The Most Common AI “Risk Factor” Categories
With over 70% of S&P 500 companies reporting AI-related risk factors in their SEC disclosures, it is crucial to consider the types of risk factors pertinent to your business. Tailoring these considerations to your specific circumstances is essential for effective risk management.
If your organization utilizes AI in critical operations such as product development, customer service, or analytics, explicitly linking these risks back to your business model and financial condition is vital. Moreover, if external AI tools are employed, factors such as vendor risk, contractual safeguards, and oversight must be integrated into your risk management framework.
In some cases, merely listing these risks is insufficient. Companies should also consider discussing how they are managing or mitigating AI risks to enhance transparency and improve the quality of disclosures.
1. Cybersecurity / Data Privacy / IT Risk
Example: “The integration of AI models and large data sets heightens our exposure to cybersecurity attacks, data breaches or misuse of data.”
Why it matters: AI systems often depend on extensive data, intricate models, and substantial IT infrastructure, leading to multiple entry points for potential threats.
Key issues: data integrity, unauthorized access, adversarial attacks on models, and regulatory obligations around data.
2. Regulatory / Legal / Compliance Risk
Example: “Emerging regulatory frameworks for AI (domestic and global) may impose additional compliance burdens or expose us to liability if our AI-driven products/services fail to comply.”
Why it matters: The rapid evolution of AI technology often outpaces legal frameworks, which can lead to significant risks if a company’s AI practices are non-compliant.
Key issues: privacy laws, algorithmic bias, discrimination, financial regulation, and governance of AI models.
3. Operational / Implementation Risk
Example: “Our ability to integrate AI into our operations, product development or internal processes may not succeed, which could result in delays, increased costs or failures.”
Why it matters: Successful execution is crucial even with promising technology. Potential pitfalls include poor data quality, model misspecification, and lack of skilled personnel.
Key issues: model training and validation failures, scalability, alignment with business processes, and cost overruns.
4. Competitive / Innovation Risk
Example: “If our competitors are able to deploy AI technologies more effectively or faster, we may lose competitive advantage or market share.”
Why it matters: AI can serve as a differentiator in the market; falling behind can have serious consequences.
Key issues: speed of change, disruptive entrants, cost of staying current, and loss of customer sentiment.
5. Ethical / Reputation Risk
Example: “If our AI systems produce biased or unfair outcomes (or are perceived to do so), our reputation could be harmed, or we may face litigation or regulatory scrutiny.”
Why it matters: The reputational damage from perceived AI misuse can have significant business impacts, even if there are no direct legal repercussions.
Key issues: bias, discrimination, transparency, public perception of AI misuse, and social responsibility.
6. Third-Party / Vendor Risk
Example: “We rely on third-party vendors/suppliers for AI components, and if they fail or the vendor’s model is flawed, this may have adverse effects.”
Why it matters: Many organizations do not develop their entire AI stack in-house, relying instead on external models and services, which introduces additional risk layers.
Key issues: vendor management, outsourcing of key AI functions, dependency risk, and data sharing with vendors.
7. Technical Limitations / Model Risk
Example: “AI systems may not perform as expected, may produce inaccurate or inappropriate outputs, or may fail when new/unanticipated conditions arise.”
Why it matters: Even sophisticated algorithms have their limits; unexpected inputs and lack of interpretability can lead to harmful outcomes.
Key issues: model bias, overfitting, black box governance, and validation and monitoring of AI performance.
