Scalable AI Governance: Designing, Auditing and Improving Your AIMS

Artificial intelligence (AI) innovations can benefit organizations across multiple sectors. However, with those technological advancements come increased needs to comply with ethical, operational and legal requirements. As tech evolves, organizations must meet higher standards when it comes to Artificial Intelligence Management Systems (AIMS).

ISO/IEC 42001 is the first internationally recognized standard for AIMS. It addresses the ethical considerations associated with AI's continuous learning and provides organizations with a structured approach to managing potential risks.

Balancing AI use and governance helps you stay compliant as you scale. By embracing these standards, you learn the requirements for effective AI usage and how to identify risks and collect evidence for audits. ISO 42001 also improves knowledge of accountability and best practices for AI deployment.

Experts expect AI to grow into an $800 billion industry by 2030, and complying with recognized standards when using AI will be an important part of future success. To achieve scalable AI governance in designing, auditing and improving your AIMS, meeting regulatory and data protection requirements is essential.

Technical Capabilities and Performance

ISO 42001 offers a comprehensive look at compliance and capabilities required for all organizations procuring or deploying AI, including third-party open source software (OSS) learning models. It also covers the organizational AIMS scope across the entire AI life cycle, including:

Problem scoping business issues and opportunities with AI
Collecting data and preparing for trustworthy ethical training
Developing models and testing AI algorithms and architectures
Deploying and integrating the AI model
Monitoring the model's performance
Planning to retire the model

In terms of performance outcomes, complying with ISO 42001 allows a faster, safer AI that's aligned with or improving on existing ISO standards and includes the following advantages:

Improved performance through efficient risk identification
More control over bias, drift and security
Safer AI output through defined trusted gates
Alignment with ISO standards like 9001, 22301, 27001 and 27701
Works with ISO 23894 AI risk management

System Configuration and Controls

A system is only as effective as its design. As organizations configure AI management systems, they must define the scope boundary and system inventory. A lack of a defined scope can lead to fluctuating AIMS that are too shallow to offer depth or too wide-ranging to be manageable. ISO/IEC 42001 has a clause in place that requires clearly defined areas of an organization that fall under AIMS.

These requirements include the geographical area in which the AIMS is applied, the configuration interfaces and dependencies associated with other systems, and the extent of AI activities. The AIMS scope is a strong foundation for implementing effective configurations and controls that demonstrate compliance. By identifying the AI systems used and analyzing their risk, organizations can align business and regulatory goals to reduce compliance gaps and inefficient controls.

Safety and Customization

Safety and compliance in artificial intelligence should involve customized risk profiles specific to each sector and industry. Conduct detailed risk assessments to locate safety issues and their consequences, followed by classifying the associated AI systems by the sector risk profile and criticality. By factoring the AI system's importance into organizational operations, determining which applications are most important for safety and security is simpler.

Customizing risk metrics provides a clear path toward identifying organizational, individual, and environmental consequences, which bolsters escalation and mitigation plans. For highly regulated industries, this may involve increased attention to critical AI systems, ongoing compliance monitoring and scheduled audits to find gaps and support regulatory readiness.

Evidence and Audit Readiness

Stage 2 of the ISO 42001 certification process requires documented evidence of items such as:

AIMS policy
Scope statements
Records of training
AI risk and impact assessments

You will also need to prove these items align with ISO standards and integrate well with current systems.

Conducting a comprehensive gap analysis helps to identify where your processes don't meet ISO 42001 requirements. Audit and certification plans should focus on keeping evidence relevant, current and focused on records as opposed to narratives. Consider the following aspects before an audit:

Ethical AI practices and procedures that focus on data protection and privacy
How effectively you keep your processes and documents
Data governance evidence and documentation
Monitoring plan and performance results

This information will illustrate key areas of vulnerability that you must address before an AI audit. Combined with a long-term focus on improvements and evidence of best practice, you'll have a strong foundation of proof and process.

Regulatory Alignment with the EU AI Act

For AI governance that scales, consider how ISO 42001 provides a practical framework for adhering to the regulatory requirements outlined in the EU AI Act. This act offers key controls, processes and management system frameworks to comply with the European Union's expectations for organizations to deliver ethical AI, transparency and human oversight.

While ISO 42001 and the AI Act are distinct, the ISO framework presents an opportunity to implement the act to demonstrate compliance. Aligning effectively with the AI Act in this way can influence faster compliance readiness and a process of maintaining reusable evidence.

When planning scalable AI governance by designing, auditing and improving your AIMS, aligning with lawful legislation signifies to clients how seriously you take protecting data and ensuring compliance.

NQA's Expertise in Gap Assessment for Audit Readiness

Mapping your current organizational controls to ISO 42001 helps develop trust with clients and ensures compliance with AI regulatory matters. NQA's decades of experience in management system auditing enable us to provide services across multiple AI sectors and types.

From small organizations to large corporations across 90 countries, we have supported clients with ISO certification for more than 30 years, with an average support time of over 10 years.

We understand why preparing for audit readiness involves preparation, integration, and certification that align with ISO audits while introducing metrics to strengthen performance. NQA's gap assessment and certification services are detailed and aligned with the latest guidelines in AI governance.