How Irish public sector bodies can use AiEGIS to comply with the EU AI Act. Real scenarios. Real layers. Real compliance.
The triage AI makes time-critical decisions that directly affect patient safety. If it under-prioritises a heart attack patient or over-prioritises a minor injury, the consequences are severe. The HSE needs to ensure the AI is continuously monitored, its decisions are auditable, and human clinicians can override it at any time.
AiEGIS monitors the triage AI in real-time. Every triage decision is logged with confidence scoring — when the AI's confidence drops below a configurable threshold (e.g., 70%), the case is automatically routed to a human clinician for review. Behavioural baselines detect if the AI starts making unusual patterns of decisions (drift detection). If the AI degrades, AiEGIS quarantines it and alerts the medical team.
AI credit scoring models can embed bias — discriminating by postcode, age, or nationality without explicit programming. The Central Bank needs visibility into how these models make decisions, whether they drift over time, and whether customers are being treated fairly. Every decision must be auditable for regulatory inspection.
AiEGIS registers each credit scoring AI with a unique identity and monitors every decision. The compliance engine enforces spending limits and approval workflows — decisions above a configurable threshold require human review. Data protection scanning catches any PII leaking through model outputs. The full audit trail satisfies Central Bank inspection requirements.
AI chatbots handling citizen queries can inadvertently collect, store, or expose personal data. An AI assistant trained on public data might memorise PII and leak it in responses. The DPC needs to verify that AI systems across regulated entities handle personal data correctly — without manually inspecting every system.
AiEGIS scans every input and output of monitored AI systems for PII — emails, phone numbers, PPS numbers, credit card numbers, IP addresses. Data is classified as PUBLIC, INTERNAL, CONFIDENTIAL, or RESTRICTED. Egress monitoring blocks RESTRICTED data from leaving the system. The DPC gets automated compliance reports per AI system showing what data exists, where, and who accessed it.
An AI tutoring platform for primary school children adapts content based on student performance. If the AI develops bias — recommending less challenging material to students from certain backgrounds — it could reinforce educational inequality. The AI must be monitored for fairness, its decisions must be transparent, and parents must be able to understand how their child is being assessed.
AiEGIS registers the tutoring AI and monitors its interactions with students. Behavioural Intelligence detects if the AI treats different student groups differently (fairness monitoring). Confidence scoring ensures low-confidence assessments are reviewed by teachers. Data protection prevents student PII from being exposed or misused. The compliance engine generates transparency reports that parents and educators can understand.
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