Jul 2026· International Journal of Innovations in Science, Engineering And Management· pp. 104-112· 0 citations· 13 references
TL;DR
A comprehensive governance blueprint for the safe deployment of agentic Salesforce Healthcare CRM systems through a Human-in-the-Loop (HITL) framework is proposed, which incorporates governance-by-design principles, zero-trust security, consent-aware data management, and continuous feedback loops to strengthen operational resilience.
Abstract
Agentic artificial intelligence is transforming Salesforce Healthcare Customer Relationship Management (CRM) platforms by enabling intelligent decision-making, workflow orchestration, and personalized care operations. However, the increasing autonomy of AI systems in regulated healthcare environments introduces significant challenges related to explainability, accountability, privacy, auditability, and compliance. This paper proposes a comprehensive governance blueprint for the safe deployment of agentic Salesforce Healthcare CRM systems through a Human-in-the-Loop (HITL) framework. The proposed architecture integrates four core layers—data integrity, intelligence and orchestration, governance and observability, and human oversight—to ensure responsible AI adoption across healthcare operations. A risk-based operating model categorizes AI-driven actions into low-, medium-, and high-risk tiers, aligning each with appropriate monitoring, approval, and escalation mechanisms. The framework further incorporates governance-by-design principles, zero-trust security, consent-aware data management, and continuous feedback loops to strengthen operational resilience. The proposed blueprint provides healthcare organizations with a practical roadmap for achieving trustworthy, explainable, compliant, and scalable agentic CRM implementations while maintaining human accountability and improving governance maturity.
The findings show that meaningful oversight is not a single human approval step and is a lifecycle capability that combines bounded autonomy, evidence-based escalation, stop authority, continuous validation, audit records, and institutional learning.
Aaron K Montgomery, Hannah E Gallagher, Derrick L Mercer· International Journal of Eng...· 0 citations
An Autonomous Decision Assurance Layer (ADAL) is proposed for AI-driven enterprise analytics environments that bridges data governance, multi-agent AI, human-in-the-loop oversight, responsible AI controls, and executive decision intelligence.
Choudhry Bilal Mazhar· International Journal for Re...· 0 citations
: Healthcare ecosystems face persistent identity-fragmentation challenges that undermine secure, interoperable data access across Electronic Health Records (EHR), telemedicine platforms and clinical Application Programming Interfaces (APIs). Existing Identity and Access Management (IAM) solutions rely on static role assignments and reactive audit mechanisms ill-suited to dynamic clinical environments. This paper presents the Zero-Trust Patient Identity Engineering Framework for Healthcare (ZTPIF-H), which integrates decentralised identity, Artificial Intelligence (AI)-driven adaptive authorisation, smart-contract-based consent governance and blockchain-based immutable auditability. The framework is organised around four pillars — Identity Assurance, Adaptive Trust Decisioning, Consent Governance and Immutable Accountability — operationalised through an eight-phase lifecycle. Relative to prior work, this version adds a multi-dimensional comparison with state-of-the-art healthcare IAM approaches, a reproducible experimental protocol, an explicit threat model with a systematic security analysis, and an AI-trustworthiness analysis covering interpretability, calibration, robustness and fail-safe behaviour aligned with the NIST AI Risk Management Framework (AI RMF) and the EU AI Act. A proof-of-concept (PoC) evaluation in a simulated secondary-care environment shows a 42% reduction in authorisation latency, a 62% reduction in false-positive denials and a 77% reduction in standing-privilege exposure versus a conventional IAM baseline. ZTPIF-H aligns with HL7 FHIR R4, OpenID Connect (OIDC), W3C Verifiable Credentials (VC) and NIST Zero Trust Architecture (ZTA) principles, offering a vendor-neutral, incrementally adoptable pathway to modern healthcare identity engineering
The U.S. healthcare delivery infrastructure functions as a complex socio-technical system-of-systems (SoS). In this environment, autonomous, interdependent entities (providers, insurers, and IT networks) must balance local efficiency with system-wide coordination. Existing governance mechanisms frequently fail to prevent operational fragmentation, yet the conditions under which governance pressure generates or inhibits coordination remain poorly understood. This study introduces a three-parameter cost framework. The framework comprises default operational costs, incoherence penalties for structural misalignment, and switching costs associated with modernization. This framework enables the examination of how governance structures shape emergent alignment in healthcare SoS. The three cost components are grounded in the Confluence Interoperability Covenant (CIC), a governance framework that codifies standards for interaction among autonomous healthcare systems and extends it into a computationally tractable form for simulation-based analysis. The contribution lies in jointly operationalizing these three cost components interacting with each other within a healthcare SoS governance framework using agent-based modeling and simulation (ABMS) and examining how their interaction shapes coordination under decentralized, periodic adaptation. Using a stylized exploratory ABMS framework implemented on a network, we analyze how these parameters influence coordination dynamics across a range of simulated governance conditions. The results reveal three emergent patterns. First, higher incoherence costs accelerate the transition from fragmented legacy configurations toward greater alignment. Second, high switching costs can create lock-in dynamics in which inefficient arrangements persist because transition burdens suppress adaptation. Third, the relationship between governance pressure and speed of coordination is non-monotonic: at early review horizons, moderate incoherence costs can produce faster coordination than maximal pressure, whereas stronger pressure eventually produces lower entropy as the simulation progresses. This finding reflects a transient finite-horizon coordination advantage rather than a fixed optimal governance level. This last finding suggests that governance effectiveness is horizon-dependent rather than a simple linear function of regulatory intensity. These findings contribute to a complexity-informed understanding of healthcare governance by showing that alignment in adaptive SoS depends not only on the intensity of interoperability pressure but on its interaction with institutional transition burdens. The model is designed as a mechanism-exploration framework to support future empirically grounded policy analysis and data collection.
Arash Vesaghi, Mohamed Mogahed, Mo Mansouri· Systems· 0 citations
Abstract—Federal and regulated organizations continue to rely on document-centric Authorization to Operate (ATO) processes even as the NIST Risk Management Framework (RMF), continuous monitoring guidance, Zero Trust Architecture (ZTA), and continuous authorization initiatives require more continuous, evidence-driven risk management [1]-[3], [13], [15]. Manual System Security Plan (SSP) updates, spreadsheet-based Plan of Action and Milestones (POA&M) tracking, and disconnected assessment evidence create governance latency: the delay between operational security events and authorization-ready governance response. This paper presents OpenGRCRMF, a proposed open, vendor-neutral reference framework that models RMF lifecycle activities as workflow states, treats authorization artifacts as structured governance objects, and maps DevSecOps and Zero Trust telemetry into authorization-relevant evidence. Using Design Science Research, the study develops the OpenGRCRMF architecture, formalizes its data and risk model, and evaluates expected governance effects through a synthetic simulation of 1,500 findings across 180 assets and 320 controls [9]. OpenGRCRMF is evaluated as a reference framework rather than a production platform using a self-contained simulation specification and sensitivity analysis. In the simulation, the OpenGRCRMF-enabled workflow reduced modeled governance processing time by 36.8 percent, increased modeled evidence completeness by 41.5 percent, and increased modeled control-to-evidence traceability by 52.7 percent compared with a document-centric baseline. These results are modeled outcomes under stated assumptions, not production deployment proof. The paper contributes a governance object model, governance latency construct, reproducibility-oriented simulation design, threat-to-validity analysis, and education-oriented framework for teaching how operational telemetry becomes authorization evidence.
Anand Janjal· Journal of Cybersecurity Edu...· 0 citations
Artificial intelligence (AI) is increasingly embedded in healthcare delivery, and when systems are scaled across clinical workflows, errors may have system-wide consequences. The Artificial Intelligence Act (AIA) and the Medical Device Regulation (MDR) establish important safety requirements for AI-based medical devices placed on the EU market, but they do not, in themselves, guarantee that AI tools deliver quality care for patients in real-world clinical settings. This article examines how the EU Public Procurement Directive (PPD) can complement the AIA and MDR to strengthen quality assurance when AI tools are adopted in healthcare systems across EU Member States. The article proposes specific procurement mechanisms as a prerequisite for adoption, thereby translating regulatory safeguards into practice. The article also argues that healthcare providers play a critical role in shaping the quality of AI. Beyond CE-marking, procurement should require evidence of clinical relevance, context-specific documentation, transparency in system design, and clearly defined quality criteria, including diagnostic accuracy and patient outcomes. Embedding legal and ethical requirements in procurement provides a pathway from law to care, ensuring that AI supports safe, equitable, and high-quality healthcare.
Jennifer Viberg Johansson, S. Slokenberga· Health Care Analysis· 0 citations
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