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AI Governance Framework

AI Governance Framework

Purpose

Implementing robust, efficient AI governance structures is crucial for overseeing the development, deployment, and operation of AI systems. Effective governance ensures that AI systems perform reliably, ethically, and align with organizational goals throughout their lifecycle. This requires establishing and continuously maintaining clear frameworks and control management practices. Identifying and mastering AI risks is a central obligation for Unique. It ensures that AI systems can be implemented with confidence, knowing that there are structures in place to manage any potential risks effectively.

The Unique AI Governance Framework is designed to ensure the reliability, factual accuracy, and integrity of outputs generated by its AI platform. This framework adheres to current good industry standards for responsible AI, including principles outlined in FINMA Guidance 08/2024 and relevant international standards like Singapore Model AI Governance Framework. The Unique AI governance structure encompasses the following key pillars: AI Governance, Inventory and Risk Classification, Data Quality, Testing & Continuous Monitoring, Documentation, Explainability, and Independent Reviews - all supported by the Unique’s ISO 42001 certification (https://unique-ch.atlassian.net/wiki/spaces/PUBDOC/pages/1384775910).

Key Pillars of the Unique AI Governance Framework

Unique has created their own AI governance principles, which has been thoroughly operationalized and built in throughout the entire Unique AI platform.

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Trust

Trust is foundational for AI adoption, especially for agentic systems that act autonomously. By demonstrating consistent, ethical AI behavior aligned with client values, we enable both end-users and stakeholders to confidently deploy AI agents that make decisions and take actions on their behalf.

Safety & Security

Safety & Security ensures agentic AI operates within legal and regulatory guardrails while protecting against agent-specific risks like unauthorized tool access, unintended actions, and cascading failures. Robust security controls and compliance frameworks enable safe deployment of autonomous AI capabilities.

Organisational standards

Individual standards

  • End-User Terms and Conditions (T&Cs)​ - available upon request

  • Legal contracts - available upon request

Product standards

Accountability

Accountability establishes clear responsibility chains for both human operators and AI agents. Every action, whether by user or agent, is traceable, auditable, and attributed to specific identities. This includes role definitions, access rights, agent permission boundaries, and escalation protocols for autonomous decisions.

Reliability & Robustness

Reliability & Robustness encompasses continuous validation of agent performance across tools, tasks, and workflows. We systematically monitor agent success rates, error patterns, and decision quality, enabling proactive corrections before issues impact operations or cascade across multi-agent systems.

Explainability & Transparency

Explainability & Transparency means users understand not just what AI outputs, but what agents do and why. Full visibility into agent reasoning, tool selection, and action chains ensures human oversight remains meaningful and agents remain aligned with intended goals throughout autonomous workflows.