Digital Identity in the Modern Era: Navigating the Nexus of Security, Privacy, and Social Inclusion (Published)
Digital identity systems have become central to the functioning of modern digital economies and governance structures. With the proliferation of national ID programs and global digital identity initiatives, questions of data security, user privacy, and social inclusion have risen to the forefront. This paper explores the evolving landscape of digital identity, examining the balance between technological advancement and ethical responsibility. It presents a comparative view of global identity programs, including India’s Aadhaar and the EU’s eIDAS framework, alongside emergent models like decentralized and self-sovereign identity (SSI). Emphasis is placed on the role of identity transformation in enterprise cybersecurity and the integration of governance automation to ensure scalable, compliant, and inclusive identity architectures. The paper concludes with strategic considerations for designing equitable digital identity systems that respect individual rights while meeting operational demands.
Keywords: Data Privacy, authentication security, digital identity, digital inclusion, identity governance
Data Privacy and Security in AI-Driven Customer Platforms: A Cloud Computing Perspective (Published)
AI-driven customer experience platforms have transformed enterprise engagement strategies by leveraging large language models and cloud-native infrastructure to deliver personalized interactions across multiple channels. These sophisticated systems process substantial volumes of sensitive customer information across distributed cloud environments, introducing multifaceted security challenges beyond conventional cybersecurity frameworks. The integration of AI with cloud computing creates unique vulnerabilities, including prompt injection, data privacy concerns, content safety risks, technical exploitation vectors, and regulatory complexity. Addressing these challenges requires comprehensive architectural approaches spanning zero trust principles, proactive data protection strategies, secure MLOps pipelines, confidential computing, and robust output monitoring. The CYBERSECEVAL benchmark provides valuable insights into security vulnerabilities even among advanced systems, highlighting concerns with prompt injection, code generation capabilities, and the fundamental tradeoff between security and functionality. Effective protection demands a holistic strategy combining technical controls with governance frameworks, ongoing security evaluation, and organizational awareness. Financial institutions and other enterprises must balance innovation with robust security while maintaining compliance across multiple jurisdictions, ultimately requiring continuous adaptation to the rapidly evolving threat landscape in AI security.
Keywords: AI security, Cloud Computing, Data Privacy, customer experience platforms, prompt injection vulnerabilities.
Navigating the Implementation of Generative AI in Customer Support Contact Centers: Challenges and Strategic Approaches (Published)
This article addresses the multifaceted challenges organizations face when implementing generative artificial intelligence in customer support contact centers. As contact centers transition from traditional human agents and rule-based systems to AI-augmented environments, they encounter significant hurdles across multiple dimensions. The article systematically examines technical integration barriers with legacy systems, data privacy and regulatory compliance requirements across jurisdictions, agent adoption resistance and workforce transformation needs, return on investment measurement complexities, and continuous model refinement strategies. Through a comprehensive analysis of industry experiences, the article identifies critical success factors, including robust integration architectures with existing infrastructure, privacy-by-design approaches to compliance, comprehensive agent reskilling programs and performance metric recalibration, sophisticated ROI measurement frameworks that capture both direct and indirect benefits, and governance mechanisms for continuous model improvement. By addressing these interconnected challenges with strategic approaches, organizations can realize the substantial benefits of generative AI in contact centers while maintaining service quality and customer trust in an increasingly complex technological and regulatory landscape.
Keywords: Contact centers, Data Privacy, continuous model refinement, generative AI, technical integration, workforce transformation
Ethical and Privacy Implications of Cloud AI in Financial Services (Published)
The financial services sector has increasingly integrated cloud computing architectures and Artificial Intelligence (AI) technologies to enhance customer engagement, streamline operational processes, and maintain a competitive edge. While these advancements bring substantial benefits, they also introduce complex ethical considerations and privacy vulnerabilities. This paper aims to critically analyze the ethical ramifications and privacy implications associated with the deployment of AWS cloud-based AI solutions within the financial services ecosystem. It will examine select case studies from the sector, identify best practices in the implementation of these technologies, and provide strategic recommendations to effectively mitigate the associated risks.
Keywords: AWS, Data Privacy, Financial Services, bias mitigation, cloud AI, data security, ethical AI, machine learning, regulatory compliance, transparency in AI
Intelligent Horizons: Navigating the Benefits and Boundaries of AI-Driven Telemedicine (Published)
Telemedicine and artificial intelligence (AI) integration has revolutionized the healthcare system through accurate diagnosis, effective treatment, and remote consultations. Some of the technologies used in AI include machine learning algorithms and natural language processing technology, which help algorithms offer predictive analytics and personalized care. In addition, these technologies have reduced the clinical staff’s work burden and have led to increased patient engagement. However, despite these skyrocketing forward movements, AI-driven telemedicine faces challenges such as data privacy threats, bias in algorithm use, and the absence of harmonization between different platforms. Implementing these limitations is among the most significant factors that make telehealth services ethical, fair, and scalable. It is therefore essential to analyze the new role of AI in telemedicine, list the advantages and possible risks, and provide strategic recommendations for addressing current challenges. The findings hope to enlighten healthcare executives, legislators, and researchers on the opportunities and challenges of AI in the telemedicine sector.
Keywords: AI, Data Privacy, Digital healthcare, Patient outcomes, predictive analytics, telemedicine
A Review of Privacy Preserving Techniques inWireless Sensor Network. (Review Completed - Accepted)
This paper represents a review of privacy preserving techniques in wireless sensor network. Wireless sensor networks are not secure. To preserve privacy of wireless sensor network various techniques are discovered. A lot of work has been done to address challenges faced to preserve privacy of wireless sensor network. In this pa-per we represent a research on privacy preserving tech-niques used in location privacy, data privacy and net-work privacy. This paper should provide help for fur-ther research in privacy preservation in wireless sensor network
Keywords: Context Privacy, Data Privacy, Source Location Privacy
Data Leakage Detection (Published)
We study the following problem: A data distributor has given sensitive data to a set of supposedly trusted agents (third parties). Some of the data are leaked and found in an unauthorized place. The distributor must assess the likelihood that the leaked data came from one or more agents, as opposed to having been independently gathered by other means. We propose data allocation strategies (across the agents) that improve the probability of identifying leakages. These methods do not rely on alterations of the released data (e.g., watermarks). In some cases, we can also inject realistic but fake data records to further improve our chances of detecting leakage and identifying the guilty party.
Keywords: Allocation Strategies, Data Leakage, Data Privacy, Fake Records, Leakage