AI governance workflow
AI risk compliance, run as a guided workflow.
Inventory your AI systems, assess their risks against recognised AI governance frameworks, map controls and collect evidence — with owners, progress and an exportable assessment for every system.
Sign in to startWhat AI risk management involves — and how a guided workflow helps
AI risk management is moving from good practice to obligation. The EU AI Act phases in duties for providers and deployers of AI systems, regulators in Singapore, the UAE and Australia have published governance frameworks and expectations, and enterprise customers increasingly ask their vendors how AI systems are assessed before they buy. The underlying questions are consistent everywhere: what AI systems do you run, what could they do to the people they affect, who is accountable, and what controls and monitoring keep the risks acceptable.
Structured frameworks — the NIST AI Risk Management Framework, ISO/IEC 42001, and regulator guidance built on them — break that into concrete work: an inventory of AI systems and their contexts, risk identification across fairness, transparency, robustness, privacy and safety, controls mapped to each material risk, human oversight defined, and evidence that the controls operate. Like every governance exercise, it stalls when it lives in slide decks: assessments go stale, ownership is unclear, and nobody can show an auditor or a customer what was actually done.
regXperience runs AI risk as a guided workflow, the same way it runs ISO 27001 or a GDPR DPIA. Each AI system gets an intake describing purpose, data and affected people; a structured risk questionnaire aligned to recognised AI governance criteria; and a control-mapping stage where mitigations, owners and evidence are recorded — ending in an exportable assessment your customers, board or regulator can read. Where an AI system also processes personal data, the GDPR DPIA workflow runs alongside it, reusing the same project facts.
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Frequently asked questions
Do SMBs really need AI risk management?
If you deploy AI that affects customers or employees — scoring, screening, recommending, generating customer-facing content — yes. Obligations arrive both from regulation (the EU AI Act applies to deployers, not just model vendors) and from procurement: enterprise buyers now send AI governance questionnaires to vendors of any size. A lightweight, documented assessment per AI system is what answers both.
Which AI governance framework should I use?
For most SMBs the NIST AI RMF is the most practical starting point: free, widely referenced and mappable to the EU AI Act and ISO/IEC 42001 later. If your buyers ask for certification, ISO/IEC 42001 is the auditable management-system standard. regXperience structures its questionnaire so the same assessment evidence serves either path.
How does AI risk assessment relate to a GDPR DPIA?
They overlap heavily whenever an AI system processes personal data: the DPIA covers risks to data subjects, while AI risk management also covers safety, robustness, fairness and accountability beyond privacy. Regulators increasingly expect both for high-risk AI. Running them as two linked workflows against one system description avoids doing the intake twice.
What does the EU AI Act require from deployers?
Deployers of high-risk AI systems must use them per the provider’s instructions, ensure human oversight and input-data relevance, monitor operation, keep logs, and in some cases run a fundamental-rights impact assessment. Even for non-high-risk systems, transparency duties (like disclosing AI-generated content) apply. An AI system inventory with per-system risk assessments is the foundation for all of it.
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