Catalog
method#Artificial Intelligence#Governance#Quality Assurance#Reliability

AI Safety Evaluation

A structured method to assess risks, robustness and governance of AI systems. Produces prioritized actions and decision-ready outputs for safer deployments.

AI Safety Evaluation is a structured method for systematically assessing risks, robustness, and governance of AI systems.
Emerging
High

Classification

  • High
  • Organizational
  • Organizational
  • Intermediate

Technical context

Model repository (e.g., MLflow, DVC)Monitoring and observability tools (e.g., Prometheus, OpenTelemetry)Issue tracking and governance boards (e.g., Jira, Confluence)

Principles & goals

Holistic approach: evaluate technical, data and organizational aspects together.Risk orientation: focus on likely impacts and user consequences.Transparency: document findings, assumptions and uncertainties.
Discovery
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • False reassurance from incomplete assessments.
  • Lack of accountability if not anchored in governance.
  • Excessive delays due to overly conservative measures.
  • Iterative application: small, regular reviews rather than infrequent large audits.
  • Involve cross-functional teams (legal, product, ML, ops).
  • Combine automated tests with manual spot checks.

I/O & resources

  • Model documentation (version, architecture, hyperparameters)
  • Training and test data metadata
  • Operational metrics, monitoring and incident logs
  • Risk assessment with prioritization
  • Concrete remediation and monitoring recommendations
  • Audit report for governance and compliance

Description

AI Safety Evaluation is a structured method for systematically assessing risks, robustness, and governance of AI systems. It combines technical, data, and organizational analysis to reveal vulnerabilities, compliance gaps, and operational risk. Outputs are prioritized remediation actions and decision-ready reports for safer AI deployment.

✔

  • Early detection of critical weaknesses before production.
  • Improved compliance and auditability for regulators.
  • Clearly prioritized actions for risk-based resource allocation.

✖

  • Blind spots for unknown failure modes of novel models.
  • High effort required for deep technical validation and data analyses.
  • Outcome quality depends on availability and quality of input data.

  • Misclassification rate by group

    Misperformance metric split by relevant subgroups for bias analysis.

  • Robustness to input perturbations

    Change in model performance under defined perturbation scenarios.

  • Time-to-detect an incident

    Average time from occurrence of an issue to its detection.

Enterprise-wide safety assessment

Case study: assessment of multiple AI applications at a financial institution with prioritized actions.

Startup checklist

Compact evaluation for small teams focused on data risks and monitoring.

Regulatory audit template

Template for evidencing to regulators, aligned with privacy and security requirements.

1

Initial scoping: define scope, stakeholders and acceptance criteria.

2

Data collection: gather model docs, test sets and monitoring data.

3

Technical checks: run robustness tests, bias analyses, security checks.

4

Organizational review: assess responsibilities, SLAs and escalation paths.

5

Report & action plan: prioritize, communicate and set implementation timelines.

⚠️ Technical debt & bottlenecks

  • Missing test data infrastructure for reproducibility.
  • Insufficient monitoring for long-term behavior.
  • Non-versioned model artifacts complicate audits.
Data quality and accessCross-functional coordination (legal, product, ML)Lack of specialized evaluation tooling
  • Relying solely on model-card metrics for safety decisions.
  • Suppressing critical findings out of fear of delays.
  • Incomplete data views leading to incorrect risk assessments.
  • Overlooking subtle distribution shifts in production.
  • Unclear ownership after identifying deficiencies.
  • Overly narrow checklists that miss creative misuse forms.
ML model evaluation and statisticsDomain knowledge and risk analysisCompliance and governance understanding
Regulatory requirements (privacy, product safety)Operational robustness and monitoring capabilityExplainability and decision traceability
  • • Confidentiality and IP restrictions on model inputs
  • • Limited observability in production systems
  • • Time resources required for in-depth tests