Industry Perspectives

Slow, siloed healthcare GRC left leaders blind during COVID; adopt continuous monitoring, tiered vendor review, and owned escalation.

CMS quality now depends on cyber readiness: EHR uptime, vendor resilience, and tested recovery protect patient safety and payments.

Don't share patient data until vendors commit in writing to scope, retention, no-training, security, subprocessors, and breach liability.

Tier vendors by patient/data impact, monitor live risk signals year-round, and enforce fast responses to protect PHI and care delivery.

Shared execution fails unless each medical-device risk decision has one named executive owner and a tracked device risk register.

How SMART converts vendor and product traits into a 300–850 systemic risk score to prioritize healthcare dependencies and chokepoints.

Pharmacy operations hide single points of failure in shared vendors that can stop prescriptions, claims, and payments—map and test your fallbacks.

How healthcare boards must treat ransomware as enterprise risk: demand tested recovery times, map vendor exposure, and measure care impact.

Explains SEC, HIPAA/HHS, state, and vendor reporting differences and how to run a unified disclosure workflow.

Standardizing risk language, not processes, lets healthcare reuse vendor evidence and keep local teams accountable.

Reclassify vendors by outage impact, care disruption, and fourth-party risk—not contract value or PHI exposure.

SBOMs list code but miss AI risks. Healthcare needs AIBOMs — living records of models, training data, runtime, bias and drift controls.

Treat vendor approval as four separate gates—assessment, risk acceptance, contract/BAA, and go‑live—to prevent PHI exposure and lost findings.

Make healthcare tabletop exercises expose real care and vendor gaps with evolving injects, decision-makers, timestamped logs, and retests.

Tier vendors at intake, use rapid reviews for low risk and deep dives for high-risk vendors to protect PHI and patient safety.

Unchecked AI in the revenue cycle risks denials, PHI exposure, audit problems, and lost revenue.

Map proposed HIPAA requirements to controls, build audit-ready evidence, prioritize patient-safety gaps, and set defensible comment positions.

Embed security across the medical AI lifecycle to prevent breaches and patient harm with risk assessments, encryption, access controls and ongoing monitoring.

Seven hidden AI risks in healthcare—from prompt injection and shadow AI to vendor exposure and model poisoning—and clear governance steps to protect patients and compliance.

Transparent, rapid, legally grounded communication is critical to protect patients and maintain operations during healthcare supply chain crises.

Overview of FDA's 2025 cybersecurity labeling for medical devices: SBOMs, connectivity disclosures, secure config, patching, AI-specific obligations.

Practical overview of de-identification, differential privacy, federated learning, and governance for secure, multi-institutional healthcare research.

Overview of clinical AI use, risks, and governance: managing bias, diagnostic errors, data privacy, cybersecurity, and compliance to protect patients.

Protect patient safety by managing vendor risks to oncology equipment, drugs, and IoMT through continuous monitoring, compliance, and incident planning.