AI Governance Guide — Policies, Roles and Controls |…

AI Governance: Policies and Controls

AI governance establishes a framework for managing the risks associated with artificial intelligence systems. This includes defining roles, processes, audit trails, and ensuring compliance with relevant regulations.

Key structures include an AI Center of Excellence (AI CoE), a Data Governance Board, and individual model owners responsible for specific AI models.

Policies and Standards

The Data Policy outlines rules for collecting, storing, and processing data, specifically addressing Personally Identifiable Information (PII), retention periods, and access controls. Prompt policies define permitted and prohibited prompts, alongside content filters to manage AI outputs.

Security Policies establish requirements for securing AI systems, encompassing encryption, authentication, and monitoring. Ethics policies define principles of fairness, transparency, and accountability within AI development and deployment.

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Multi-region AI governance is crucial to ensure compliance with diverse regulatory requirements such as the EU AI Act, UK GDPR, and US regulations. This involves tailoring policies and controls to specific regional needs.

Regional teams require training on these localized policies, alongside regular compliance reviews and knowledge sharing across regions.

Frequently asked questions

How should Service Level Indicators (SLIs) and Service Level Objectives (SLOs) be configured for quality, reliability, and security?

SLIs and SLOs should be configured to monitor key aspects like model accuracy, system uptime, and data security. Alerts should be established to trigger when an SLO is breached, alongside comprehensive logging of all AI operations for auditing purposes.

Where should one begin building an AI governance framework?

Start by assigning Model Owners responsible for each AI system's development, monitoring, and updates. Establish an AI CoE to coordinate strategy and best practices, alongside a Data Governance Board for data oversight and a Risk Committee for assessing potential risks.

What documents are essential for establishing an AI governance program?

Key documents include Model Cards detailing model characteristics, metrics, limitations, and risks; Data Cards describing datasets' sources, quality, and biases; a Risk Register tracking identified risks and mitigation strategies; and comprehensive policy documentation covering data, security, and ethics.

How can accountability be implemented within AI systems?

Implement thorough logging of all AI decisions with traceable identifiers, restrict access through Role-Based Access Control (RBAC), maintain audit trails for all operations, utilize version control for models, and leverage distributed tracing for debugging. Regular audits and transparency dashboards contribute to accountability.

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