AI-driven digital ecosystems span diverse stakeholders including technology firms, regulators, accelerators and civil society, yet often lack cohesive ethical governance. This paper proposes a four-pillar framework (SCOR) to embed accountability, fairness, and inclusivity across such multi-actor networks. Leveraging a design science approach, we develop a Shared Ethical Charter(S), structured Co-Design and Stakeholder Engagement protocols(C), a system of Continuous Oversight and Learning(O), and Adaptive Regulatory Alignment strategies(R). Each component includes practical guidance, from lite modules for resource-constrained start-ups to in-depth auditing systems for larger consortia. Through illustrative vignettes in healthcare, finance, and smart city contexts, we demonstrate how the framework can harmonize organizational culture, leadership incentives, and cross-jurisdictional compliance. Our mixed-method KPI design further ensures that quantitative targets are complemented by qualitative assessments of user trust and cultural change. By uniting ethical principles with scalable operational structures, this paper offers a replicable pathway toward responsible AI innovation in complex digital ecosystems.

The emergence of AI agents powered by large language models (LLMs) marks a pivotal shift toward the Agentic Web, a new phase of the internet defined by autonomous, goal-driven interactions. In this paradigm, agents interact directly with one another to plan, coordinate, and execute complex tasks on behalf of users. This transition from human-driven to machine-to-machine interaction allows intent to be delegated, relieving users from routine digital operations and enabling a more interactive, automated web experience. In this paper, we present a structured framework for understanding and building the Agentic Web. We trace its evolution from the PC and Mobile Web eras and identify the core technological foundations that support this shift. Central to our framework is a conceptual model consisting of three key dimensions: intelligence, interaction, and economics. These dimensions collectively enable the capabilities of AI agents, such as retrieval, recommendation, planning, and collaboration. We analyze the architectural and infrastructural challenges involved in creating scalable agentic systems, including communication protocols, orchestration strategies, and emerging paradigms such as the Agent Attention Economy. We conclude by discussing the potential applications, societal risks, and governance issues posed by agentic systems, and outline research directions for developing open, secure, and intelligent ecosystems shaped by both human intent and autonomous agent behavior. A continuously updated collection of relevant studies for agentic web is available at: https://github.com/SafeRL-Lab/agentic-web.

As AI systems are integrated into social networks, there are AI safety concerns that AI-generated content may dominate the web, e.g. in popularity or impact on beliefs. To understand such questions, this paper proposes the Digital Ecosystem of Beliefs (Digico), the first evolutionary framework for controlled experimentation with multi-population interactions in simulated social networks. The framework models a population of agents which change their messaging strategies due to evolutionary updates following a Universal Darwinism approach, interact via messages, influence each other's beliefs through dynamics based on a contagion model, and maintain their beliefs through cognitive Lamarckian inheritance. Initial experiments with an abstract implementation of Digico show that: a) when AIs have faster messaging, evolution, and more influence in the recommendation algorithm, they get 80% to 95% of the views, depending on the size of the influence benefit; b) AIs designed for propaganda can typically convince 50% of humans to adopt extreme beliefs, and up to 85% when agents believe only a limited number of channels; c) a penalty for content that violates agents' beliefs reduces propaganda effectiveness by up to 8%. We further discuss implications for control (e.g. legislation) and Digico as a means of studying evolutionary principles.

The digital age, driven by the AI revolution, brings significant opportunities but also conceals security threats, which we refer to as cyber shadows. These threats pose risks at individual, organizational, and societal levels. This paper examines the systemic impact of these cyber threats and proposes a comprehensive cybersecurity strategy that integrates AI-driven solutions, such as Intrusion Detection Systems (IDS), with targeted policy interventions. By combining technological and regulatory measures, we create a multilevel defense capable of addressing both direct threats and indirect negative externalities. We emphasize that the synergy between AI-driven solutions and policy interventions is essential for neutralizing cyber threats and mitigating their negative impact on the digital economy. Finally, we underscore the need for continuous adaptation of these strategies, especially in response to the rapid advancement of autonomous AI-driven attacks, to ensure the creation of secure and resilient digital ecosystems.

The last decades have been characterized by unprecedented technological advances, many of them powered by modern technologies such as Artificial Intelligence (AI) and Machine Learning (ML). The world has become more digitally connected than ever, but we face major challenges. One of the most significant is cybercrime, which has emerged as a global threat to governments, businesses, and civil societies. The pervasiveness of digital technologies combined with a constantly shifting technological foundation has created a complex and powerful playground for cybercriminals, which triggered a surge in demand for intelligent threat detection systems based on machine and deep learning. This paper investigates AI-based cyber threat detection to protect our modern digital ecosystems. The primary focus is on evaluating ML-based classifiers and ensembles for anomaly-based malware detection and network intrusion detection and how to integrate those models in the context of network security, mobile security, and IoT security. The discussion highlights the challenges when deploying and integrating AI-enabled cybersecurity solutions into existing enterprise systems and IT infrastructures, including options to overcome those challenges. Finally, the paper provides future research directions to further increase the security and resilience of our modern digital industries, infrastructures, and ecosystems.

Credit and Finance for MSMEs: Today, the digital ecosystem is signifying its ability to overcome fundamental barriers to the progression of finance for inclusive and sustainable development. New-age banking solutions have evolved from the conventional brick and motor branches. From neobanks, and e-wallets, to buy now pay later (BNPL) and no-cost EMIs, there is a stark transformation across the economy's spending, lending, and borrowing patterns. The government has adopted a cognizant approach to lead the country towards a digital economy. The new wave has bought a paradigm shift in the MSME borrowing pattern as they adapt to the digital ecosystem.

The dawn of AI is a revolutionary step for the digital world. AI matures the brains of intelligent machines and fuels the innovations to derive maximum value from every business process. Sources said that AI will grow with a compound growth rate (CAGR) of 39.4% in 2022–2030. And in this period, the business process will be fully automated, and, to shape the futuristic plans, organizations will also vouch for their money to leverage AI in their workforce. If you are a business owner, it is high time to kick in something extraordinary in your business process.

In this decade of 21st century, world is seeing very different challenges in the power management in Power Generation, transmission, distribution & consumption. As most of the countries are taking on challenges on sustainability goals, there is significant Energy Transition happening towards more & more GREEN energy. Lot more sources of energy like Wind, Solar are becoming more viable and being adopted. However, this has put a clear expectation on adding intelligence into the existing products & solutions to manage this transition as well as adopting new digital software-based solutions. With the rapid advancement in the IoT & Cloud infrastructure, creating this intelligence is now feasible and economically viable.

According to research by the National Restaurant Association Research and Knowledge Group, the restaurant industry will be drastically different by the year 2030. WIthin a decade, it could be possible for an individual to approach a drive-through in an autonomous vehicle, order through an AI-powered voice ordering assistant, and eat food that was prepared by robots. One of the most startling aspects of this interaction is that there is not a single human involved besides the consumer. These changes will cause massive implications for every aspect of the sector, most importantly for the workers and consumers. Restaurants and food joints, once reliable venues for human interaction, will be important for the implementation of artificial intelligence (AI) technology.

In the past few years, AI has evolved to become one of the most powerful tools in tech history to bring machines and humankind together. Back in the day, AI was limited to speculations and fictional stories. But in the modern state, AI is no longer confined to laboratories and scientific labs, instead, it has become a part of our daily lives. Starting from search engines, call-center chatbots, to AI-enabled humanoid robots, there is a whole range of artificial intelligence products and services that are available in the market, which has not only accelerated the growth in the functional capacities of the industries but has also enhanced our existing living conditions. AI is a pressing priority in the modern era.