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AI Governance

What Is AI Governance?

Learn what AI governance is and why it’s essential for managing AI responsibly.

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Steps to AI Governance

Learn the key steps to building effective AI governance with support from Validaitor.

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Answering Your Questions

Get clear answers to commonly asked questions about AI governance and how to apply it.

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5 Steps to AI Governance

Validaitor guides you through five essential steps to test, monitor, and document your AI systems with confidence

Define Your Governance Framework

Start by identifying the regulatory frameworks that apply, such as the EU AI Act, ISO 42001, or NIST AI RMF. Validaitor helps by mapping your use cases to relevant global and industry-specific regulations.

1

Map AI Assets, Roles, and Controls

Create a comprehensive inventory of AI systems, define ownership and responsibilities, and implement appropriate controls to ensure accountability and lifecycle oversight.

2

Identify and Mitigate Risks

Conduct risk assessments to uncover potential harms related to bias, privacy, security, and performance. Implement mitigation strategies and document risk decisions to ensure responsible AI deployment.

3

Continuously Monitor and Validate Models

Establish ongoing monitoring for model performance and evolving regulations. Regular validation ensures models remain accurate, fair, and aligned with business and ethical goals over time.

4

Audit and Reporting

Maintain detailed records of decisions, model changes, and governance activities to demonstrate compliance with internal policies and external regulations. Be prepared for internal reviews or regulatory audits.

5

What is AI Governance?

Your Frequently Asked Questions

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    • Managing AI in critical infrastructures (i.e. energy, transportation, or healthcare) comes with a unique set of challenges:

      • Growing Complexity of AI Assets: As AI systems evolve, they become more complex and harder to track. Companies often struggle to maintain visibility over what models are running, where, and how they’re performing.

      • Diffusion of AI and Shadow AI: AI is no longer confined to centralized teams. Business units often develop or deploy models independently, leading to “shadow AI” that escapes governance and introduces risk.

      • Reliance on Vendors: Many organizations depend on third-party vendors for AI solutions. This creates a black-box problem—limited transparency into how models work, how they’re trained, or whether they comply with internal policies.

      • Regulatory Complexity: With AI regulations rapidly evolving—especially in the EU and other regions—companies face increasing pressure to ensure compliance. But aligning technical systems with legal frameworks is no small feat.

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    • At Validaitor, we believe that AI governance should feel natural. Our platform is designed with that philosophy in mind:

      • Governance That Feels Natural

        • It’s customizable, so teams can tailor governance to their workflows.

        • Supports collaboration across roles—data scientists, compliance officers, and business leaders can all work in sync without changing how they do their jobs.

      • All-in-One Platform:

        • We reduce the overhead of adoption by offering a comprehensive suite—from model inventory and risk assessments to documentation and AI Testing—all in one place.

      • Compliance Comes Ready:

        • We embed policy templates and regulatory mappings into the platform, so teams can innovate confidently, knowing they’re aligned with standards like the EU AI Act.

        • Documentation is automated, making it easy to generate the evidence needed for audits or internal reviews.

      • Enablement When Resources Are Limited:

        • Not every organization has a dedicated AI governance team. Validaitor helps bridge that gap, offering guidance and automation to support teams with limited resources.

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    • Insight: Our platform is designed to be compatible with legacy systems, providing seamless integration and enhancing the security posture without disrupting existing operations.

    • Integration is a key focus for us. Validaitor is built with an integration-ready design:

    • We support standard APIs and connectors that make it easy to plug into existing infrastructure—whether it’s SCADA systems, data lakes, or model deployment platforms.

    • Our platform is modular, so organizations can start small—perhaps with model inventory or risk scoring—and expand over time.

    • We also ensure data sovereignty and security, which is critical in sectors like energy where operational data is highly sensitive.

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