Industry Perspectives

Plan and manage cybersecurity, data privacy, and transparency risks from superhuman AI in healthcare using frameworks, continuous monitoring, and human oversight.

Regular, structured risk reviews help healthcare organizations protect PHI, maintain ISO 27001 and HIPAA compliance, and reduce data breach risk.

How healthcare orgs can comply with the 2026 HIPAA Security Rule: mandatory MFA, encryption, annual pen tests, 72-hr restores, and continuous audit readiness.

Identify vendor risks in ambulatory surgery centers—cyber threats, equipment failures, and noncompliance—and practical steps for mitigation.

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

AI speeds healthcare incident response - reducing detection time and manual work while automating containment and recovery, but it needs robust governance.

AI automates SOC 2 and HIPAA evidence collection, slashing audit prep time and costs while enabling continuous monitoring and real-time compliance for healthcare.

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

Fortune 500 healthcare companies face escalating AI‑driven risks—from adversarial attacks to massive data breaches. This guide breaks down the enterprise‑level AI threat landscape, governance models, NIST‑aligned controls, and how platforms like Censinet RiskOps™ and Censinet AI™ help manage AI at scale.

AI is transforming diagnostics and operations in healthcare—but legacy risk frameworks built for static software can’t manage threats like data poisoning, model drift, and black‑box algorithms. This guide explains why traditional risk management falls short and how modern AI‑ready strategies and platforms like Censinet RiskOps™ fill the gaps.

Examines AI-specific cyber, liability and compliance gaps in healthcare and how tailored insurance, audits, human oversight and automation can reduce exposure.

84% of healthcare leaders say cyber risk outpaces budgets; explore low-cost steps: MFA, phishing training, patching, and vendor oversight to reduce exposure.

Inventory devices, map PHI flows, score clinical impact, and align IoT risk with FDA, HIPAA, and AAMI requirements.

Learn 7 AI evaluation methods for cybersecurity detection and triage, including rubrics, benchmarks, golden sets, human review, and LLM judge workflows.

How FDA Section 524B forces SBOMs, postmarket plans, secure design, and access controls for medical IoT — a patient-safety approach.

Role-based PHI access, least-privilege rules, break-glass limits, MFA, session timeouts, HR-tied account changes and audited log reviews.

Run safe, workflow-focused DAST: sanitized staging, logged-in FHIR tests, CI/CD gates, and prioritized triage.

Hospital AI documentation checklist: inventory, standard logs, data & model lineage, human overrides, tamper-proof storage, and framework mapping.

Checklist to meet FDA premarket cybersecurity: confirm scope, map data flows, prepare SBOM, document controls, and validate via testing.

Analysis of 10 failure points in medical device supply chains and immediate actions to reduce shortages, cyber and quality risks.

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.