AI in Healthcare Needs Cyber Resilience, Not Checklists

A hospital can lose power and still keep patients alive. But when a hospital loses access to systems—EHR, radiology, lab, bed management—the whole place slows to a crawl in minutes. That’s why the public-sector shift described in Ireland’s cyber conversation matters for healthcare leaders: cybersecurity is no longer a compliance function. It’s operational resilience.

I’ve noticed a pattern across health and public-sector tech programmes: teams get excited about AI triage, AI documentation, imaging support, and automation… then realise the hard part isn’t model selection. It’s the security and governance needed to run AI safely at scale. If your cyber posture can’t handle ransomware, supply-chain compromise, and AI-enabled phishing, your AI roadmap is going to stall—or worse, create new clinical risk.

Public-sector security leaders are already saying the quiet part out loud: prevention-only thinking doesn’t work. The better approach is to assume disruption will happen and design for continuity. For healthcare AI, that’s the difference between “we deployed a pilot” and “we embedded AI into clinical operations without raising patient safety risk.”

Why public-sector cyber resilience is now a healthcare AI prerequisite

Answer first: AI adoption in healthcare increases the value and exposure of data and workflows, so cyber resilience becomes the foundation for safe AI-enabled care.

Healthcare sits at the intersection of critical services and sensitive data. That makes it attractive to attackers and unforgiving when systems go down. When you introduce AI, you typically add:

• More data movement (training, fine-tuning, monitoring)
• More integrations (EHR connectors, PACS/RIS, call-centre systems)
• More vendors (AI model providers, annotation services, cloud tooling)
• More automation (agents, auto-summarisation, auto-coding, auto-routing)

Each one expands the attack surface.

The public sector’s evolving stance—treating cyber as a board-level continuity issue—maps perfectly to hospital reality. A ransomware incident isn’t just “an IT problem.” It’s cancelled procedures, delayed diagnoses, diverted ambulances, staff reverting to paper, and stressed clinical decision-making.

Here’s the stance I think healthcare should adopt: If an AI tool is important enough to be used in patient-facing workflows, it’s important enough to have a tested resilience plan.

Myth to drop: “Security slows down AI innovation”

Security can slow down chaos. It should speed up safe delivery.

When governance is clear—data classification, vendor requirements, access controls, incident playbooks—AI teams ship faster because they’re not renegotiating risk decisions every sprint. That’s the core public-sector lesson: make cyber a repeatable capability, not a last-minute gate.

The threat picture: ransomware, supply chains, and AI-powered social engineering

Answer first: The most likely way healthcare AI programmes get disrupted is not model failure—it’s ransomware, third-party compromise, or staff being tricked by AI-crafted phishing.

Public-sector leaders are calling out three pressures that hit healthcare especially hard.

Ransomware: the “service disruption” weapon

Ransomware persists because it targets what healthcare can’t tolerate: downtime. Even if backups exist, recovery takes time, and time is the scarce resource in hospitals.

If you’re deploying AI, assume attackers will aim for:

• Identity systems (to lock out staff)
• File shares and imaging archives (to freeze diagnostics)
• EHR integration points (to break workflows)
• Backup systems (to remove your exit option)

A practical rule: your recovery strategy should be tested against clinical timeframes, not IT timeframes. “We can restore in 72 hours” may be acceptable in some industries; in acute care, it can be catastrophic.

Supply-chain exposure: your vendor is part of your perimeter

Healthcare AI is vendor-heavy. Even when the model runs locally, it may rely on:

• Model updates and signature feeds
• Telemetry and monitoring platforms n- Support access channels
• Data pipelines and connectors

Public-sector security discussions highlight the uncomfortable truth: you can’t fully outsource risk. If your third party gets hit, you feel it.

What works in practice is a tiered approach:

• Tier 1 (clinical-critical): strongest controls, strict access, tight monitoring, tested failover
• Tier 2 (operational): strong controls, scheduled resilience testing
• Tier 3 (non-critical): baseline controls, rapid isolation capability

Most organisations don’t formalise this, then discover during an incident that they treated “AI documentation assistant” like a toy—until clinicians relied on it.

AI-assisted phishing and targeting: the human layer is still the weak point

Attackers are using AI to scale persuasion: more believable emails, more tailored lures, better timing. Public-sector leaders are right to emphasise training because human error remains a top driver of breaches.

If you’re rolling out generative AI internally, your staff will also start trusting AI outputs more. That changes the risk profile:

• Clinicians may accept an AI-generated message as “approved language”
• Admin teams may follow polished instructions without verifying
• Support desks may get socially engineered with convincing context

Security awareness training should include AI-native scenarios, not just generic “don’t click links.”

Resilience over prevention: what “good” looks like for hospitals deploying AI

Answer first: A resilient healthcare AI environment assumes incidents will happen, then limits blast radius and restores priority services quickly.

Prevention matters. But resilience is the multiplier. Here’s a concrete view of what “good” looks like when AI is moving from pilot to production.

Build for continuity: segmented systems and clinical-safe fallbacks

Hospitals need the ability to isolate parts of the network without losing everything.

• Network segmentation: Keep AI workloads, data engineering platforms, and core clinical systems separated so compromise doesn’t cascade.
• Least privilege access: AI tools shouldn’t have broad read/write rights “because it’s easier.”
• Downtime procedures: Paper workflows and read-only EHR modes still matter. Write them down, train them, practise them.

A useful test: If the AI tool disappears for 48 hours, what breaks—and how do you keep patient safety intact?

Treat incident response as an operational drill, not a document

Public-sector guidance stresses testing response plans so people know their roles under pressure. Healthcare needs this even more.

Run tabletop exercises that include:

• Clinical leadership (who decides service prioritisation?)
• IT and security (who isolates what, and when?)
• Communications (how do you inform staff without panic?)
• Vendor escalation (what if the AI provider is the incident?)

The goal isn’t perfect performance. It’s reducing decision latency when every minute counts.

Governance for AI use: fast decisions, fewer surprises

AI introduces governance questions that don’t show up in classic app deployments:

• What data can be used for training, fine-tuning, or evaluation?
• Where is PHI processed, and who can access logs?
• How do you monitor for prompt injection or data leakage?
• What’s the policy for staff entering sensitive information into assistants?

A strong governance setup isn’t a giant committee. It’s clear decision rights and pre-approved patterns.

Snippet-worthy rule: If you can’t explain where your AI tool gets data, where it sends data, and how you recover it after an incident, it’s not ready for clinical workflow.

The ransomware payment debate: bans don’t replace preparedness

Answer first: A ransomware payment ban may reduce incentives for criminals, but it can also corner healthcare organisations unless resilience is already strong.

The public conversation about ransomware payment bans is understandable: stop funding attackers. Ethically, it’s hard to argue with.

Operationally, healthcare is messy. A blanket ban can create an impossible decision when critical services are down and recovery is uncertain. The more workable approach (and the one I’d back for healthcare) is:

• Mandatory rapid reporting and central support structures
• Clear decision frameworks for crisis leadership
• Pre-positioned recovery capability so payment isn’t the “fastest path”

The point is not to normalise paying. It’s to ensure hospitals never feel forced into it.

If you want one metric that predicts whether a hospital will be pressured to pay, it’s this: time-to-restore priority clinical systems from clean backups.

A practical 90-day plan for healthcare AI security readiness

Answer first: In 90 days, you can materially reduce AI-related cyber risk by tightening identity, hardening vendors, and rehearsing response.

If you’re a CIO, CISO, digital transformation lead, or clinical systems owner, here’s a plan that’s realistic without being timid.

Days 1–30: Know what you have, and who can touch it

1. Map your AI data flows (inputs, outputs, storage, logs)
2. Create an AI asset inventory (models, endpoints, connectors, vendor tools)
3. Enforce MFA everywhere—especially admin and vendor access
4. Remove standing privileges (use just-in-time access for elevated roles)

Days 31–60: Control third parties and reduce blast radius

1. Classify vendors by clinical criticality (Tier 1–3)
2. Require minimum vendor controls (logging, breach notification, access governance)
3. Segment AI environments from core clinical networks
4. Set retention rules for prompts, transcripts, and AI logs

Days 61–90: Practise failure and prove recovery

1. Run a ransomware tabletop focused on AI + EHR integration points
2. Test restores of the systems your AI depends on (not just the AI stack)
3. Train staff on AI-assisted phishing scenarios
4. Define “safe mode” operations: what continues, what pauses, who decides

If this feels like a lot, that’s because it is. Healthcare AI is safety-critical software. Treat it that way.

Where this fits in the “AI in Technology and Software Development” series

This series often talks about automation, cloud optimisation, code generation, and scalable deployment patterns. Here’s the healthcare twist: the same engineering maturity that makes AI products reliable—observability, least privilege, change control, incident response—also makes them safer for patients.

The public sector is learning to stop treating cybersecurity as paperwork. Hospitals should copy that mindset aggressively.

If you’re planning to expand AI from a few pilots to hospital-wide usage in 2026, the real question isn’t “Which model?” It’s: Can your cybersecurity posture keep clinical AI available, trustworthy, and recoverable under attack?

If you want help pressure-testing your AI security readiness—vendor risk, architecture, incident drills, and governance—build a short list of your top three AI use cases and assess them against resilience requirements before procurement locks you in. What would you change if you assumed disruption was inevitable?