Catalog
concept#AI#Governance#Data#Security

AI Governance

Framework and processes for the responsible, safe and legally compliant use of AI systems.

AI governance establishes frameworks, processes and accountabilities for safe, fair and legally compliant deployment of AI systems.
Emerging
High

Classification

  • High
  • Organizational
  • Organizational
  • Intermediate

Technical context

Data catalogs and metadata systemsCI/CD and MLOps pipelinesIT security and compliance tools

Principles & goals

Risk-based approachTransparency and accountabilityData protection and minimization
Discovery
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Overhead and delays to innovation due to bureaucratic processes.
  • Lack of accountability when roles are unclear.
  • False sense of security if controls are only superficial.
  • Risk-based prioritization instead of one-size-fits-all
  • Cross-functional committees for reviews and escalations
  • Automated monitoring coupled with manual reviews

I/O & resources

  • Model and data documentation
  • Risk register and impact assessments
  • Legal requirements and data protection rules
  • Governance policies and checklists
  • Audit reports and compliance evidence
  • Operationalized monitoring and escalation processes

Description

AI governance establishes frameworks, processes and accountabilities for safe, fair and legally compliant deployment of AI systems. It includes policies, risk assessments, monitoring and procedures for model explainability and auditability. The aim is to build trust and systematically meet regulatory and ethical requirements across the organization.

✔

  • Reduces legal and reputational risks via structured controls.
  • Improves trust with users and regulators.
  • Enables repeatable processes for assessment and monitoring.

✖

  • Implementation requires organizational change and effort.
  • Not all risks can be fully eliminated technically.
  • Regulatory requirements may vary by region.

  • Number of governance reviews

    Number of model and risk reviews performed per period.

  • Incident rate due to model failures

    Number of significant misdecision or bias incidents in production.

  • Compliance deviations

    Number of deviations from internal policies or regulatory requirements.

EU guidelines for trustworthy AI (application)

Organization aligns processes with EU ethics principles, implements impact assessments and documents decisions systematically.

Risk-driven approval in a bank

Bank defines different review paths for credit scoring models based on risk and audit needs.

Monitoring playbook at a SaaS provider

SaaS provider establishes alerts, retraining rules and dashboards to monitor model performance.

1

Starter assessment for inventory and prioritization

2

Define policies, roles and review processes

3

Introduce technical basics: logging, monitoring, explainability

⚠️ Technical debt & bottlenecks

  • Missing automated logs and metrics
  • Unversioned model artifacts and datasets
  • Ad-hoc workarounds instead of sustainable integrations
Lack of data transparencyUnclear responsibilitiesMissing automation for monitoring
  • Only document but not implement review processes
  • Governance as PR exercise without real controls
  • Controls on low data level while model risks remain ignored
  • Ignoring cultural change needs during introduction
  • False belief that tools can replace governance
  • Insufficient documentation for audits
Knowledge of ML models and data qualityUnderstanding of regulatory requirementsProcess and risk management skills
Traceability of decisionsScalability of review and monitoring processesIntegration of compliance and security requirements
  • • Regional regulatory differences
  • • Limited resources for compliance operations
  • • Technical legacy systems hinder integration