Emerging Technology and Security Awareness in Healthcare
Emerging technology questions test risk judgement for AI, cloud, telemedicine, and connected devices, plus whether the workforce can recognise threats. The exam favours controls over hype.
Core risks
Generative AI can leak PHI through prompts, retain data, and produce unsafe clinical suggestions. Use approved enterprise tools with BAAs, DLP, prompt logging, and human review. AI and ML models need governance for bias, drift, model poisoning, and explainability. Telehealth expands the attack surface through home networks and consumer devices. IoMT and cloud increase vendor dependency. Quantum computing threatens RSA and ECC; plan post-quantum cryptography migration for long-lived health records. Blockchain is not a privacy control by itself; immutable ledgers can conflict with erasure rights.
Awareness
Training must be role-based: clinicians need phishing, MFA, and PHI handling; developers need secure coding; executives need risk ownership. Run phishing simulations and measure reporting, not just completion. Create a speak-up culture and easy reporting. Shadow AI is a major risk: staff pasting notes into public chatbots.
Worked example
A clinician wants to summarise a patient note with a public LLM. The privacy officer flags PHI disclosure, no BAA, and unclear retention. The organisation deploys an enterprise LLM with a BAA, DLP blocking of identifiers, audit logs, and a policy that outputs require clinician review. Training explains why the public tool is prohibited. Reasoning: the control set addresses confidentiality, vendor accountability, and safe use without banning the workflow outright.
Traps
Treating awareness training as a completion checkbox. Assuming the cloud provider secures everything. Ignoring shadow AI. Assuming blockchain guarantees privacy. Delaying post-quantum planning for long-retention records.