AI Governance Best Practices: A Guide for Compliance Teams

AI is rapidly becoming part of day-to-day operations in financial crime compliance, especially in AML. But while the use of AI is accelerating, the frameworks to manage its risks often lag behind.

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This gap can create significant exposure for compliance teams. Without proper governance, AI tools may introduce bias, produce errors, or make decisions that are difficult to explain during audits or exams.

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For organizations beginning to explore AI in AML compliance, it’s important to start with a practical, scalable approach to governance. This guide outlines AI governance best practices to get started, based on insights shared in a recent Unit21 webinar featuring compliance and AI governance experts.

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Why AI Needs Its Own Governance

AI introduces new risks that traditional model governance may not fully address. While existing model risk frameworks can be a foundation, AI adds complexity in areas like:

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• Bias: Models may treat similar customers differently if the training data isn’t balanced.
• Explainability: Outputs may be difficult to understand or justify, especially with black-box models.
• Drift: AI systems can evolve over time, sometimes in ways that degrade performance or accuracy.
• Automation: AI may trigger decisions or actions without direct human input, raising questions around accountability.

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These issues are especially important in AML, where decisions can affect customer access, trigger regulatory filings, or escalate into reputational risks. With regulators increasingly focused on AI in financial services, governance can no longer be optional; it’s a core part of risk management.

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The Crawl-Walk-Run Strategy

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Building an AI governance program doesn’t need to be overwhelming. A phased approach can help compliance teams make steady progress while staying aligned with broader risk frameworks.

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Crawl: Inventory and Awareness

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Start by understanding where AI is already in use. Many organizations already rely on machine learning in transaction monitoring, adverse media screening, or KYC tools; sometimes without realizing it. This phase is about visibility. Even if AI is not yet widely adopted, it’s important to know where it might be introduced in the future.

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Key steps:

• Identify all tools or systems using AI or machine learning.
• Document their purpose, inputs, and decision outputs.
• Note whether they influence regulatory outcomes (e.g., SARs, customer exits).

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Walk: Update Governance Policies

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Once AI use is identified, the next step is to update governance policies. This doesn’t mean starting from scratch. Instead, adapt existing model risk policies to account for AI-specific risks. This sets the foundation for oversight, ensuring AI governance best practices are incorporated to manage explainability, bias, and other concerns.

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Focus areas:

• Incorporate AI into the enterprise risk assessment.
• Define how AI risks (e.g., explainability, bias) map to your existing risk appetite.
• Add language to model governance documents that outlines expectations for AI systems.

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Run: Build Controls and Oversight

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With policies in place, the next step is to implement controls. Start small and prioritize tools with the greatest regulatory impact. Layering these controls can help compliance teams manage AI risk without slowing innovation.

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Examples of early controls:

• Require human review of any alert or recommendation made by AI.
• Use version control to track changes to AI models and policies.
• Begin testing AI outputs for accuracy, bias, and consistency.

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Connecting AI to Existing Risk Programs

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AI governance works best when it’s built into existing risk and AML compliance programs, not treated as a separate project. Integrating AI into broader frameworks ensures that its risks and impacts are considered alongside other operational and regulatory concerns.

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To do this effectively, start by updating the enterprise risk assessment to include AI-specific risks like bias, drift, and explainability. Then, map AI usage to your organization’s risk appetite to define where human oversight is required. 

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Finally, align governance efforts with AML activities such as transaction monitoring, customer due diligence (CDD), and SAR processes. This helps create a more consistent, accountable, and auditable approach to managing AI.

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Human Oversight: The First (and Most Important) Control

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A key principle in AI governance best practices is ensuring humans remain in control through a “human-in-the-loop” model. Practical approaches include:

• Alert Review: Always have humans review and validate alerts generated by AI. This helps catch any errors or false positives before they impact your compliance decisions.
• Quality Assurance (QA): Regularly sample alerts and the decisions made on them to ensure accuracy and consistency. QA checks help maintain the reliability of your AI system over time.
• Escalation: Ensure analysts can override or stop automated actions as needed. This control helps prevent mistakes and ensures human judgment stays central in critical situations.

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AI should support decision-making, not replace it, especially in high-risk areas like AML investigations or customer exits. Oversight is increasingly expected by regulators and should be part of any AI governance framework.

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Using Existing Guidance: ISO, NIST, and More

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You don’t have to start from scratch when building AI governance. There are established frameworks designed to help organizations manage AI risks effectively. Two key resources are the NIST AI Risk Management Framework and ISO/IEC 42001, which provide practical guidance for handling AI governance.

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These frameworks focus on important principles like transparency, human oversight, explainability, and continuous monitoring. These ideas are especially relevant for compliance teams working in AML, where trust and accountability are critical.

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Document Everything: Policies, Versions, and Audit Trails

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Good governance depends on solid documentation, but this doesn’t mean drowning in paperwork. It’s about keeping clear records of all changes, reviews, and decisions related to your AI systems. This helps ensure you can explain how your AI tools operate and evolve.

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Best practices include using version control for your AI policies, logging system updates or tests, and documenting key decisions, especially when automation affects outcomes. This level of transparency is essential if regulators or auditors come knocking.

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Ready to Apply These AI Governance Best Practices Today?

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Start building a stronger compliance program today by exploring Unit21’s AI Agent. This powerful platform combines AI-driven automation with human expertise, empowering your team to focus on real risks more effectively while following proven AI governance best practices.

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Don’t miss the chance to learn from the experts; register and watch the full webinar for practical insights on getting started with AI governance in AML compliance.