Industry Perspectives

South Korea’s Financial Security Institute unveils an AI reliability and safety evaluation framework for finance.

Law firm investigates Baylor Genetics data breach exposing patient and employee personal information.

Law firm investigates Lone Star Community Health Center breach exposing 250,130 patients' personal and medical data.

How DLP supports HIPAA: monitor, log, and control ePHI (email, endpoints, cloud) while pairing tools with risk analysis and governance.

Five IoT incident-response metrics—MTTD, MTTA/MTTR-start, containment, recovery, improvement—mapped to NIST CSF for safer device operations.

AI should triage and continuously update healthcare vendor risk, but final high‑impact decisions must remain human‑controlled.

EDR stops attacks fast in healthcare by isolating devices, blocking harmful behavior, and matching automation to patient-safety needs.

Practical guardrails for safe healthcare AI: validation, monitoring, bias testing, vendor controls, and HIPAA compliance.

Protect research data and IP when working with AI drug discovery vendors. Learn top threats, governance steps, technical defenses, and continuous monitoring.

ISO 27001 and GDPR together secure patient data, reduce compliance gaps, and align incident response with GDPR’s 72-hour breach rule.

NCQA, AAAHC, and TJC vendor credentialing, security, and 2025 updates — why continuous monitoring and automation protect PHI and accreditation.

Practical guidance for healthcare vendors to design SOC 2–aligned PHI training: role-based lessons, regular refreshers, documentation, and audit-ready automation.

Centralize vendor inventories, prioritize critical suppliers, tighten contracts, and test contingency and incident response plans to reduce supply chain failures.

How the NIST Cybersecurity Framework boosts healthcare security—faster detection, fewer breaches, lower cyber insurance costs, and stronger vendor risk oversight.

Monitor AI in healthcare: set interpretability goals, apply XAI (SHAP, LIME, Grad-CAM), stream EHR data to real-time dashboards, and audit for bias and compliance.

Steps healthcare organizations must take to vet AI/ML vendors for FDA clearance, HIPAA security, PCCPs, and ongoing performance monitoring.

Assess and prioritize critical vendors, align continuity plans, and use automated monitoring to reduce third‑party risks and prevent service outages.

Clear differences between SOC 2 gap analysis and full audits for healthcare — readiness steps, timelines, costs, and which to use for compliance.

Practical steps to assess cloud vendor security, enforce HIPAA/HITRUST, and ensure business continuity to protect patient data and care delivery.

Practical 2025 guide to assessing and monitoring AI vendors in healthcare: security, bias mitigation, contract terms, and continuous compliance.

Framework to manage FDA medical device vendor risk: use SBOMs, enforce secure development, monitor vulnerabilities, and document CAPA for compliance.

Evaluate healthcare AI vendors for fairness, transparency, bias mitigation, and patient data rights using a practical ethics and compliance checklist.

Learn core skills, certifications, and training roadmaps to assess third‑party risk, ensure HIPAA compliance, and manage vendor cybersecurity in healthcare.

Prioritize testing and controls for functions that can harm patients, expose ePHI, or disrupt care; validate and maintain risk evidence.