Deeploy’s guided risk assessment turns complex EU AI Act requirements into actionable steps. Classify your AI systems, understand your obligations, and implement the right controls in minutes.
The AI Act uses a risk-based framework where AI systems are classified as unacceptable, high-risk, limited-risk, or minimal-risk. Getting your risk classification wrong means deploying non-compliant AI systems, facing potential fines up to €35 million or 7% of global turnover, and exposing your organization to regulatory scrutiny. Deeploy eliminates guesswork with a structured risk assessment workflow that guides you to accurate classifications and immediate compliance actions.
Organizations struggle with EU AI Act risk assessment for several reasons:
Deeploy’s AI governance platform transforms complex EU AI Act compliance into an actionable workflow. Our risk assessment tool makes AI risk classification within the EU AI Act requirements simple. In just a few steps, you’ll:
About risk assessment for the EU AI Act
An EU AI Act risk assessment is a structured evaluation process that determines which of the four risk categories (unacceptable, high, limited, or minimal) applies to your AI system based on its intended purpose, deployment context, and potential impact on fundamental rights and safety.
Deeploy provides a guided questionnaire that evaluates your AI system against EU AI Act criteria. You receive an automated risk classification recommendation, can apply it directly to your use case, and get access to relevant compliance controls, all documented for regulatory review.
Risk assessments should be reviewed whenever you modify your AI system’s functionality, change its intended purpose, deploy it in a new context, or when regulatory guidance evolves. Deeploy allows you to save progress and return to assessments as your AI systems evolve.
Based on your risk classification, Deeploy surfaces relevant controls from frameworks like the EU AI Act, ISO 42001, and Responsible AI standards. These include technical requirements (monitoring, explainability, human oversight) and organizational measures (documentation, governance processes) that you can implement directly on your AI deployments.
