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With a centralized GenAI governance framework, the airline now benefits from secure, compliant, and standardized AI adoption.

This client is a leading U.S. passenger airline with a significant global presence, operating an extensive network of domestic and international flights that connect hundreds of cities worldwide.

Challenge

A major airline needed a centralized GenAI Governance Framework to ensure secure, compliant, and standardized AI adoption across multiple Lines of Business. This shared service was required to mitigate risks, streamline oversight, and enable efficient AI deployment.

Solution

Orion developed a GenAI Governance Framework by identifying and integrating best-in-class governance tools, both commercial and open-source, while addressing gaps with custom-built solutions. Prioritizing Microsoft Azure-based services, Orion built a runtime governance platform for an integrated single pane of glass view into underlying governance controls.

Impact

  • Regulatory Compliance: Ensured adherence to policies, ethics, and transparency guidelines.
  • Risk & Security Controls: Established governance, access, and data security measures.
  • Standardization: Developed best practices, production standards, and a Model Registry.
  • Bias & Explainability: Implemented processes for bias detection and model transparency.
  • Monitoring & Auditing: Enabled performance tracking, compliance reporting, and cost oversight.

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FAQs

Your questions,
answered.

A GenAI governance framework is a structured set of policies, tools, controls, and processes for managing generative AI safely. It helps organizations monitor usage, manage risk, protect data, and support compliant AI adoption. 

Airlines need AI governance because they operate in complex, regulated, and security-sensitive environments. A governance framework helps ensure AI systems are used responsibly, consistently, and in line with internal policies and compliance requirements. 

Responsible AI governance works by setting standards for security, access, model usage, bias detection, explainability, monitoring, and auditing. These controls help organizations manage AI risks while enabling teams to deploy AI more confidently. 

A model registry is a centralized place to track AI models, their versions, owners, status, and deployment details. It supports standardization, oversight, auditability, and responsible management of models across the enterprise. 

Bias detection is important because AI systems can produce unfair, inaccurate, or misleading outputs if not monitored. Detecting bias helps organizations improve transparency, reduce risk, and build more trustworthy AI applications. 

Companies should create a GenAI governance framework before scaling AI use across multiple teams or business units. It is especially important when AI systems handle sensitive data, support business decisions, or operate in regulated industries. 

AI monitoring and auditing help organizations track model performance, compliance, security, cost, and risk over time. These practices make it easier to detect issues, prove accountability, and maintain control after AI systems go into production. 

A single governance view helps AI adoption by giving teams centralized visibility into policies, controls, risks, and model activity. This supports consistent oversight and reduces fragmented decision-making across business units. 

Answer