1. Identifying risks: This means figuring out where AI might cause problems. Risks can come from many places, such as: 

• Data quality: AI is only as good as the data it is pulling from, so having well-structured, up-to-date data is crucial.  
• Data bias: If the data used to train AI is unfair or incomplete, the AI might make biased decisions. 
• Model drift: Over time, AI models can become less accurate as they process new data that doesn’t match the data they were trained on. 
• Ethical issues: AI can sometimes make decisions that aren’t fair, transparent, or ethical. 

2. Implementing controls: Once the risks are identified, companies need to put safeguards in place. These controls can include: 

• Bias detection algorithms: Tools that check if AI systems are treating people fairly. 
• Data privacy protocols: Rules that protect sensitive information from being exposed. 
• Compliance checks: Making sure AI systems follow all laws and regulations. 

3. Monitoring outcomes: AI isn’t a static technology. Businesses need to regularly check how AI is performing to catch issues early. Continuous monitoring helps to: 

• Spot problems before they become serious. 
• Make improvements as needed. 
• Ensure the AI stays reliable and accurate over time. 

1. Financial loss: AI systems are designed to make decisions quickly, but if they’re based on faulty data or flawed algorithms, the financial fallout can be huge. For example, AI used in investment strategies might make poor trading decisions, while in supply chain management, an AI system that misjudges inventory needs could cause product shortages or overstocking. 
2. Legal penalties: Many industries are heavily regulated, and AI systems that don’t comply with laws can get companies into serious trouble. Violating data privacy laws, like the GDPR in Europe, can result in hefty fines. Discrimination laws also apply to AI; if an algorithm is found to be biased, companies could face lawsuits or government penalties. 
3. Reputational damage: Trust is hard to earn and easy to lose. A single incident where AI provides incorrect information or makes a biased decision can damage a company’s reputation overnight.  
4. Operational disruptions: AI systems are often embedded in critical business operations. If they fail unexpectedly, it can bring entire workflows to a halt. Imagine an AI system managing an airline’s scheduling or customer service —sudden disruptions could lead to widespread problems and financial losses.  

NIST AI risk management framework (AI RMF) 

Developed by the National Institute of Standards and Technology (NIST) in the United States, this framework is designed to help organizations identify and manage AI risks effectively.  

It focuses on four key areas: 

• Governance: Setting clear roles and responsibilities for managing AI. This includes defining who is accountable for AI systems, how decisions are made, and what ethical standards should be followed. 
• Mapping: Identifying potential risks throughout the entire AI lifecycle, from data collection to model deployment. This helps organizations understand where things could go wrong. 
• Measuring: Assessing the severity and likelihood of risks. By evaluating how big a risk is and how likely it is to happen, companies can prioritize which risks to address first. 
• Managing: Implementing controls to reduce or eliminate identified risks. This could involve technical solutions like bias detection tools or policy changes to improve oversight. 

The NIST is a voluntary framework that emphasizes flexibility, allowing organizations to adapt its principles to fit their specific needs, regardless of size or industry.

The EU AI act

The European Union AI Act is one of the most comprehensive AI regulations in the world. The AI Act entered into force in August 2024 and will be fully applicable in August 2026 for EU Member States. 

It classifies AI systems based on their level of risk: 

• Minimal risk: Systems like spam filters that pose little threat to safety or rights. 
• Limited risk: AI that requires transparency but doesn’t have a major impact on individuals. 
• High risk: Systems used in critical areas like healthcare, law enforcement, or finance. These face strict compliance requirements to ensure safety and fairness. 
• Unacceptable risk: AI that is considered too dangerous, such as systems that manipulate behavior in harmful ways. These are banned altogether. 

The EU AI Act emphasizes transparency, accountability, and human oversight. Its goal is to ensure that AI technologies are safe, ethical, and respect fundamental human rights. 

ISO/IEC standards for AI management

The International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) have developed global standards to help organizations manage AI risks.  

These standards focus on: 

• Data quality: Ensuring the data used to train AI is accurate, complete, and unbiased. 
• Security: Protecting AI systems from cyber threats and data breaches. 
• System robustness: Making sure AI performs reliably under different conditions and can recover from failures. 

ISO/IEC standards are used worldwide and help organizations create consistent, high-quality AI systems. They provide a common language for discussing AI risks and best practices.