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Free AI Scaing Guide

Scale AI With a Risk-Based Control Framework

Build an AI governance approach that matches oversight to risk—combining regulatory expectations with the operational, financial, reputational, security, and data risks that matter to your organization.

  • A six-phase methodology for managing AI risk across the lifecycle
  • A practical approach to identifying, assessing, and prioritizing use-case-specific risks
  • A four-pillar control model spanning governance, prevention, detection, and correction
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What's Inside the Guide

A practical methodology for turning AI risk assessment into proportionate controls, clear accountability, and continuous governance.

1
AI Use Case Analysis

Define the solution’s purpose, business objectives, users, stakeholders, models, data sources, integrations, regulatory context, and human oversight.

2
Risk Analysis & Assessment

Identify relevant risks and evaluate their likelihood and impact across the organization, affected individuals, and society.

3
Control Identification & Implementation

Select safeguards that address prioritized risks and reduce exposure to a level aligned with the organization’s risk appetite.

4
Ownership & Accountability

Assign owners for individual risks and controls so that implementation, monitoring, approval, and risk acceptance are explicit.

5
Integration Into Organizational Processes

Embed the framework into existing governance structures, project delivery methods, and operating processes across the AI lifecycle.

6
Continuous Monitoring & Improvement

Reassess risks and controls as models, data sources, functionality, operating environments, and regulatory expectations change.

This guide combines regulatory expectations with a business-oriented risk perspective and Nemko Digital’s four-pillar model for structuring AI controls.

This Is for You If...

  • Your organization is expanding its use of AI and needs governance that can scale across different use cases
  • You need to evaluate AI risk beyond regulatory compliance, including potential operational, financial, reputational, cybersecurity, intellectual-property, and third-party impacts
  • Your teams need a repeatable way to connect risk assessments with proportionate controls, accountable owners, and ongoing monitoring
Not quite right?

If you are looking only for a high-level summary of AI regulation, this guide may be more detailed than you need. It is designed for organizations that are ready to establish an AI risk-management framework or mature existing governance practices.

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Trusted by Global Leaders

Established by the Norwegian government in 1933, Nemko has 90+ years of heritage in testing, inspection, and certification. With 850+ employees across 28 locations on 3 continents, we serve 7,000+ customers in over 150 countries.

  • Samsung
  • Panasonic
  • LG
  • Jotron
  • Wärtsilä
  • Kärcher

Frequently Asked Questions

  • Is this guide really free?

      Yes. There is no hidden cost or trial. Download it, use it as a practical reference, and share it with the teams involved in AI governance and risk management.

  • How long is the guide?

      The guide is 13 pages and is structured for practical use. It explains the full methodology while making the six phases, three impact dimensions, assessment logic, and four control pillars easy to revisit.

  • What is a risk-based AI control framework?

      A risk-based AI control framework connects the risks associated with a specific AI use case to proportionate safeguards. It brings together identified risks, assessed risk levels, selected controls, ownership responsibilities, and governance decisions so that oversight reflects the actual exposure rather than applying the same requirements to every AI system.

*Built for AI leaders, product teams, risk managers, compliance specialists, information security professionals, business owners, and executive sponsors.

Get the AI Risk Management Guide

Use this practical guide to identify relevant AI risks, prioritize them consistently, select proportionate controls, and embed accountable governance throughout the AI lifecycle.

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