AI Workflow Risk Assessor Workspace

Select an SME workflow preset or configure custom parameters below to generate an instant risk classification score, radar matrix, and contextual mitigation checklist.

1. Workflow Parameters

Client-Side Processing

Risk Dimensions Assessment

50%
Considers PII, non-public financial ledgers, and vendor model training opt-outs.
50%
Evaluates operational, legal, or financial cost of factual model hallucinations.
50%
Measures automated decision authority, sign-off complexity, and audit trail needs.
50%
Assesses output commercial usage, training data exposure, and software licensing.
50%
Determines required employee manual verification before action execution.

2. Risk Score & Assessment

50
Moderate Risk Workflow

Standard operational risks. Requires defined verification steps and baseline data safeguards.

Dimension Radar Profile

Privacy Hallucination Governance Copyright HITL

Recommended Safeguards

How to Interpret Your AI Workflow Risk Assessment

The SME AI Workflow Risk Index provides a normalized 0 to 100 risk magnitude score designed to help managers, operations directors, and legal counselors determine the appropriate governance posture prior to launching Generative AI integrations.

Risk Classification Tiers

  • Low Risk (0 - 25): Internal, non-sensitive tasks with negligible data privacy concerns. Model hallucination causes minor delays rather than financial liability. Examples include internal brainstorming, preliminary keyword research, and non-confidential text formatting.
  • Moderate Risk (26 - 50): Operational workflows involving internal business text or mild customer interaction. Requires standard review procedures and explicit vendor terms validation to confirm data is not used for model training.
  • High Risk (51 - 75): Tasks handling sensitive customer data, proprietary code, or financial planning. Mandatory Human-in-the-Loop (HITL) review protocols and Zero Data Retention (ZDR) enterprise APIs are required.
  • Critical Risk (76 - 100): High-stakes workflows such as automated legal contract review, autonomous healthcare advice, or unvetted HR resume filtering. Requires executive sign-off, legal counsel review, and sandboxed deployment.

Key Assumptions & Methodological Boundaries

This evaluation model assumes standard commercial foundation model APIs (e.g., OpenAI, Anthropic, Google Gemini, AWS Bedrock). It evaluates inherent workflow risk prior to custom fine-tuning or secondary security controls. The output serves as an operational decision framework, not formal legal advice.