Balancing Security and Privacy: The Pivotal Role of AI in Modern Healthcare Systems
- URL: http://arxiv.org/abs/2601.15697v1
- Date: Thu, 22 Jan 2026 06:51:45 GMT
- Title: Balancing Security and Privacy: The Pivotal Role of AI in Modern Healthcare Systems
- Authors: Binu V P, Deepthy K Bhaskar, Minimol B,
- Abstract summary: This paper explores how artificial intelligence (AI) plays a crucial role in enhancing security while protecting user privacy.<n>We examine real-world examples from the healthcare sector to illustrate how organizations can implement AI solutions that strengthen security without compromising patient privacy.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: As digital threats continue to grow, organizations must find ways to enhance security while protecting user privacy. This paper explores how artificial intelligence (AI) plays a crucial role in achieving this balance. AI technologies can improve security by detecting threats, monitoring systems, and automating responses. However, using AI also raises privacy concerns that need careful consideration.We examine real-world examples from the healthcare sector to illustrate how organizations can implement AI solutions that strengthen security without compromising patient privacy. Additionally, we discuss the importance of creating transparent AI systems and adhering to privacy regulations.Ultimately, this paper provides insights and recommendations for integrating AI into healthcare security practices, helping organizations navigate the challenges of modern management while keeping patient data safe.
Related papers
- Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies [57.521647436515785]
We define frontier AI auditing as rigorous third-party verification of frontier AI developers' safety and security claims.<n>We introduce AI Assurance Levels (AAL-1 to AAL-4), ranging from time-bounded system audits to continuous, deception-resilient verification.
arXiv Detail & Related papers (2026-01-16T18:44:09Z) - Security-First AI: Foundations for Robust and Trustworthy Systems [0.0]
This manuscript posits that AI security must be prioritized as a foundational layer.<n>We argue for a security-first approach to enable trustworthy and resilient AI systems.
arXiv Detail & Related papers (2025-04-17T22:53:01Z) - Transforming Cyber Defense: Harnessing Agentic and Frontier AI for Proactive, Ethical Threat Intelligence [0.0]
This manuscript explores how the convergence of agentic AI and Frontier AI is transforming cybersecurity.<n>We examine the roles of real time monitoring, automated incident response, and perpetual learning in forging a resilient, dynamic defense ecosystem.<n>Our vision is to harmonize technological innovation with unwavering ethical oversight, ensuring that future AI driven security solutions uphold core human values of fairness, transparency, and accountability while effectively countering emerging cyber threats.
arXiv Detail & Related papers (2025-02-28T20:23:35Z) - Transparency, Security, and Workplace Training & Awareness in the Age of Generative AI [0.0]
As AI technologies advance, ethical considerations, transparency, data privacy, and their impact on human labor intersect with the drive for innovation and efficiency.<n>Our research explores publicly accessible large language models (LLMs) that often operate on the periphery, away from mainstream scrutiny.<n>Specifically, we examine Gab AI, a platform that centers around unrestricted communication and privacy, allowing users to interact freely without censorship.
arXiv Detail & Related papers (2024-12-19T17:40:58Z) - Position: Mind the Gap-the Growing Disconnect Between Established Vulnerability Disclosure and AI Security [56.219994752894294]
We argue that adapting existing processes for AI security reporting is doomed to fail due to fundamental shortcomings for the distinctive characteristics of AI systems.<n>Based on our proposal to address these shortcomings, we discuss an approach to AI security reporting and how the new AI paradigm, AI agents, will further reinforce the need for specialized AI security incident reporting advancements.
arXiv Detail & Related papers (2024-12-19T13:50:26Z) - Enhancing Guardrails for Safe and Secure Healthcare AI [0.0]
I propose enhancements to existing guardrails frameworks, such as Nvidia NeMo Guardrails, to better suit healthcare-specific needs.
I aim to ensure the secure, reliable, and accurate use of AI in healthcare, mitigating misinformation risks and improving patient safety.
arXiv Detail & Related papers (2024-09-25T06:30:06Z) - SoK: Security and Privacy Risks of Healthcare AI [15.655956766190256]
The integration of artificial intelligence (AI) and machine learning (ML) into healthcare systems holds great promise for enhancing patient care.<n>However, it also exposes sensitive data and system integrity to potential cyberattacks.<n>Current security and privacy (S&P) research on healthcare AI is highly unbalanced in terms of healthcare deployment scenarios and threat models.
arXiv Detail & Related papers (2024-09-11T16:59:58Z) - Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs)
It examines the challenges introduced by AI components and the impact on testing procedures.
The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
arXiv Detail & Related papers (2024-02-21T08:29:42Z) - AI Maintenance: A Robustness Perspective [91.28724422822003]
We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance.
We propose an AI model inspection framework to detect and mitigate robustness risks.
Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
arXiv Detail & Related papers (2023-01-08T15:02:38Z) - Trustworthy AI Inference Systems: An Industry Research View [58.000323504158054]
We provide an industry research view for approaching the design, deployment, and operation of trustworthy AI inference systems.
We highlight opportunities and challenges in AI systems using trusted execution environments.
We outline areas of further development that require the global collective attention of industry, academia, and government researchers.
arXiv Detail & Related papers (2020-08-10T23:05:55Z) - Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable
Claims [59.64274607533249]
AI developers need to make verifiable claims to which they can be held accountable.
This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems.
We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
arXiv Detail & Related papers (2020-04-15T17:15:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.