Assess Current AI Practices 

Clearly draft how AI is being used within your organization, both in its current form and how you envision it being utilized in the future. 

List all the intended use cases for the AI system, understand the onboarding process, and consider the budget, resources, and support needed to implement the technology successfully. Address critical factors such as privacy, security, proportionality, and ethical considerations to ensure comprehensive oversight.

Evaluate Existing Policies and Procedures

Ensure that current policies and procedures align with the organization’s mission, values, and risk management goals. Leadership should review and adjust AI risk management practices to integrate seamlessly into daily operations and reflect the organization’s broader strategic objectives.

Governance Framework

A solid governance framework is non-negotiable. This is the backbone of your AI strategy, which lays out all the rules, policies, and structures that guide how you use AI across your organization. 

Moreover, it helps align AI projects with your company’s goals, ensures you’re meeting legal and ethical standards, and keeps everything on track, from the early planning stages to decommissioning AI systems.

Roles and Responsibilities

Next, you must ensure everyone knows who’s responsible for what. Define roles clearly—who oversees model development, who monitors AI performance, and who is in charge when something goes wrong. 

When everyone knows their part, from developers to compliance teams, you can ensure that risks are handled properly and nothing is overlooked.

AI Lifecycle Policies

Lastly, think about the entire lifecycle of your AI systems, from when you first bring them in, through development and deployment, to when they’re eventually phased out. You need policies in place to manage risks at each stage. 

This includes regular risk assessments, monitoring AI behavior after deployment, and planning to address any issues. Your policies should also align with industry standards and ethical guidelines, ensuring your AI systems remain compliant, fair, and secure throughout its lifecycle.

• Align with Goals. Ensure policies reflect the organization’s mission and support strategic objectives.
• Check Compliance. Verify adherence to laws, regulations, and ethical standards.
• Assess Risk Management. Review how AI risks are identified, mitigated, and managed.
• Ensure Transparency. Confirm documentation supports accountability and human review.
• Evaluate Monitoring. Check that systems are audited regularly for compliance.
• Gather Feedback. Use stakeholder input to adapt policies to evolving needs.

Identify Gaps and Areas For Improvement 

During your review of current policies and procedures, look for any inconsistencies or inefficiencies. Focus on outdated processes, unclear responsibilities, and areas where AI risks are not fully addressed. Identifying these gaps should be your top priority to enhance your overall risk management strategy.

The next step involves pinpointing these weaknesses. 

• Pinpoint missing guidelines around AI system transparency.
• Identify gaps in accountability for monitoring AI outputs.
• Assess the absence of clear policies governing AI decision-making.
• Highlight areas where AI risk assessments are incomplete or outdated.
• Check for insufficient documentation on AI system performance evaluations.
• Determine if there is a lack of oversight in handling AI-related incidents.
• Look for any gaps in ethical and legal compliance specific to AI systems.

Once you’ve highlighted these areas, propose updates or changes. Strengthening your AI risk management framework includes implementing new protocols for ethical AI use, improving data handling procedures, or introducing regular assessments to ensure compliance with both internal and external standards.