风险评估
风险评估是一个 AI 评估模块，可帮助您在用户之前发现 AI 应用程序中隐藏的弱点。它使用我们专有的内部提示库，针对模拟真实世界对抗行为的目标挑战，对您的 AI 响应进行基准测试。从而及早洞察潜在的故障点，从而增强 AI 的可靠性、安全性和可信度。
风险评估是一个 AI 评估模块，可帮助您在用户之前发现 AI 应用程序中隐藏的弱点。它使用我们专有的内部提示库，针对模拟真实世界对抗行为的目标挑战，对您的 AI 响应进行基准测试。从而及早洞察潜在的故障点，从而增强 AI 的可靠性、安全性和可信度。
主要特点
风险评估是一个 AI 评估模块，可帮助您在用户之前发现 AI 应用程序中隐藏的弱点。它使用我们专有的内部提示库，针对模拟真实世界对抗行为的目标挑战，对您的 AI 响应进行基准测试。从而及早洞察潜在的故障点，从而增强 AI 的可靠性、安全性和可信度。
风险评估是一个 AI 评估模块，可帮助您在用户之前发现 AI 应用程序中隐藏的弱点。它使用我们专有的内部提示库，针对模拟真实世界对抗行为的目标挑战，对您的 AI 响应进行基准测试。从而及早洞察潜在的故障点，从而增强 AI 的可靠性、安全性和可信度。
Early Risk Detection
Identify potential safety or ethical issues before real-world failures occur.
Compliance Documentation
Generate audit-ready reports aligned with regulatory requirements.
Lifecycle Risk Management
Cost & Uncertainty Reduction
Generate audit-ready reports aligned with regulatory requirements.
Trust & Transparency
Strengthen confidence among customers, partners, and regulators through open evaluation.
Maintain continuous observability and control across the entire AI lifecycle.
应用场景
风险预检
在部署用于临床之前，请评估该模型的幻觉答案、道德合 规性以及患者互动中的潜在安全问题。
风险预检
在部署用于临床之前，请评估该模型的幻觉答案、道德合规性以及患者互动中的潜在安全问题。
风险预检
在部署用于临床之前，请评估该模型的幻觉答案、道德合规性以及患者互动中的潜在安全问题。
应用程序验证
模拟用户操作和对抗性提示，以确定推荐引擎是否会受到影响而违反 KYC/AML 合规性。
How it works
From risk definition to actionable evidence
Scope
Map the business use case to AIDX’s multi-level safety taxonomy and define measurable thresholds.
Generate & curate
Construct targeted evaluation datasets covering both standard and adversarial scenarios.
Execute
Run structured assessments across selected models or model versions under controlled conditions.
Evaluate
Quantify performance using industry-recognized safety metrics to ensure objective and reproducible comparison
Interpret
Synthesize results into interpretable summaries highlighting key strengths and potential risks.
Recommend
Provide clear, evidence-based improvement guidance for model alignment, prompt design, or policy optimization.
Key features of the capability
1
Standardized Benchmarking
Employs consistent, industry-accepted metrics for fair cross-model evaluation.
2
Interpretable Methodology
Provides transparent scoring logic and clearly defined evaluation criteria.
3
Traceable Evidence Base
Every result is backed by categorized test sets and documented behavioral analysis.
4
Policy Alignment
Evaluation thresholds and taxonomies are configurable to match organizational or regulatory frameworks.
5
Extensible Framework
Supports continuous evolution through the addition of domain-specific safety categories while maintaining comparability and methodological integrity.
Why AIDX ?
Methodology first
research-grounded scoring and rationales—not just dashboards.
Governance ready
artifacts structured for internal review boards and audits.
Lifecycle fit
supports pre-launch certification and post-launch regression monitoring
Composable
integrates with robustness/RAG checks; deployable as report, API, or embedded controls.