Hybrid AI Deployment System

A Hybrid AI Deployment System is a software deployment system that combines sanctioned AI components with partially authorized AI elements under conditional governance oversight.

• AKA: Mixed AI Deployment, Semi-Authorized AI System, Partial AI Governance System, Transitional AI Platform.
• Context:
  • It can typically bridge Shadow AI Systems to Sanctioned AI Systems through phased authorization.
  • It can typically allow AI Experimentation within defined boundaries.
  • It can typically provide Conditional AI Access based on risk assessment levels.
  • It can typically implement Graduated AI Controls reflecting maturity stages.
  • It can typically enable Innovation Flexibility while maintaining core security standards.
  • ...
  • It can often emerge during AI Transformation Periods as interim solutions.
  • It can often accommodate Department-Specific AI Needs with local approvals.
  • It can often utilize AI Sandbox Environments for controlled testing.
  • It can often require Progressive Compliance meeting incremental requirements.
  • ...
  • It can range from being a Loosely Hybrid AI Deployment System to being a Tightly Hybrid AI Deployment System, depending on its control stringency.
  • It can range from being a Temporary Hybrid AI Deployment System to being a Permanent Hybrid AI Deployment System, depending on its deployment duration.
  • It can range from being a Limited Hybrid AI Deployment System to being a Extensive Hybrid AI Deployment System, depending on its organizational reach.
  • It can range from being a Low-Risk Hybrid AI Deployment System to being a Managed-Risk Hybrid AI Deployment System, depending on its risk tolerance.
  • ...
  • It can balance AI Innovation Speed with AI Security Requirements.
  • It can transition Shadow AI Systems toward full authorization.
  • It can pilot AI Governance Frameworks through controlled rollouts.
  • It can measure AI Consumer Surplus Measures via mixed deployment data.
  • It can inform AI Economic Impact Measures through hybrid performance metrics.
  • ...
• Examples:
  • Organizational Hybrid AI Deployment Systems, such as:
    • AI Center of Excellence Model, with approved core tools and experimental edge tools.
    • Business Unit AI Autonomy, allowing local AI choices within corporate guidelines.
    • Innovation Lab AI Framework, permitting rapid prototyping before formal review.
  • Technical Hybrid AI Deployment Systems, such as:
    • API Gateway AI Control, monitoring external AI services with usage limits.
    • Containerized AI Environment, isolating experimental models from production systems.
    • Federated AI Learning System, combining central models with local adaptations.
  • Governance Hybrid AI Deployment Systems, such as:
    • Tiered AI Approval Process, requiring different authorization levels by risk category.
    • Pilot-to-Production AI Pipeline, graduating successful experiments to full deployment.
    • Exception-Based AI Management, allowing justified deviations from standard policy.
  • ...
• Counter-Examples:
  • Fully Sanctioned AI System, which has complete authorization.
  • Pure Shadow AI System, which lacks any oversight.
  • Rigid AI Governance, which prohibits flexibility.
• See: AI Deployment System, Sanctioned AI System, Shadow AI System, AI Governance Framework, Innovation Management System, Risk Management Framework, AI Adoption Pattern.

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