Formation of Working Groups With Industry Leaders To Refine AI Risk Management Practices

AI is moving fast, really fast. Nearly every company invests in it, but only 1% would call themselves “AI mature.” That means most organizations are still figuring out how to make AI work efficiently, mitigate risks, and drive real business outcomes.

So, how do you bridge that gap? You don’t do it alone. This is where working groups with industry leaders come in. Bringing together AI experts, compliance specialists, and industry pioneers creates a powerful force for refining AI risk management strategies.

Why form AI Risk Management working groups?

• AI is evolving faster than regulations. Industry leaders are already tackling the challenges of AI governance, security, and compliance. Learning from them helps organizations adapt quickly.
• AI risk is not a one-company problem. Bias, security vulnerabilities, and regulatory shifts affect the entire industry. A working group fosters collaborative solutions instead of each company reinventing the wheel.
• Benchmarking against industry standards. Engaging with AI pioneers helps set the right benchmarks for best practices, risk assessments, and compliance strategies.
• Building AI maturity together. Moving from experimentation to full AI integration requires cross-industry learning and collaboration.

How to Build an Effective AI Risk Management Working Group

If you want to refine AI risk management practices, you need the right people in the room. Here’s how to structure a strong working group:

Include Cross-Industry Experts

AI risk is not isolated to a tech issue as it impacts compliance, security, business strategy, and ethics. Ensure representation from:

• AI model developers (to address technical risks like bias and drift)
• Compliance & legal teams (to navigate regulatory landscapes like GDPR, NIST AI RMF, ISO 42001)
• Cybersecurity specialists (to handle adversarial AI attacks and security vulnerabilities)
• Business leaders & risk officers (to ensure AI aligns with corporate goals and risk appetite)

Establish Clear Objectives 

AI risk management is broad, so define what the group aims to achieve, such as:

• Developing risk assessment methodologies that align with HITRUST AI RMF
• Sharing AI compliance strategies across industries
• Creating AI governance best practices for explainability, bias detection, and security

Set a Cadence for Collaboration 

AI risk evolves quickly, so working groups should:

• Meet quarterly or bi-annually for formal reviews
• Maintain ongoing discussions via digital collaboration platforms
• Establish a feedback loop to track progress and update risk strategies

Leverage HITRUST AI RMF as a Common Framework

HITRUST AI RMF provides a structured, adaptable foundation. The working group can:

• Map best practices to existing HITRUST controls
• Propose new risk management controls based on emerging AI threats
• Align AI maturity models with compliance standards