Governance

AI Governance & EU AI Act

The EU AI Act isn't coming. It's already here. Qonera supports seven core articles across literacy, oversight, accuracy, transparency, record-keeping, risk management, and incident reporting.

AI literacy obligations applied from February 2025. Broader transparency and oversight requirements land August 2, 2026. The question isn't whether your team will be affected. It's whether your current AI workflow is ready to be scrutinised.

How Qonera Supports AI Governance

The EU AI Act doesn't just regulate AI systems. It raises expectations for how organisations use AI in practice, around literacy, oversight, and transparency. Qonera is built around the workflow that makes those expectations real.

AI Literacy Art. 4

Article 4 of the EU AI Act requires organisations to ensure staff using AI understand its capabilities, limitations, and risks. This obligation has applied since February 2025.

Qonera operationalizes literacy by making teams engage with source quality, model disagreement, and evidence gaps rather than treating AI output as automatically reliable. Review discipline is literacy in practice.

Human Oversight Art. 14

Article 14 requires that deployers of high-risk AI systems ensure appropriate human oversight, with the ability to understand capabilities and limitations, detect failures, and intervene or halt the system when necessary.

Qonera builds that discipline into the workflow structurally. Every AI-assisted output goes through a named reviewer before delivery: source audit, multi-model stress test, conflict analysis, and sign-off. The reviewer can flag, override, or reject any output. Nothing leaves without a named person on record.

Accuracy and Robustness Art. 15

Article 15 requires high-risk AI systems to achieve appropriate levels of accuracy, robustness, and consistency across outputs, with mechanisms to detect and handle errors or inconsistencies.

Qonera addresses this through its Multi-Model Stress Test: three independent AI models run every query in parallel, without influence from each other. A judge model synthesizes the results. Where models agree, confidence is high. Where they diverge, the Conflict Heatmap surfaces the disagreement directly so the reviewer can investigate before the output goes further.

This makes inconsistency visible rather than hidden, which is the practical requirement Art. 15 points toward.

Transparency Art. 50

Qonera helps teams maintain clearer records of what was AI-assisted, what evidence was used, what conflicts were identified, and who was accountable for the final output.

As Article 50 transparency obligations become applicable from August 2, 2026, that kind of documentation becomes increasingly relevant for organisations using AI in professional workflows.

Record-Keeping Art. 12

Article 12 sets expectations around logging and traceability for AI systems. Qonera records every AI inference with the model used, token counts, cost, provider, region, and a hash of the system prompt.

These records form a tamper-evident audit trail with hash-chain integrity verification. Compliance administrators can export the full trail as CSV or PDF for regulator review, filtered by date range, organisation, or model.

Risk Management Art. 9

Article 9 sets out a risk management framework for AI systems, covering identification, analysis, and mitigation of risks. Qonera runs automated risk screening on every AI response using a two-tier architecture.

A heuristic first pass checks for PII disclosure, prescriptive medical or legal advice, fabricated citations, and security-sensitive content. When signals are detected, a secondary AI classifier produces a structured risk verdict with severity level and reasoning.

High-risk detections automatically create incident reports, notify administrators by email, and can trigger approval gating so a supervisor reviews the output before it reaches a client.

Incident Reporting Art. 73

Article 73 requires providers to report serious incidents to national authorities. Qonera supports this with both manual and automated incident reporting.

Any user can report a problematic AI output directly from the message interface, selecting a category and severity. The automated risk monitoring system supplements manual reports by detecting high-risk outputs in real time. Administrators triage incidents through a dedicated workflow, and all records are available for regulator disclosure.

FAQ

Does Qonera make us EU AI Act compliant?

No. Qonera supports stronger internal governance workflows: review, evidence checking, and approval discipline. It doesn't provide legal advice and doesn't guarantee compliance with any regulation.

Do we need Qonera to satisfy the AI Act?

The Act doesn't require any specific tool. It requires organisations to demonstrate responsible AI use, human oversight, and appropriate governance. Qonera is one practical way to build that into your workflow.

What changes on August 2, 2026?

It's the main application date for much of the EU AI Act framework. That's when broader transparency and oversight obligations become enforceable for most organisations. It's not a ban on AI. It's when governance expectations become formal requirements.

Who does this apply to?

Any organisation using AI in professional workflows, especially where outputs reach clients, inform decisions, or carry reputational risk. Agencies, consulting firms, research teams, and advisory practices are directly in scope.

How does the automated risk monitoring work?

Every AI response passes through a heuristic screening layer that checks for common risk patterns at zero cost. When a potential issue is detected, a secondary AI classifier evaluates the response and assigns a severity level. The entire process runs in the background and does not slow down the user experience.

Is Qonera a legal compliance tool?

No. Qonera is a professional integrity layer for AI-assisted work. It helps teams review, challenge, and approve outputs before they go out, which supports good governance, but is not a substitute for legal advice.