Research Article
Chatbot-Enhanced Mental Health First Aid in Corporate Settings: Addressing Risks, Implementing Crisis Management, and Promoting Employee Well-Being
Sourav Banerjee*,
Ayushi Agarwal,
Ayush Kumar Bar
Issue:
Volume 12, Issue 1, June 2024
Pages:
1-4
Received:
18 December 2023
Accepted:
29 December 2023
Published:
11 January 2024
Abstract: This research rigorously explores the implementation of Chatbot-Enhanced Mental Health First Aid (MHFA) within corporate contexts, presenting an innovative paradigm for mitigating mental health risks and bolstering employee well-being. Amidst increasing recognition of the pervasive nature of mental health challenges in the workplace, this research elucidates the potential of AI-driven chatbots to augment conventional MHFA methodologies. These sophisticated chatbot systems offer an accessible, stigma-free avenue for support, facilitating early detection and preliminary counselling in instances of mental health crises. The study meticulously evaluates the efficacy of chatbots in crisis intervention and their seamless integration into holistic corporate wellness frameworks. These encompass a spectrum of initiatives, including proactive health promotion programs, adaptable work policies, and comprehensive employee assistance schemes. The research also navigates the intricacies of embedding MHFA programs in organisational structures, addressing challenges like resistance to technological and procedural shifts and concerns around data privacy. Strategic methodologies are proposed to navigate and surmount these barriers effectively. A pivotal aspect of this research is the ethical deployment and privacy preservation in the utilisation of chatbots. The paper provides a thorough critique of the ethical considerations and privacy safeguards essential in the management of sensitive mental health information, ensuring adherence to ethical standards and confidentiality. Concludingly, the study posits that the integration of chatbot-enhanced MHFA can substantially reduce workplace mental health stigma, align with legal compliance mandates, and facilitate cost-efficiency. This innovative approach supports the development of a more comprehensive and accessible mental health infrastructure within corporate settings. Looking ahead, the paper advocates for further empirical research to assess the longitudinal impacts of chatbot-enhanced MHFA, explore diverse employee interactions with these systems, and advance AI algorithms for tailored mental health support. The infusion of AI-driven chatbots in MHFA programs is heralded as a pivotal advancement, signifying a major stride towards fostering more resilient, supportive, and mentally healthy workplace environments.
Abstract: This research rigorously explores the implementation of Chatbot-Enhanced Mental Health First Aid (MHFA) within corporate contexts, presenting an innovative paradigm for mitigating mental health risks and bolstering employee well-being. Amidst increasing recognition of the pervasive nature of mental health challenges in the workplace, this research ...
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Research Article
Prioritization of Application Security Vulnerability Remediation Using Metrics, Correlation Analysis, and Threat Model
Santanam Kasturi*,
Xiaolong Li,
John Pickard,
Peng Li
Issue:
Volume 12, Issue 1, June 2024
Pages:
5-13
Received:
8 February 2024
Accepted:
23 February 2024
Published:
13 March 2024
Abstract: As part of a continuing research for evaluating threats posed for exposed attack surface, this study will provide a consolidated view of exploitability of vulnerable applications presenting a web attack surface of an organization exposed to an attacker. While testing and scanning technologies like Static Analysis Security Testing (SAST), Dynamic Analysis Security Testing (DAST), Application Ethical Hack (Penetration Testing), a monitoring technology like the Web Application Firewall (WAF) provides web traffic information of the number of transaction requests for every application under study. To ensure validity, reliability, and completeness of observation multiple applications must be observed. Research from a prior study is referenced that shows correlation between incoming WAF requests and existing vulnerabilities. Using correlation analysis, vulnerabilities metrics, and a threat model analysis help identify pathways to an attack. A vulnerability map-based attack tree can be developed using Common Weakness Enumeration (CWE) and Common Vulnerabilities and Exposures (CVE) information. The threat model analysis and vulnerability-based attack tree can help in simulation studies of possible attacks. This attack tree will show the linkages between vulnerabilities and a lineage pointing to how an attack could travel from the incoming WAF requests to deep down into the application code of exposed and existing, open vulnerabilities travelling laterally to create a more expanded attack crossing trust boundaries using application data flow.
Abstract: As part of a continuing research for evaluating threats posed for exposed attack surface, this study will provide a consolidated view of exploitability of vulnerable applications presenting a web attack surface of an organization exposed to an attacker. While testing and scanning technologies like Static Analysis Security Testing (SAST), Dynamic An...
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