Catalog
concept#AI#Governance#Reliability#Security

AI Safety

AI Safety describes concepts and measures to minimize risks from AI systems.

AI safety concerns principles, methods and governance to ensure AI systems act reliably, predictably and without causing harm.
Emerging
High

Classification

  • High
  • Organizational
  • Organizational
  • Intermediate

Technical context

MLOps platforms (e.g., CI/CD for models)SIEM and monitoring systemsIdentity and access management (IAM)

Principles & goals

Prevention first: proactively identify and mitigate risks.Transparency: make decisions and models explainable where possible.Accountability: define clear roles, responsibilities and escalation paths.
Discovery
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Lack of accountability leads to unclear escalation paths.
  • Overfitting safety measures can hinder innovation.
  • Insufficient monitoring lets harmful behavior remain undetected for long.
  • Perform adversarial and robustness tests before rollout.
  • Include human-in-the-loop for critical decisions.
  • Ensure versioning and reproducibility of all models.

I/O & resources

  • Training and test data with bias analyses
  • Model artifacts and evaluation reports
  • Policies, compliance requirements and stakeholder inputs
  • Risk assessment and release decisions
  • Monitoring configuration and alerts
  • Documentation for explainability and tests

Description

AI safety concerns principles, methods and governance to ensure AI systems act reliably, predictably and without causing harm. It covers risk assessment, robustness, transparency and regulatory measures. The goal is to prevent unintended harms and reduce long-term risks. It combines technical, organizational and legal perspectives.

✔

  • Reduction of harm and liability risks through preventive measures.
  • Increased trust from users and regulators in AI products.
  • Better controllability and early warning for misbehavior.

✖

  • Absolute safety is unattainable; residual risks remain.
  • High effort required for validation, monitoring and governance.
  • Explainability can conflict with performance and complexity.

  • Incident frequency

    Number of safety-relevant incidents per operational period.

  • Robustness score

    Measure of model stability against perturbations and adversarial inputs.

  • Explainability coverage

    Share of decisions for which adequate explanations are available.

Content moderation with safety policies

Platform implements rules, monitoring and human escalation for automated moderation.

Robust control for autonomous test vehicle

Test environment validates fault tolerance and safety shutdowns during operation.

Governance board for AI products

Interdisciplinary board reviews risks, policies and approvals before market entry.

1

Identify stakeholders and establish a governance board.

2

Define risk criteria and metrics.

3

Implement testing and monitoring pipelines.

4

Introduce release processes with canaries and rollback mechanisms.

5

Conduct regular audits and simulations.

6

Continuously improve based on incidents and metrics.

⚠️ Technical debt & bottlenecks

  • Outdated monitoring pipelines without test coverage.
  • Insufficiently documented models and decisions.
  • Monolithic systems that prevent fast updates.
Lack of explainable modelsLimited test data for rare eventsCross-disciplinary governance communication
  • Deploying AI without bias analysis in sensitive decision processes.
  • Replacing transparency with technical details instead of understandable explanations.
  • Delegating governance responsibility entirely to external consultants.
  • Overly tight formalizations that prevent adaptive responses.
  • Underestimating rare but severe scenarios.
  • Lack of communication between technical and legal teams.
Machine learning engineers with robustness expertiseSecurity and risk analystsLegal and compliance specialists
Robustness to adversarial inputsTraceability and explainability of decisionsContinuous monitoring and incident response
  • • Data protection laws and regulatory requirements
  • • Limited compute resources for comprehensive testing
  • • Business requirements that favor fast releases