Safer Internet Day 2026: How to govern “invisible AI” already in care

Internet safety in health and social care now means governing AI embedded in everyday systems. These three questions help you map it, own it, and evidence safe, well-led use at scale

Safer Internet Day 2026 highlights the safe and responsible use of AI. In health and social care, that theme lands differently. Internet safety is no longer mainly about blocking risky websites or managing social media use. The bigger risk is quieter and more consequential: AI is already embedded in everyday care delivery, often unnoticed, ungoverned, and therefore unauditable.

If you cannot clearly describe where AI is influencing decisions in your service, you cannot credibly say it is safe, effective, or well-led.

In this blog, Dr Richard Dune addresses health and social care leaders who are responsible for digital maturity, systems integration, governance, and compliance. He focuses on practical assurance: visibility, accountability, and evidence, not hype.

Internet safety has changed in regulated care

Care is delivered through systems: records, care management platforms, workforce tools, monitoring devices, triage processes, documentation workflows, and third-party services. If AI shapes any part of that system, AI governance is not just “IT“. It is:

• Patient safety and quality
• Risk and information governance
• Workforce competence
• Leadership accountability. 

In other words, it belongs in your operating model.

The “invisible AI” layer in everyday systems

When many people hear “AI“, they picture a chatbot. In reality, algorithmic decision-making is already present in tools most services rely on daily, including:

• Digital records and care management systems (how information is captured and surfaced)
• EPR/EHR features such as risk flags, predictive prompts, or decision support
• Rostering and workforce tools optimising patterns and allocations
• Monitoring technologies triggering alerts and prioritisation
• AI-assisted documentation (summaries, drafting, coding, triage support), plus informal generative AI use by staff. 

The governance problem is simple:

That creates avoidable risk, not because teams are careless, but because the technology becomes “normal” before it becomes governable.

Why this matters: These are governance risks, not tech risks

The most significant AI risks in care are operational and regulatory.

• Unsafe or inequitable decisions – If models are trained on non-representative data or used outside their intended context, outputs can be biased or unreliable, quietly widening inequalities and undermining safe care
• The accountability vacuum – When AI influences a decision, who is accountable? If the answer is “the system” or “the vendor“, governance has already failed. Accountability remains human and organisational
• Weak assurance under inspection – If AI is embedded in core systems, leaders must be able to evidence how its use is governed, monitored, and improved. If you cannot produce evidence quickly, governance is not mature, even if care is good
• Data protection and automated influence – Even when decisions are not fully automated, AI can materially shape judgment. If leaders cannot explain safeguards, escalation routes, oversight, and training, risk increases.

Bottom line – If AI is influencing care, AI governance is a patient safety and leadership issue.

The three AI governance questions every care leader should ask

These questions are intentionally practical. They require accountability, not technical expertise.

1. Where is AI currently used in our service?

Start with discovery, not policy writing. Create a simple AI use map covering:

• Every digital system used in care delivery (including “small” tools)
• AI features enabled within vendor products
• Third-party platforms staff use day-to-day
• Informal or unsanctioned generative AI use.

You cannot govern what you cannot see.

Tip – Don’t just ask IT. Ask frontline staff what they actually use during a shift. Informal AI use is often a sign of workflow friction: slow processes, fragmented systems, or unclear guidance.

2. Who is accountable for its outputs?

Every AI-influenced tool needs a named human owner. Ownership means someone can answer, in plain English:

• What is it for, and what must it never be used for?
• What happens when it is wrong?
• How do we detect drift or failure?
  
• Who signs off on updates, configuration changes, or new features?

If AI is influencing safety-critical decisions, accountability must be explicit before something goes wrong.

3. How would we evidence to regulators that it is safe, effective, and well-led?

A credible evidence pack typically includes:

• Purpose, scope, and where it sits in the pathway
• Proportionate risk assessment and equality impact thinking
• Oversight arrangements (care/clinical governance)
• Staff training and competence expectations
• Monitoring, audit, incident reporting and learning
• Information governance and data protection controls
• Version/change control when systems are updated. 

If you cannot produce this evidence quickly, AI governance is not yet mature.

Turning the three questions into action this month

You do not need a large AI programme. You need a lightweight operating rhythm and a few disciplined artefacts.

Establish a lightweight AI governance working group

Keep it cross-functional and pragmatic: service lead, clinical/practice lead, information governance, digital/supplier lead, safeguarding/quality.

Focus on discovery, decisions, escalation routes, and assurance, not paperwork for its own sake.

Create an AI use registry (your single source of truth)

A simple register makes governance real. Capture: tool/system, AI feature, pathway location, named owner, key risks/mitigations, training needs, monitoring approach, and change control expectations.

Integrate AI governance into what you already do

Avoid a standalone “AI compliance” silo. Fold AI into existing governance: risk management, incidents and learning, competence and training, information governance, supplier and contract management.

Train staff to recognise AI, not fear it

Staff do not need to become data scientists. They do need to recognise where AI influences work, understand limitations, and know when to escalate. The goal is critical awareness, not blind trust or fear.

Common pitfalls to avoid (what weak AI governance looks like)

Most problems are not caused by “bad AI“. They are caused by invisible AI and unclear ownership. Watch for these recurring pitfalls:

• Assuming the vendor governs it for you – Suppliers provide tools; you remain accountable for safe use in your context
• Treating AI as a policy exercise – A policy without an AI use map, owners, monitoring, and escalation is not governance
• Focusing only on clinical tools – AI in rostering, documentation and risk flags can still influence outcomes and decisions
• Allowing informal use to grow in the shadows – If staff are using generative AI to save time, fix the workflow and give safe guidance
• No change control story – When products update, AI features can change. If you cannot evidence review and sign off, assurance will fail.

What “inspection-ready evidence” looks like in practice

For most organisations, the goal is not a 40-page dossier. It is a clear, consistent evidence trail that can be produced quickly and explained in plain language. At minimum, aim for:

• An AI use registry (single source of truth)
• A one-page AI governance statement (purpose, principles, accountability, escalation)
• Evidence of competence (training/briefings, role expectations, do’s and don’ts)
• Monitoring and audit (what you check, how often, what triggers review)
• Incident learning (how AI-related concerns are reported, investigated, and improved)
• Supplier governance (named contacts, data and safety expectations, update notifications, change review). 

If you can show those elements, you are no longer relying on “trust us“. You are demonstrating a managed, well-led approach that fits existing risk and quality frameworks.

Digital maturity: Scaling safely in a fragmented system

The biggest risk is not that AI exists. It is that AI sits inside fragmented systems and fragmented governance. If evidence for policies, training, incidents, audits and assurance lives in separate places, you will struggle to demonstrate a coherent golden thread under scrutiny, especially when AI influences documentation, prioritisation, or decision support.

Scaling safely means standardising workflows, connecting evidence, designing audit trails, and applying disciplined change control so governance survives updates.

A useful maturity test:

If the answer is “collapse“, you do not have a technology problem. You have an operating model problem.

The test: Could you answer tomorrow?

If an inspector, commissioner, or family asked tomorrow: “Where is AI influencing care in your service, and how do you know it is safe?“

Could you answer with confidence? If not, the solution is not a large AI transformation programme. It starts with three questions, clear accountability, and governance that treats AI as part of how care is delivered today, not tomorrow.

Because internet safety in care is no longer about blocking access. It is about governing what is already here and what you adopt next.

Safer Internet Day 2026: How to govern “invisible AI” already in care – LearnPac Systems UK –