Model Behavior Risk
What is Model Behavior Risk?
Model Behavior Risk is the risk that an AI system will behave in ways you did not expect, did not intend, or cannot fully control.
This includes:
- Wrong answers
- Harmful or biased outputs
- Hallucinations
- Unpredictable decisions
- Unsafe behavior under certain inputs
- Performance changes over time (drift)
NIST explicitly frames this as part of the broader need to manage risks "associated with artificial intelligence (AI)" and ensure systems behave in trustworthy ways.
Midgard and EY also emphasize that model behavior is one of the hardest risks to identify and requires continuous testing and governance.
What does Model Behavior Risk Do?
Model Behavior Risk shows up when:
- The model gives incorrect or misleading answers
- The model behaves differently in production than in testing
- The model changes over time (drift)
- The model produces biased or unfair outcomes
- The model is overly confident in wrong answers
- The model fails in edge cases
- The model is manipulated by malicious prompts or inputs
This why NIST and IBM stress the need for structured AI risk management frameworks to detect and mitigate these issues.
Why it Matters for Executives?
Model Behavior Risk can lead to:
- Regulatory violations
- Legal exposure
- Reputational damage to the organization
- Biased or discriminatory decisions
- Operational failures
- Incorrect business decisions
- Loss of customer and stakeholder trust
It is one of the most visible and most dangerous forms of AI risk.
Transparency Disclosure
All images and videos on this site were AI generated and/or are Getty licensed images that may have been AI generated. AI was also used to edit the content descriptions.
