AI Governance & Risk Management: Importance, Components & Key Considerations 

Artificial intelligence (AI) isn’t just a high-tech tool reserved for large-scale enterprises – it’s increasingly being implemented in middle-market companies across finance, supply chain, HR, and beyond. 

Whether utilized to streamline financial reporting or automate day to day functions, AI systems are empowering companies to make faster progress and better strategic decisions. However, new efficiencies also mean new risks. 

Algorithm biases, the mishandling of sensitive data, regulatory blind spots, incurred costs – this is what’s waiting for companies that fail to implement a strong AI governance framework. 

As experts in helping companies mitigate risk, we’ve outlined what AI governance really means, why it matters for growing businesses, and how to build a framework that keeps your AI efforts productive, compliant, and trustworthy. 

Important things to know about AI governance:

1. What AI governance is and why it’s important
2. What makes up a strong AI governance framework
3. AI governance challenges
4. Which regulations lay the groundwork for AI governance 

What is AI Governance, and Why is it Important? 

AI governance involves policies, procedures, ethical principles, and regulatory considerations that control how artificial intelligence is developed, deployed, and managed within an organization. 

The goal is to ensure that a company’s use of artificial intelligence is transparent, accountable, safe, compliant, and aligned with the right values. 

When conducted correctly, AI governance: 

• Prevents bias and ensures fair treatment of customers, vendors, and employees. 
• Protects data privacy and upholds security standards. 
• Fosters transparency around how AI decisions are made. 
• Builds trust with regulatory bodies, stakeholders, and end users. 
• Improves system performance by establishing clear feedback loops and oversight. 

AI technology can certainly create efficiency and unlock new channels for growth – but without proper governance, it also opens the door to new risks and vulnerabilities that companies may not be equipped to handle all on their own. 

What Makes Up a Strong AI Governance Framework? 

1. Ethical components of AI governance
2. Regulatory compliance components of AI governance 
3. Risk management components of AI governance  
4. Types of AI governance structures  

Creating a reliable AI governance framework isn’t just a one-and-done checklist – it’s an ever-evolving system that needs to continuously develop alongside you as you implement new technology and adhere to new laws and regulations. 

Ethical components 

Ethics isn’t just a philosophical debate – it’s a compliance and strategic issue waiting to happen if ignored. As such, we need to ensure our AI governance structures start with core human values like fairness, transparency, and accountability. 

Practical areas to consider include: 

• Bias detection and mitigation. It’s important to regularly audit AI models for bias across gender, race, age, and other protected characteristics. 

• Ability to be explained. It’s essential to ensure that AI outputs can be explained in plain language. For example, if an AI-powered system denies a loan or flags a transaction, end users deserve to know why. 

• Human protocols. Automation is a powerful tool, but companies should be careful not to over-rely on this. Critical decisions should always involve options to review or override. 

Regulatory compliance components

Artificial intelligence platforms are only as reliable and trustworthy as the data they’re trained upon. AI governance structures should put data privacy and security front and center. 

Companies should consider: 

• Whether or not they must adhere to GDPR CCPA, or other data privacy regulations. 
• Whether they adhere to emerging AI global legislation, such as the EU AI Act. 
• Conducting data lineage tracking to ensure a proper understanding of where data comes from and how it is being utilized. 
• Internal controls that limit who can modify models, access training data, or deploy changes. 

Risk management components

A common mistake companies make when implementing AI is thinking they can build or purchase it, turn it on, and then let it run unmonitored in the background. Remember: Smart risk management means ongoing oversight. 

This means: 

• Conducting pre-deployment risk assessments to evaluate the potential impact and likelihood of harm before implementing AI. 
• Creating audit trails by documenting decisions made by AI systems, especially in high-stakes areas. 
• Using dashboards, alerts, and automated testing to monitor performance and flag errors. 

These internal controls mirror traditional SOX or IT audit and compliance protocols – and consultants with experience in these domains (like our team at Bridgepoint) can help extend them to AI. 

Types of AI governance structures  

Strong AI governance structures need a solid team of people behind them.  

Some models to consider include: 

• AI ethics committees that act as cross-functional teams to evaluate risk, guide development, and review use cases. 
• Policy frameworks: Written rules and procedures that guide everything from model development to vendor selection. 
• AI risk management frameworks, such as the NIST AI Risk Management Framework (NIST AI RMF). Learn more about NIST assessments here: NIST Cybersecurity Risk Assessments for Government Contractors & Subcontractors. 

AI Governance Challenges 

Even with the best intentions, building strong governance can be difficult to maneuver – especially for organizations who already operate with lean teams. 

Managing rapid technological advancements  

The pace of AI innovation is rapid. What’s cutting edge today might be obsolete in six months. As such, AI governance needs to be flexible and regularly updated. 

Navigating varying international regulations 

Different regions have different standards. If your company operates across borders, you’ll need artificial intelligence governance policies that quickly adapt to disparate regulatory environments. 

Striking a balance between progress and protection 

Too much governance can stall innovation. But with too little, risk reigns supreme. Strong AI governance requires a balance between progress and protection – and that’s where an outside perspective can make all the difference. 

Which Regulations Lay the Groundwork for AI Governance? 

• EU AI Act: Classifies AI systems by risk level and imposes strict requirements for high-risk applications (for example, hiring, credit scoring, etc.). 
• U.S. SR 11-7: Originally for banking, this guidance on model risk management is increasingly applied to AI tools that impact finance and compliance. 
• Canada’s Directive on Automated Decision-Making: Sets rules for federal agencies using algorithms, including mandatory impact assessments. 
• Other global, state, and local regulations 

Final Thoughts on AI Governance 

Establishing an AI governance framework from scratch can be challenging – especially if your internal teams are already juggling audits, reporting deadlines, and strategic initiatives. 

But our experienced team at Bridgepoint Consulting is here to help. Our finance, technology, and risk experts have walked this road before, and we bring that real-world experience to every engagement. 

We help organizations: 

• Develop custom governance frameworks with the right balance of control and flexibility.
• Coordinate across departments to align stakeholders and workflows.
• Conduct risk assessments and prepare for regulatory review or internal audits.
• Deploy tools for automated monitoring, alerts, and dashboards.
• Train staff on AI ethics and decision oversight.
• Keep frameworks current with evolving tech and compliance standards.