedding rights in artifacts and workflows) rather than ex post policing. We conclude with a scope and limitations section that surfaces governance pitfalls (e.g., sybil risk and unequal bargaining power), regulatory alignment challenges, and open research questions on watermark robustness, cross-chain portability, and human-centric remedies.
Decentralized AI, Rights Management, Provenance, Verifiable Credentials, Decentralized Identifiers, C2PA, Data Trusts, Programmable Licensing, EU AI Act, Copyright
Smart Contracts and Legal Enforcement Across Jurisdictions
Smart contracts—self-executing code that automates performance of agreed terms—are moving from proof-of-concepts to production in financial markets, supply chains, digital media, and data-sharing ecosystems. Their cross-border use exposes long-standing private-international-law questions (choice of law, jurisdiction, recognition and enforcement) to new technical realities (immutability, pseudonymity, decentralization, oracles, and automated execution). This manuscript synthesizes statutory developments, soft-law instruments, case trends, and regulatory experiments across common-law and civil-law systems to assess enforceability and remedies when “code meets law.” It distinguishes (i) smart legal contracts (code plus natural-language terms) from (ii) code-only arrangements and (iii) hybrid architectures that rely on oracles and off-chain governance. A comparative analysis reviews the UK’s common-law approach (UKJT legal statement and digital-asset reforms), the EU Data Act’s “essential requirements” for smart contracts used in data-sharing, U.S. e-signature and UCC Article 12 reforms, Switzerland’s DLT Act, Germany’s eWpG, France’s DEEP/PACTE framework, Italy’s statutory recognition of smart contracts, Singapore’s ETA/PSA model, and India’s IT Act recognition of e-contracts. Building on these sources, we propose a ten-jurisdiction Smart-Contract Enforceability Index (SC-EI) and offer a doctrinal-plus-technical methodology for drafting, evidence, and dispute resolution. Results show: (1) enforceability is strongest where e-signature/e-record laws, property-law clarity for digital assets, and arbitration pathways co-exist; (2) regulatory “guardrails” (e.g., EU Data Act safe-termination controls) are rising; (3) private-international-law instruments (Rome I, Hague Principles, New York Convention) remain the backbone for cross-border resolution. We conclude with a best-practice checklist (governing law, forum, off-chain override, oracles, auditability, kill-switch, evidence mapping) and outline future research on conflict-of-laws for decentralized systems, on-chain ADR, and standards for verifiable execution logs.
Smart Contracts, Enforceability, Cross-Border, Arbitration, UCC Article 12, EU Data Act, UKJT, DLT Act, E-Signatures, Private International Law
AI-Based Threat Intelligence Sharing on Decentralized Networks
Cyber threat intelligence (CTI) has become indispensable for anticipating, detecting, and mitigating sophisticated attacks that evolve faster than any single organization can track. Traditional, hub-and-spoke CTI exchanges—centralized repositories, mailing lists, or vendor-managed portals—struggle with timeliness, trust, privacy, and single-point-of-failure risks. This manuscript proposes and elaborates an AI-based, privacy-preserving threat intelligence sharing framework built atop decentralized networks. Artificial intelligence components automate ingestion from heterogeneous sources, extract indicators of compromise (IOCs) and tactics, techniques, and procedures (TTPs), correlate multi-source signals across organizations, and continuously score indicator quality, confidence, and relevance. A decentralized substrate—using permissioned distributed ledgers and peer-to-peer overlays—ensures provenance, immutability, and resilient dissemination while enforcing fine-grained access policies (e.g., Traffic Light Protocol), lineage tracking, and incentive-compatible governance. We review the state of the art in CTI formats (STIX/TAXII), sharing platforms (MISP, OpenCTI), and knowledge bases (MITRE ATT&CK). We then synthesize advances in federated learning, secure aggregation, differential privacy, and private set intersection to support collaborative analytics without exposing sensitive telemetry. The methodology section details an end-to-end architecture: (1) AI pipelines for NLP-based IOC/TTP extraction, graph learning for cross-organizational correlation, anomaly detection for novel threats, and active learning loops with analysts; (2) a decentralized trust layer with verifiable credentials, reputation-weighted consensus, and smart contracts for curation and slashing; (3) privacy-preserving protocols for secure multi-party collaboration; and (4) interoperability bridges to existing CTI ecosystems via STIX 2.1/TAXII 2.1 and MISP connectors. A results section presents a proof-of-concept evaluation and design-space trade-offs (precision/recall, timeliness, deduplication, ledger latency, and byzantine robustness). The paper concludes with limitations and practical adoption pathways for sectoral ISACs/ISAOs, critical infrastructure operators, and multinational consortia.
Cyber Threat Intelligence, Decentralized Networks, Federated Learning, Differential Privacy, STIX/TAXII, Blockchain, Reputation Systems, Graph Neural Networks, MISP, MITRE ATT&CK
Blockchain for Identity Theft Prevention in Digital AI Applications
Escalating identity theft—fueled by large‐scale data breaches, AI-assisted social engineering, and deepfakes—undermines trust in digital systems and especially in AI-driven applications where automated agents transact, personalize content, and make consequential decisions. This manuscript proposes a standards-aligned, privacy-preserving reference architecture that uses blockchain to anchor decentralized identifiers (DIDs), verifiable credentials (VCs), selective-disclosure cryptography, and revocation registries; combines device-bound, phishing-resistant authentication (passkeys); and integrates content provenance signals for AI outputs. We synthesize the state of the art (W3C DID/VC, OpenID4VCI/OpenID4VP, ISO/IEC 18013-5 mDL, C2PA, NIST SP 800-63 and 800-207, ENISA), then present a methodology—BC-Guard—for securing AI user and agent identity life cycles: enrollment, authentication, authorization, transaction attestation, and post-event audit. A qualitative results section analyzes expected risk reductions for common identity-theft attack paths (credential phishing, account takeover, synthetic identity KYC fraud, and real-time deepfake impersonation). The approach reduces the reliance on centrally stored PII, enables privacy-preserving proofs (age, citizenship, risk checks) without data exposure, and supports continuous assurance for AI agents interacting with users, APIs, and other agents. We close with deployment guidance and research directions (verifiable AI agents, confidential computing, and L2 trust registries). (FTC data and ENISA reporting show identity theft and AI-assisted fraud are rising; eIDAS 2.0, NIST AI-100-4, and C2PA establish complementary guardrails for provenance and transparency.)
Blockchain, Self-Sovereign Identity, Decentralized Identifiers, Verifiable Credentials, Selective Disclosure, OpenID4VC, Passkeys (FIDO), Zero Trust, C2PA, eIDAS 2.0, mDL, AI Safety
Bias and Discrimination in Decentralized AI Decision Systems
Decentralized AI decision systems—spanning federated learning, peer-to-peer optimization, DAO-governed models, and blockchain-orchestrated inference—promise resilience, privacy, and transparency by distributing data, compute, and control. Yet decentralization does not automatically guarantee fairness. This paper examines how bias and discrimination emerge and persist when decision-making is pushed to the edge or collectively governed, and how those dynamics differ from centralized pipelines. We synthesize the literature on algorithmic bias, fairness metrics, federated learning with non-IID data, and token-based governance to articulate a socio-technical framework for risk. We then propose and test a mixed-methods methodology combining (i) a conceptual risk model mapping bias vectors (data, model, governance, incentive, and identity layers), (ii) a simulation on heterogeneous subpopulations under three deployment regimes—centralized baseline, decentralized stake-weighted governance, and decentralized with fairness and governance mitigations, and (iii) a statistical analysis using standard equality-of-opportunity and calibration measures. In a synthetic evaluation configured to stress real-world non-IID skew and wealth concentration in governance, a naïve decentralized regime amplifies parity gaps (e.g., equalized-odds TPR gaps increase by ~3–5 percentage points versus centralized), primarily due to (a) minority underrepresentation in local silos, (b) emergent power-law concentration in token voting, and (c) protocol-level incentive misalignment favoring short-term accuracy. A mitigated design—differentially private group-reweighted training, group distributionally robust optimization (Group DRO), model-card/datasheet governance requirements, and quadratic voting with identity checks—reduces disparities to below centralized baselines on core metrics while preserving decentralization benefits. We conclude with implementation guidance: align incentives with fairness constraints, measure and publish group-level performance continuously, and embed governance primitives (e.g., quadratic funding/voting, appeals, rotating audits) capable of resisting both model and governance capture.
Decentralized AI, Fairness, Discrimination, Federated Learning, DAO Governance, Blockchain, Non-IID Data, Equalized Odds, Quadratic Voting, Bias Mitigation
AI in Cyber Law Enforcement and Forensic Evidence Collection
Artificial intelligence (AI) is transforming how law-enforcement agencies investigate cybercrime and collect, analyze, and present digital evidence. This manuscript examines the end-to-end lifecycle of AI-enabled digital forensics—from first response and triage to acquisition, interpretation, legal admissibility, and courtroom presentation—through the lens of internationally recognized standards and emerging regulatory frameworks. We situate AI tools (e.g., anomaly detection for log triage, NLP for case intelligence, computer vision for media forensics, and model-agnostic explainers for transparency) within well-established forensic process models and chain-of-custody requirements. We analyze challenges unique to cloud and mobile environments, propose a standards-aligned methodology for deploying and validating AI in forensic workflows, and discuss substantive legal gatekeeping tests for expert evidence (FRE 702/Daubert/Frye) alongside the evolving constraints of the EU AI Act for law-enforcement biometric uses. A discussion of fairness, robustness, and auditability addresses risks highlighted in empirical studies (e.g., demographic performance differentials in face recognition; predictive policing feedback loops). A results section synthesizes expected operational benefits and measurable safeguards from a pilot-style implementation, while acknowledging limitations and governance needs. We conclude with a pragmatic blueprint that integrates AI capabilities without compromising evidentiary integrity, due process, or human rights, and we provide 20 authoritative references to support adoption and oversight.
AI Forensics, Digital Evidence, Cyber Law Enforcement, Chain of Custody, Cloud Forensics, EU AI Act, Daubert, FRE 702, ISO/IEC 27037, Fairness and Bias
Zero-Knowledge Proofs in Blockchain for Secure AI Model Sharing
Secure sharing of AI models across organizational boundaries is hard: providers want to protect intellectual property (model weights, architectures, and training data provenance), while consumers want cryptographic assurance that an advertised model was actually used and that a claimed evaluation (or compliance property) is correct. This manuscript proposes and evaluates a blockchain-backed design that uses zero-knowledge proofs (ZKPs) to make AI model sharing verifiable, privacy-preserving, and auditable. We synthesize the state of the art in ZK systems (zk-SNARKs, PLONK, Bulletproofs, zk-STARKs, and recursive schemes) and recent advances in zero-knowledge machine learning (zkML). Building on these, we present a practical architecture: models are registered on-chain by committing to immutable fingerprints; off-chain provers generate ZK proofs of (i) correct inference by a committed model, (ii) basic policy compliance (e.g., license scope; dataset-use attestations), and (iii) optional training process attestations via proof-of-learning artifacts. We report a simulation study comparing Groth16, PLONK, and STARK-style provers for realistic inference circuits and show that Groth16 yields the smallest proofs and fastest verification for moderate circuits, while PLONK offers circuit universality with similar verification costs and STARKs trade larger proofs for transparency and post-quantum assumptions. Across 300 synthetic trials, median verifier time remained sub-25 ms and proof sizes ranged from ~0.2 KB (Groth16) to ~90 KB (STARK) for common inference tasks, enabling economical on-chain verification. We discuss design choices (hashes, recursion, and gas budgeting), limitations (prover cost, model scale, privacy scope), and a roadmap toward policy-aware, privacy-preserving model exchanges for regulated industries.
Zero-Knowledge Proofs, zk-SNARK, PLONK, zk-STARK, Blockchain, zkML, Proof-of-Learning, Model Provenance, Privacy, Verifiable Inference
Causal Reasoning as a Path to Explainable and Generalizable Artificial Intelligence
Artificial Intelligence (AI) systems, particularly those based on deep learning, have achieved extraordinary success in pattern recognition and predictive tasks. However, their reliance on correlation-based learning has raised serious concerns regarding explainability, robustness, fairness, and generalization. These limitations are especially problematic in high-stakes domains such as healthcare, autonomous systems, finance, and governance, where AI decisions must be transparent, reliable, and adaptable to changing environments. Causal reasoning offers a promising paradigm to address these challenges by enabling AI systems to move beyond surface-level correlations toward an understanding of underlying cause–effect relationships. This paper explores causal reasoning as a foundational pathway to explainable and generalizable artificial intelligence. It examines the theoretical underpinnings of causal inference, contrasts causal and correlational learning, and analyzes how causal models enhance explainability and out-of-distribution generalization. The paper further reviews emerging approaches for integrating causal reasoning into modern AI systems, including structural causal models, counterfactual learning, invariant representations, and hybrid neuro-symbolic architectures. Key applications, challenges, and future research directions are discussed. The study argues that causal reasoning is not merely an auxiliary feature but a necessary component for building trustworthy, human-aligned, and generalizable AI systems.
Causal reasoning, explainable AI, generalization, structural causal models, counterfactuals, robust AI, causal inference
Copyright (c) 2024 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
Optimizing Problem Management through Proactive Knowledge Base Development
Effective problem management is crucial for maintaining the stability and efficiency of IT services. Traditional reactive approaches often lead to recurring issues and increased downtime. This paper explores the optimization of problem management through proactive knowledge base development, emphasizing the integration of knowledge management practices to identify and resolve underlying causes before they escalate into incidents. By analyzing case studies and implementing structured methodologies, organizations can enhance their problem management processes, reduce incident frequency, and improve overall service quality.
Problem Management, Knowledge Base Development, Proactive Approach, IT Service Management, Root Cause Analysis, Knowledge Management Practices, Incident Reduction, Service Quality Improvement, ITIL Framework, Continuous Improvement.
Comparative Study of Tier I vs Tier II Support Models in ITSM
This study investigates the comparative effectiveness of Tier I and Tier II support models within IT Service Management (ITSM). Tier I, often the first point of contact, addresses basic user issues, while Tier II handles more complex technical problems. Through a systematic literature review and statistical analysis, the research evaluates key performance indicators (KPIs) such as resolution time, customer satisfaction, and operational costs. The findings suggest that while Tier II support is more resource-intensive, it is essential for resolving intricate issues that Tier I cannot address. The study also identifies gaps in current research and proposes areas for future exploration, including the integration of AI in support processes and the impact of remote work on support efficiency.
Tier I support, Tier II support, IT Service Management, comparative analysis, resolution time, customer satisfaction, operational costs, AI integration, remote work, support efficiency.
Enhancing Root Cause Analysis Efficiency in Large-Scale IT Operations
Root Cause Analysis (RCA) is pivotal in identifying and mitigating underlying issues in large-scale IT operations. Traditional RCA methods often struggle with the complexity and scale of modern IT infrastructures. This manuscript explores advanced methodologies and case studies that enhance RCA efficiency, focusing on automation, machine learning, and system integration. By examining contemporary approaches and real-world applications, we aim to provide insights into optimizing RCA processes for large-scale IT environments.
Root Cause Analysis, IT Operations, Automation, Machine Learning, System Integration, Incident Management, Scalability, Efficiency, Case Studies, IT Infrastructure.
Incident Response Optimization in Global Command Centers: Lessons from Financial IT Operations
In the evolving landscape of enterprise IT, maintaining a robust security posture is paramount. The integration of Configuration Management Databases (CMDB) with Incident Management systems offers a strategic approach to enhance security measures. This manuscript explores the significance of CMDB in providing comprehensive visibility into IT assets, their configurations, and interdependencies. By examining case studies and methodologies, the paper demonstrates how this integration facilitates proactive incident detection, efficient response, and continuous improvement in security protocols. The findings underscore the critical role of integrated systems in fortifying enterprise security frameworks.
Enterprise Security, Configuration Management Database, Incident Management, ITIL, Cybersecurity, Proactive Defense, Vulnerability Management, Automation, Risk Assessment, IT Operations
Improving Enterprise Security Posture through Integrated CMDB and Incident Management
Operational Excellence in IT Audit through AI-Augmented Control Testing
The integration of Artificial Intelligence (AI) into IT audit processes has ushered in a transformative era, enhancing operational excellence through AI-augmented control testing. This paper explores the evolution of IT audit methodologies, emphasizing the pivotal role of AI in automating control assessments, improving accuracy, and ensuring compliance. By examining current practices and proposing a simulation-based approach, the study aims to provide a comprehensive understanding of AI's impact on IT audit efficiency and effectiveness.
AI in IT Audit, Control Testing, Operational Excellence, Automation, Compliance, Simulation Research
ServiceNow CMDB Governance as a Pillar for Cybersecurity Resilience
In the contemporary digital landscape, organizations face escalating cyber threats that necessitate robust resilience strategies. The Configuration Management Database (CMDB) within ServiceNow offers a centralized repository of IT assets and their interdependencies, serving as a critical component in enhancing cybersecurity resilience. This manuscript explores the pivotal role of CMDB governance in fortifying an organization's cybersecurity posture. By ensuring data accuracy, establishing clear governance frameworks, and integrating CMDB with other IT service management processes, organizations can proactively identify vulnerabilities, streamline incident response, and ensure compliance with regulatory standards. The research underscores the importance of a well-maintained CMDB as a foundational element in achieving operational resilience and mitigating cyber risks.
ServiceNow CMDB, Cybersecurity Resilience, IT Governance, Configuration Management, Incident Response, Operational Resilience, IT Service Management, Data Accuracy, Compliance, Risk Management
Continuous Compliance in Hybrid IT Environments with Integrated ITSM-GRC Workflows
In the evolving landscape of hybrid IT infrastructures, organizations face the dual challenge of maintaining operational agility while ensuring stringent compliance with regulatory standards. Traditional compliance models, characterized by periodic audits and manual interventions, are increasingly inadequate in addressing the dynamic nature of modern IT environments. This paper explores the integration of IT Service Management (ITSM) and Governance, Risk, and Compliance (GRC) frameworks to establish a continuous compliance model within hybrid IT ecosystems. By embedding compliance controls into ITSM workflows and leveraging automation, organizations can achieve real-time compliance monitoring, proactive risk management, and enhanced operational resilience. The study presents a conceptual framework, outlines key research objectives, and discusses the implications of this integrated approach on organizational compliance strategies.
Hybrid IT, Continuous Compliance, ITSM, GRC, Automation, Policy-as-Code, Risk Management, Cloud Security, Compliance Automation, Regulatory Compliance
2nd International Seminar on Bridging Tradition and Technology: AI Perspectives through the lens of Indian Knowledge System
This special issue of the Scientific Journal of Artificial Intelligence and Blockchain Technologies (SJAIBT) presents the proceedings of the Second International Seminar on “Bridging Tradition and Technology: AI Perspectives through the Lens of Indian Knowledge Systems,” an interdisciplinary forum dedicated to the convergence of artificial intelligence (AI) and Indian Knowledge Systems (IKS). Across 67 peer-reviewed papers, the issue explores how AI intersects with diverse fields—including education, ethics, environmental sustainability, digital literacy, gender studies, public policy, and cultural heritage—to address contemporary challenges of sustainability, equity, and cultural preservation. An overarching theme of the seminar is the interdisciplinary bridging of traditional wisdom and modern technology. The collected studies emphasize that aligning AI innovation with indigenous knowledge and ethical principles is key to developing responsible AI systems and fostering inclusive technological progress. Key focal areas include the transformation of education through AI-enhanced learning aligned with IKS perspectives, as well as strategies for women’s empowerment and digital literacy aimed at fostering an inclusive AI-driven society. In addition, several papers propose innovative teacher training models grounded in ancient pedagogical insights to prepare educators for AI-integrated classrooms. Other contributions examine holistic health frameworks that integrate AI with traditional wellness practices and explore AI-driven approaches to environmental sustainability informed by indigenous ecological wisdom. Together, these works underscore a paradigm in which cutting-edge AI applications are harmonized with cultural heritage, social values, and environmental sustainability objectives. This integrated approach illustrates a roadmap for future research and public policy initiatives that leverage both historical wisdom and modern science for societal benefit.
Achieving Seamless Integration of Financial and Supply Chain Systems using modern SAP tools
Financial and supply chain system integration is required to improve operational efficiency and decision-making processes in contemporary businesses. Nevertheless, most organizations continue to experience difficulties in achieving seamless integration due to heterogeneous systems, poor real-time data exchange, and inefficient process workflows. Conventional approaches tend to fail to offer real-time visibility, leading to the creation of data silos, which make it difficult for financial and supply chain departments to collaborate effectively. This research investigates the potential presented by existing SAP tools to bridge the gaps and provide a common platform for financial and supply chain function integration. SAP S/4HANA, in combination with integrated modules such as SAP Ariba, SAP Integrated Business Planning (IBP), and SAP Fiori, provides sophisticated capabilities to improve operational efficiency, improve data accuracy, and facilitate seamless communication between finance and supply chain departments. The research investigates the processes through which the tools facilitate data synchronization, automate processes, and improve cross-functional collaboration. By concentrating on real-time analytics, cloud-based applications, and AI-based insights, this research seeks to illustrate how contemporary SAP tools can overcome conventional integration challenges, allowing businesses to optimize their supply chain and financial functions. The research findings will identify best practices, outline common issues, and offer actionable recommendations for organizations seeking to implement a strong, future-proof integrated system. This research extends the knowledge of how businesses can leverage the wide capabilities of SAP tools to achieve operational excellence and facilitate long-term growth.
Financial integration, supply chain systems, SAP S/4HANA, SAP Ariba, SAP IBP, real-time data synchronization, workflow automation, cross-functional collaboration, AI-driven insights, cloud-based solutions, operational efficiency, data accuracy.
Copyright (c) 2025 Scientific Journal of Artificial Intelligence and Blockchain Technologies https://creativecommons.org/licenses/by-nc/4.0
Blockchain Governance Models for Open Source AI Communities
Open source AI communities increasingly steward high-impact artifacts—foundation model weights, safety evaluations, curated datasets, and inference tooling—whose governance must balance openness, safety, sustainability, and inclusivity. Blockchain technologies promise tamper-evident records, programmable institutions, and novel incentive designs for these communities, yet the governance models available—from straightforward token-weighted voting to quadratic and reputation-weighted systems—differ markedly in participation, resilience, and fairness
AI-Assisted Sharding Techniques for Blockchain Scalability
Sharding—partitioning a blockchain’s state and workload across multiple committees—remains one of the most promising approaches to scale permissionless ledgers without sacrificing decentralization. However, static shard layouts and coarse, rule-based reconfiguration often underperform when faced with bursty demand, non-stationary access patterns, or cross-shard dependencies. This manuscript proposes an AI-assisted sharding framework that integrates (i) graph-neural forecasters for short-horizon workload prediction on account/contract interaction graphs, (ii) a reinforcement-learning (RL) policy for online decisions about shard sizing, committee rotation, and cross-shard routing, and (iii) safety wrappers that enforce cryptographic and protocol constraints (e.g., minimum committee sizes, randomness beacons, and rotation limits) to preserve security.
AI-Augmented Blockchain Fork Detection and Recovery Protocols
Forks—temporary or permanent divergences in a blockchain’s ledger—arise from benign network asynchrony, configuration heterogeneity, or adversarial behavior such as selfish mining and eclipse/routing attacks. Although modern fork-choice rules (e.g., longest-chain/most-work, GHOST variants) and finality gadgets reduce the practical impact of short reorgs, detection remains largely reactive and recovery is often ad hoc at the protocol or operational layer. This paper proposes an AI-augmented framework for (i) early fork risk prediction from peer-to-peer (P2P) telemetry and block-header streams, (ii) real-time fork detection using multi-view anomaly models, and (iii) policy-driven recovery that coordinates mempool hygiene, relay routing, and safe rollback/finality choices.
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.
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
Bridging IT Audit and ITSM: A Process-Centric Approach Using ServiceNow
This research explores the integration of IT Audit and IT Service Management (ITSM) through a process-centric approach utilizing ServiceNow. The study examines how aligning ITSM processes with auditing frameworks enhances organizational efficiency, compliance, and service delivery. By analyzing existing literature and conducting simulations, the paper identifies key areas where ServiceNow can facilitate this integration, offering a comprehensive model for organizations aiming to streamline their IT operations.
IT Audit, IT Service Management, ServiceNow, Process Integration, Compliance, Simulation, Organizational Efficiency, Service Delivery, Framework Alignment, IT Operations.
Automating Compliance Monitoring in ITSM Platforms Using Machine Learning
This manuscript explores the integration of Machine Learning (ML) into IT Service Management (ITSM) platforms to automate compliance monitoring. As organizations face increasing regulatory demands and complex IT environments, traditional manual compliance processes become inadequate. ML offers a transformative approach by enabling proactive, real-time compliance oversight. The paper discusses the application of ML in automating compliance tasks, enhancing accuracy, and reducing operational overhead. Furthermore, it presents a case study demonstrating the effectiveness of ML-driven compliance automation in an enterprise ITSM context.
Machine Learning, IT Service Management, Compliance Monitoring, Automation, Predictive Analytics, Anomaly Detection, Service Level Agreements, Regulatory Compliance, IT Operations, Enterprise ITSM
ServiceNow Performance Analytics for Real-Time IT Audit Dashboards
This study examines the integration of ServiceNow Performance Analytics into IT audit dashboards, focusing on its real-time data visualization capabilities. The research aims to assess how these dashboards enhance audit efficiency, accuracy, and decision-making processes. Through a simulation-based methodology, key performance indicators (KPIs) were analyzed to determine the impact of real-time analytics on audit outcomes. Findings indicate significant improvements in audit response times and issue resolution, demonstrating the effectiveness of ServiceNow Performance Analytics in modernizing IT audit practices.
ServiceNow, Performance Analytics, IT Audit, Real-Time Dashboards, Key Performance Indicators, Simulation Study, Data Visualization, Audit Efficiency, Decision-Making, IT Governance
Reducing Audit Lifecycle Costs through CMDB Automation and Standardization
This study explores the impact of Configuration Management Database (CMDB) automation and standardization on reducing audit lifecycle costs in IT environments. By integrating automated discovery tools and standardized processes, organizations can achieve significant improvements in data accuracy, compliance, and operational efficiency. The research highlights best practices, presents a statistical analysis of audit preparation time reduction, and identifies key areas for further enhancement.
CMDB, automation, standardization, audit lifecycle, IT asset management