This paper presents a multi-cloud networking archi-tecture built on zero trust principles and micro-segmentation to provide secure connectivity with authentication, authorization, and encryption in transit. The proposed design includes the multi-cloud network to support a wide range of applications and workload use cases, compute resources including contain-ers, virtual machines, and cloud-native services, including IaaS (Infrastructure as a Service (IaaS), PaaS (Platform as a service). Furthermore, open-source tools provide flexibility, agility, and independence from locking to one vendor technology. The paper provides a secure architecture with micro-segmentation and follows zero trust principles to solve multi-fold security and operational challenges.

This study evaluates the adoption of DevSecOps among small and medium-sized enterprises (SMEs), identifying key challenges, best practices, and future trends. Through a mixed methods approach backed by the Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) theory, we analyzed survey data from 405 SME professionals, revealing that while 68% have implemented DevSecOps, adoption is hindered by technical complexity (41 %), resource constraints (35 %), and cultural resistance (38 %). Despite strong leadership prioritization of security (73 %), automation gaps persist, with only 12 % of organizations conducting security scans per commit. Our findings highlight a growing integration of security tools, particularly API security (63 %) and software composition analysis (62 %), although container security adoption remains low (34 %). Looking ahead, SMEs anticipate artificial intelligence and machine learning to significantly influence DevSecOps, under-scoring the need for proactive adoption of AI -driven security enhancements. Based on our findings, this research proposes strategic best practices to enhance CI/CD) pipeline security including automation, leadership-driven security culture, and cross-team collaboration.

Securing Agentic Artificial Intelligence (AI) systems requires addressing the complex cyber risks introduced by autonomous, decision-making, and adaptive behaviors. Agentic AI systems are increasingly deployed across industries, organizations, and critical sectors such as cybersecurity, finance, and healthcare. However, their autonomy introduces unique security challenges, including unauthorized actions, adversarial manipulation, and dynamic environmental interactions. Existing AI security frameworks do not adequately address these challenges or the unique nuances of agentic AI. This research develops a lifecycle-aware security framework specifically designed for agentic AI systems using the Design Science Research (DSR) methodology. The paper introduces MAAIS, an agentic security framework, and the agentic AI CIAA (Confidentiality, Integrity, Availability, and Accountability) concept. MAAIS integrates multiple defense layers to maintain CIAA across the AI lifecycle. Framework validation is conducted by mapping with the established MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) AI tactics. The study contributes a structured, standardized, and framework-based approach for the secure deployment and governance of agentic AI in enterprise environments. This framework is intended for enterprise CISOs, security, AI platform, and engineering teams and offers a detailed step-by-step approach to securing agentic AI workloads.

This study investigates the prevalence and underlying causes of work-related stress and burnout among cybersecurity professionals using a quantitative survey approach guided by the Job Demands-Resources model. Analysis of responses from 50 cybersecurity practitioners reveals an alarming reality: 44% report experiencing severe work-related stress and burnout, while an additional 28% are uncertain about their condition. The demanding nature of cybersecurity roles, unrealistic expectations, and unsupportive organizational cultures emerge as primary factors fueling this crisis. Notably, 66% of respondents perceive cybersecurity jobs as more stressful than other IT positions, with 84% facing additional challenges due to the pandemic and recent high-profile breaches. The study finds that most cybersecurity experts are reluctant to report their struggles to management, perpetuating a cycle of silence and neglect. To address this critical issue, the paper recommends that organizations foster supportive work environments, implement mindfulness programs, and address systemic challenges. By prioritizing the mental health of cybersecurity professionals, organizations can cultivate a more resilient and effective workforce to protect against an ever-evolving threat landscape.

Romance scams (RS) inflict financial and emotional damage by defrauding victims under the guise of meaningful relationships. This research study examines RS trends in the U.S. through a quantitative analysis of web searches, news articles, research publications, and government reports from 2004 to 2023. This is the first study to use multiple sources for RS trend analysis. Results reveal increasing public interest and media coverage contrasted by a recent decrease in incidents reported to authorities. The frequency of research dedicated to RS has steadily grown but focuses predominantly on documenting the problem rather than developing solutions. Overall, findings suggest RS escalation despite declining official reports, which are likely obscured by low victim reporting rates. This high-lights the need for greater awareness to encourage reporting enabling accurate data-driven policy responses. Additionally, more research must focus on techniques to counter these crimes. With improved awareness and prevention, along with responses informed by more accurate data, the rising RS threat can perhaps be mitigated.

Ransomware and other malware inflict devastating financial and operational damage on organizations worldwide by exploiting deeply embedded, hard-to-detect vulnerabilities in their systems. Detecting these vulnerabilities in compiled code before malicious actors exploit them remains a critical challenge in cybersecurity. This research introduces TEDVIL (Transformer-based Embeddings for Discovering Vulnerabilities in Lifted Code), a novel framework which uses transformer-based embeddings to train neural networks to detect vulnerabilities in lifted code. The framework was implemented using bidirectional (BERT and RoBERTa) and unidirectional (GPT-1 and GPT-2) transformer-based models to generate embeddings for training Long Short-Term Memory (LSTM) neural networks to detect stack-based buffer overflows in Low-Level Virtual Machine (LLVM) intermediate representation code. For comparison, simpler word2vec models (Skip-Gram and Continuous Bag of Words) were also trained, and their embeddings were used to train LSTMs. The results show that the LSTMs using GPT-2 embeddings outperformed those using GPT-1, BERT, RoBERTa, and word2vec embeddings, achieving a top accuracy of 92.5% and an F1-score of 89.7%. Notably, these results are achieved when the embedding model is trained with a dataset of just 48,000 functions, demonstrating effectiveness in resource-constrained settings. The findings underscore the effectiveness of TEDVIL in identifying hard-to-detect vulnerabilities in compiled code, and lay the groundwork for future research in leveraging transformer-based models for vulnerability detection.