From Policy to Pipeline: A Governance Framework for AI Development and Operations Pipelines Talal Butt, Muhammad Iqbal, Noor Arshad IEEE Access, 2026 Artificial intelligence systems increasingly operate in high-risk domains where regulatory frameworks such as the EU AI Act, NIST AI RMF, and ISO/IEC 42001 impose explicit evidence and accountability requirements. However, existing engineering practice remains largely manual, retrospective, and decoupled from operational pipelines, resulting in inconsistent provenance, limited reproducibility, and inadequate clause-level traceability. This paper introduces Governance as Evidence for AI Pipelines (GEAP), a pipeline-native governance framework that expresses regulatory and organizational policies as machine-interpretable Governance as Code rules. GEAP integrates governance directly into a unified SDLC–MLOps execution spine by enforcing promotion decisions at five gates—Data, Training, Validation, Release, and Operations—each of which emits signed, content-addressed artifacts into a tamper-evident Evidence Backbone. These artifacts are assembled into a per-run Conformity Bundle, from which the proposed Clause-to-Artifact Traceability mechanism deterministically renders clause coverage across multiple regulatory regimes without manual crosswalks or duplicated documentation. The framework further introduces quantitative governance metrics that measure adequacy, completeness, stability, and evidence hygiene. A detailed synthetic case study of an intensive-care sepsis early-warning system demonstrates GEAP’s ability to standardize promotion control, detect policy violations, and produce replayable, audit-ready compliance manifests in a high-risk clinical context. The results show that governance can operate as a deterministic, reproducible, and verifiable pipeline property rather than an external documentation exercise, enabling more disciplined, transparent, and accountable AI deployment practices.
Explainable AI: Applications, Challenges, Current Solutions and Future Research Directions Talal Butt, Muhammad Iqbal ACM International Conference Proceeding Series, 2025 The fast integration of artificial intelligence (AI) into essential domains such as healthcare, finance, and the operation of autonomous systems has come to bear significant challenges related to transparency, trust, and accountability. The more complex the AI models and deep learning frameworks are, the more pressing the necessity of Explainable AI (XAI) becomes. This paper surveys the state of the art in XAI, addressing not only the benefits that such applications bring but also discussing the challenges inherently met in the practice of XAI. Although such transparency techniques are crucial for fostering user trust and the ethical deployment of AI systems, the field is relatively young and has several serious roadblocks. These include a widespread debate on the interpretability-performance trade-off, deficiencies in evaluation metrics, and limitations in terms of scalability. It also focuses on current research efforts to overcome challenges and develop better, more robust, context-aware XAI techniques. These requirements will be central in the future of artificial intelligence, in which the demands for transparency and interpretability are bound to increase with the rise in the embedding of artificial intelligence in society. This study highlights the importance of ongoing research into XAI to ensure that AI developments are influential but also responsible and trustworthy.
A Comprehensive Framework for Intelligent, Scalable, and Performance-Optimized Software Development Noor Arshad, Talal Ashraf Butt, Muhammad Iqbal IEEE Access, 2025 Integrating Artificial Intelligence (AI) into the Software Development Life Cycle (SDLC) has become necessary to enhance efficiency, scalability, and performance in modern software systems. Instead of incorporating the AI functionality into their SDLC, traditional SDLC models typically add-on the AI software functionality after they have integrated AI functionality into their application or software process. Because of this, developers undergo inefficiencies in their development workflows, experience performance bottlenecks during testing, and experience challenges of incorporating AI to improve an application’s performance through optimization. This paper proposes a new AI-Optimized Software Development Life Cycle (AI-SDLC), which is a holistic and comprehensive framework that encases the embedded AI capabilities and optimization strategies throughout the SDLC process during every stage of the system development, so that requirements-gathering, development, testing, and maintenance are hybrid software processes and not dictated by AI vs. traditional software development processes. AI-SDLC presents new development roles, such as AI Integration Specialist, Code Optimizer, and UX Optimization Specialist, which helps developers work across disciplines and increases collaborative interaction between traditional developers and AI engineers. AI-SDLC also utilizes an AI-driven automated hybrid software process in areas such as requirement elicitation, design/architecture validation, testing, deployment monitoring, and scalability to produce robust high-performance systems in all areas of practicing software development life cycle work. The discourse includes a rich case study based on a Smart Logistics Management System to demonstrate practical implementation of the AI-SDLC and how it facilitates improvement in system efficiency and improved user experience. Additionally, the discussion also highlights the possibilities of AI-SDLC practical implementation in other industrial domain areas such as e-Commerce, finance, aviation and enterprise solution based projects with practical considerations for implementation. In conclusion, the discussion provides findings that support AI-SDLC as a structured and intelligence-driven approach to Software Development Life Cycle implementation that addresses the weaknesses of traditional software design and development frameworks.
IntegriScan: An Operations-First Architecture for Real-Time Deepfake Voice Fraud Detection Talal Ashraf Butt, Hanan Al-Herbawi, Hamda Alraeesi, Aaesha Almehrzi, Taif Almurshidi, Reem Alyammahi 2025 10th International Conference on Information Technology Trends Itt 2025, 2025 Synthetic voice fraud has evolved from a theoretical threat to an operational crisis, capable of bypassing conventional security and costing millions. While AI research has produced increasingly accurate detectors, a vast gap remains between benchmark performance and a deployable, trusted system ready for high-stakes environments. This work introduces IntegriScan, an operations-first decision engine designed to close that gap. We embed a state-of-the-art, self-supervised Wav2Vec 2.0 backbone within a privacy-aware, audit-ready web architecture engineered for real-time, single-utterance screening. On a held-out test set of $\mathbf{1, 1 7 9}$ diverse utterances, IntegriScan achieves $\mathbf{9 2. 2 \%}$ accuracy with a recall-focused balance ideal for fraud triage. Crucially, a 45-participant user study confirms high perceived trust and usefulness, validating our human-centric design. IntegriScan provides a new benchmark for operational readiness, demonstrating that the fusion of powerful speech representations with disciplined system design can deliver the fast, reliable, and—above all—governable deepfake screening that modern finance and telecom desperately need.
Edge Intelligence based Social Internet of Things for Smart Cities Talal Ashraf Butt ACM International Conference Proceeding Series, 2022 Social Internet of Things (SIoT), inspired by human social networks, is the next phase in the evolution of the Internet of Things (IoT) that enables a plethora of devices with diverse sensors to build and maintain social relationships among them. These social relationships can be created autonomously by devices based on their mutual interests, context and the requirements of different applications. The smart city paradigm is built on large-scale IoT. It can benefit from SIoT to improve the provision of value-added services for citizens by exploiting the social relationships of devices. This paper reviews the SIoT concepts and challenges and proposes an architecture to reap its potential for smart city applications. Furthermore, it emphasizes the societal implication of SIoT in a smart city scenario.
Future Smart Cities: Vision, Challenges and Technology Trends Talal Ashraf Butt ACM International Conference Proceeding Series, 2021 The smart cities paradigm is being adopted by many nations worldwide to offer smart services to their citizens while optimizing the cost of managing crowded and expanding urban areas. The concept is mainly based on the Internet of Things (IoT) infrastructure to utilize its sensed data of intelligent, interconnected devices to create value-added novel services. However, the smart cities paradigm is still not fully realized because of several challenges, such as heterogeneity of the devices and standards involved. This paper provides a vision for future smart cities and emphasizes its challenges. Moreover, the paper discusses the current technology trends that have the potential to materialize the vision of future smart cities. The paper also proposes a scalable and extensible architecture to employ smart cities’ current and future trends.
Applications of Machine Learning in Big-Data Analytics and Cloud Computing Applications of Machine Learning in Big Data Analytics and Cloud Computing, 2021
Context-aware cognitive disaster management using fog-based Internet of Things Talal Ashraf Butt Transactions on Emerging Telecommunications Technologies, 2019 The natural and man‐made disasters are inevitable in many circumstances. Thus, an effective disaster management system (DMS) is vital for any community that can use state‐of‐the‐art technologies to deal with such cataclysmic events. There is plethora of latest technologies such as Internet of Things (IoT) and cloud and fog Computing that have provided the required infrastructure and connectivity to collect and analyze data from users and physical environment for cognitive decision‐making. This article analyzes different stages of DMSs, and existing IoT solutions are also discussed that are focused on prevention, preparation, response, and recovery from disasters. It also proposes a context‐aware fog‐based IoT architecture to realize a cognitive DMS that can learn from the collected and synthesized data to reduce the impact of catastrophic events by taking immediate actions. Furthermore, the significance of the architecture is validated in different scenarios and inherent open research challenges are emphasized.
Governing What the EU AI Act Excludes: Accountability for Autonomous AI Agents in Smart City Critical Infrastructure TA Butt, M Iqbal, R Iqbal arXiv preprint arXiv:2605.01091 , 2026 2026
UGAF-ITS: A Standards Harmonization Framework and Validation Tool for Multi-Framework AI Governance in Distributed Intelligent Transportation Systems TA Butt, M Iqbal, R Iqbal arXiv preprint arXiv:2604.22789 , 2026 2026
From Policy to Pipeline: A Governance Framework for AI Development and Operations Pipelines TA Butt, M Iqbal, N Arshad IEEE Access 14, 1373-1397 , 2025 2025 Citations: 7
IntegriScan: An Operations-First Architecture for Real-Time Deepfake Voice Fraud Detection TA Butt, H Al-Herbawi, H Alraeesi, A Almehrzi, T Almurshidi, R Alyammahi 2025 10th International Conference on Information Technology Trends (ITT … , 2025 2025
Towards Reliable Explainable AI: A Novel Stability Metric for Trustworthy TA Butt, M Iqbal Proceedings of the 2025 10th International Conference on Multimedia Systems … , 2025 2025 Citations: 2
Towards Reliable Explainable AI: A Novel Stability Metric for Trustworthy Interpretations TA Butt, M Iqbal International Conference on Multimedia Systems and Signal Processing, 198-210 , 2025 2025 Citations: 1
A comprehensive framework for Intelligent, Scalable, and Performance-Optimized software development N Arshad, TA Butt, M Iqbal IEEE Access 13, 74062-74077 , 2025 2025 Citations: 14
Explainable ai: Applications, challenges, current solutions and future research directions T Butt, M Iqbal Proceedings of the 2024 the 12th International Conference on Information … , 2024 2024 Citations: 3
Fair-Share Methods for Scheduling Scientific Workflows in Cloud B Aldabaybah, T Alrawashdeh, TA Butt, K Almiani 2023 20th ACS/IEEE International Conference on Computer Systems and … , 2023 2023
Edge Intelligence based Social Internet of Things for Smart Cities TA Butt Proceedings of the 2022 10th International Conference on Information … , 2022 2022
Applications of Machine Learning in Big-Data Analytics and Cloud Computing SK Pani, S Tripathy, G Jandieri, S Kundu, TA Butt CRC Press , 2022 2022 Citations: 8
Future smart cities: vision, challenges and technology trends TA Butt Proceedings of the 2021 9th international conference on information … , 2021 2021 Citations: 12
Safe farming as a service of blockchain-based supply chain management for improved transparency R Iqbal, TA Butt Cluster Computing 23 (3), 2139-2150 , 2020 2020 Citations: 119
Privacy management in social internet of vehicles: review, challenges and blockchain based solutions TA Butt, R Iqbal, K Salah, M Aloqaily, Y Jararweh IEEE Access 7, 79694-79713 , 2019 2019 Citations: 183
Context-aware Cognitive Disaster Management using Fog-based Internet of Things TA Butt Transactions on Emerging Telecommunications Technologies , 2019 2019 Citations: 18
Social Internet of Vehicles TA Butt, R Iqbal, M Kehal 2019
Trust Management in Social Internet of Vehicles: Factors, Challenges, Blockchain and Fog Solutions R Iqbal, TA Butt, M Afzaal, K Salah International Journal of Distributed Sensor Networks., 10.1177/1550147719825820 , 2019 2019 Citations: 152
Security and Privacy in Smart Cities: Issues and Current Solutions TA Butt, M Afzaal Smart Technologies and Innovation for a Sustainable Future, 317--323 , 2019 2019 Citations: 45
Context-aware data-driven intelligent framework for fog infrastructures in internet of vehicles R Iqbal, TA Butt, MO Shafiq, MWA Talib, T Umar IEEE Access 6, 58182-58194 , 2018 2018 Citations: 77
Social Internet of Vehicles A GCC (Gulf Cooperation Council) Perspective T Butt, R Iqbal, M Kehal Global Information Diffusion and Management in Contemporary Society, Chapter 7 , 2018 2018 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
Privacy management in social internet of vehicles: review, challenges and blockchain based solutions TA Butt, R Iqbal, K Salah, M Aloqaily, Y Jararweh IEEE Access 7, 79694-79713 , 2019 2019 Citations: 183
Trust Management in Social Internet of Vehicles: Factors, Challenges, Blockchain and Fog Solutions R Iqbal, TA Butt, M Afzaal, K Salah International Journal of Distributed Sensor Networks., 10.1177/1550147719825820 , 2019 2019 Citations: 152
Social Internet of Vehicles: Architecture and Enabling Technologies T Butt, R Iqbal, SC Shah, T Umar Computers and Electrical Engineering 69 (C), 68-84 , 2018 2018 Citations: 131
Safe farming as a service of blockchain-based supply chain management for improved transparency R Iqbal, TA Butt Cluster Computing 23 (3), 2139-2150 , 2020 2020 Citations: 119
Context-aware data-driven intelligent framework for fog infrastructures in internet of vehicles R Iqbal, TA Butt, MO Shafiq, MWA Talib, T Umar IEEE Access 6, 58182-58194 , 2018 2018 Citations: 77
Adaptive and context-aware service discovery for the internet of things TA Butt, I Phillips, L Guan, G Oikonomou Conference on Internet of Things and Smart Spaces, 36-47 , 2013 2013 Citations: 54
TRENDY: An adaptive and context-aware service discovery protocol for 6LoWPANs TA Butt, I Phillips, L Guan, G Oikonomou Proceedings of the third international workshop on the web of things, 1-6 , 2012 2012 Citations: 53
Security and Privacy in Smart Cities: Issues and Current Solutions TA Butt, M Afzaal Smart Technologies and Innovation for a Sustainable Future, 317--323 , 2019 2019 Citations: 45
Context-aware Cognitive Disaster Management using Fog-based Internet of Things TA Butt Transactions on Emerging Telecommunications Technologies , 2019 2019 Citations: 18
Provision of adaptive and context-aware service discovery for the Internet of Things. TA Butt Loughborough University , 2014 2014 Citations: 15
A comprehensive framework for Intelligent, Scalable, and Performance-Optimized software development N Arshad, TA Butt, M Iqbal IEEE Access 13, 74062-74077 , 2025 2025 Citations: 14
Future smart cities: vision, challenges and technology trends TA Butt Proceedings of the 2021 9th international conference on information … , 2021 2021 Citations: 12
Design of a RESTful middleware to enable a web of medical things N Philip, T Butt, D Sobnath, R Kayyali, S Nabhani-Gebara, B Pierscionek, ... 2014 4th International Conference on Wireless Mobile Communication and … , 2014 2014 Citations: 12
Applications of Machine Learning in Big-Data Analytics and Cloud Computing SK Pani, S Tripathy, G Jandieri, S Kundu, TA Butt CRC Press , 2022 2022 Citations: 8
From Policy to Pipeline: A Governance Framework for AI Development and Operations Pipelines TA Butt, M Iqbal, N Arshad IEEE Access 14, 1373-1397 , 2025 2025 Citations: 7
Explainable ai: Applications, challenges, current solutions and future research directions T Butt, M Iqbal Proceedings of the 2024 the 12th International Conference on Information … , 2024 2024 Citations: 3
Towards Reliable Explainable AI: A Novel Stability Metric for Trustworthy TA Butt, M Iqbal Proceedings of the 2025 10th International Conference on Multimedia Systems … , 2025 2025 Citations: 2
Towards Reliable Explainable AI: A Novel Stability Metric for Trustworthy Interpretations TA Butt, M Iqbal International Conference on Multimedia Systems and Signal Processing, 198-210 , 2025 2025 Citations: 1
Social Internet of Vehicles A GCC (Gulf Cooperation Council) Perspective T Butt, R Iqbal, M Kehal Global Information Diffusion and Management in Contemporary Society, Chapter 7 , 2018 2018 Citations: 1
Governing What the EU AI Act Excludes: Accountability for Autonomous AI Agents in Smart City Critical Infrastructure TA Butt, M Iqbal, R Iqbal arXiv preprint arXiv:2605.01091 , 2026 2026
