Due to its growing application in many industries, artificial intelligence is helping improve processes and decision-making faster and more effectively. However, if AI systems aren’t designed, implemented or managed appropriately, they can also introduce new risks including risks that pertain to cybersecurity, ethics, compliance, and dependability. As such, AI risk assessment has become critical; this form of risk evaluation enables organizations to better identify, assess, and manage risk so that AI systems remain trustworthy, transparent and compliant over the course of their full lives.
This blog serves as your comprehensive guide to conducting an AI risk assessment, covering risks, frameworks, actionable steps, and best practices.
By the time you’re done reading, you will understand the importance of AI risk assessments and, most importantly, how to perform them effectively while keeping your organization protected from threats.
“Read our guide to The Impact of Artificial Intelligence in Cybersecurity to understand how AI is reshaping security strategies.“
What Are AI Risks?
Understanding the types of risks you encounter is the beginning of artificial intelligence risk assessments. Among the fields AI systems face risk in broad strokes are:
- Safety Risks: Unforeseen agentic AI system behaviors that perform multi-step tasks with minimal human interaction as well as algorithm malfunctions.
- Security Risks: Attacks against adversarial AI security include prompt injections, or model poisoning using altered training data.
- Legal and Ethical Risks: Legal and ethical dangers comprise violations of developing privacy regulations or discriminatory results from biased datasets.
- Performance Risks: Model drift, where an artificial intelligence’s precision declines over time as real-world data evolves.
- Sustainability Risks: The carbon footprint of major LLM training and inference.
“Security threats are a major concern in AI. Read our guide to AI Threat Intelligence in Cybersecurity to explore how AI-driven security solutions can help mitigate risks.“
Some real-world examples of AI risks include:
Legal Risk: In 2025, a federal judge in Mobley v. Workday, Inc. conditionally certified a national class action following claims that Workday’s artificial intelligence screening technologies resulted in disparate impacts against applicants over the age 40.
Operational Risk: In 2024, a Canadian tribunal held Air Canada responsible when its chatbot provided inaccurate refund recommendations, therefore proving that AI-generated responses have contractual responsibility.
Source: https://qualysec.com/ai-risk-assessment/
