Industry Perspectives

Healthcare organizations face rising third-party risks; learn five essential KPIs to monitor and mitigate vendor-related cybersecurity threats.

Explore the top cybersecurity challenges in healthcare supply chains and learn effective strategies for securing patient data and operations.

Why adopting NIST in healthcare is hard: high costs, legacy devices, regulatory overlap, skill gaps, and complex risk assessments — and how to address them.

Explains five main barriers U.S. healthcare organizations face adopting ISO 27001: leadership, budgets, regulations, documentation, and vendor risks.

Layered security: tokenize identifiers and encrypt unstructured records to limit PHI breaches and simplify compliance.

Cybersecurity in healthcare is critical for patient safety and operational integrity. Explore the evolving threats and essential leadership strategies.

Explore key assessment metrics for managing third-party risks in clinical applications, ensuring compliance and protecting patient data.

Explore the critical differences between third-party risk management and vendor management in healthcare to safeguard patient data and enhance operations.

Understand the differences between Third-Party Risk Assessments and Vendor Security Assessments to effectively manage cybersecurity in healthcare.

Explore the complexities of third-party IAM in healthcare, highlighting challenges and effective strategies for securing patient data and ensuring compliance.

Practical, step-by-step guidance to prepare healthcare organizations for third-party audits across HIPAA, SOC 2, and ISO 27001—control mapping, vendor tiering, and remediation.

Vendor risks are draining IT resources in healthcare by 20%, hindering patient care and innovation due to compliance and cybersecurity challenges.

Step-by-step checklist to assess healthcare vendor risks: inventory, compliance, cybersecurity, PHI handling, scoring, and continuous monitoring for 2025.

How healthcare organizations can build AI-ready systems: governance, data interoperability, secure compute, Zero Trust, MLOps, and phased modernization.

How the precautionary principle shapes healthcare AI: risk assessments, governance, pilots, and continuous monitoring to protect patients and PHI.

AI improves care but creates patient-safety, cybersecurity, and compliance risks; healthcare leaders must identify, score, and mitigate them.

AI is revolutionizing healthcare diagnostics and operations, but it also expands the cyberattack surface, increases algorithmic bias, and creates new data‑privacy risks. This guide explains the hidden dangers of AI in healthcare and outlines strategies—including governance, transparency, and secure AI risk management—to keep systems and PHI safe.

Explores critical cybersecurity risks in medical AI—data pipeline exposure, model poisoning, and device vulnerabilities—and practical defenses like governance, monitoring, and secure design.

AI boosts diagnostics and cuts costs but brings cyber, bias, and vendor risks — this article explains governance and real-time tools to manage them.

Traditional risk controls fail for AI in healthcare—opaque models, model drift, and new attacks demand cross-functional governance, continuous monitoring, and AI-specific frameworks.

AI improves diagnostics and workflows but brings clinical, cybersecurity, and compliance risks; governance, clinician oversight, and vendor controls are crucial.

AI boosts threat detection and automates risk assessments in healthcare—human judgment, governance, and NIST-aligned oversight remain essential.

AI-powered security for connected medical devices enables real-time threat detection, automated responses, and prioritized risk management to protect patient safety.

AI and IoT improve care but increase cyber risk — healthcare must adopt Zero Trust, encryption, vendor governance, continuous monitoring, and fast incident response.