AI Risks Becoming More Evident—But Insuring Against Them Remains a Challenge
As artificial intelligence (AI) integrates deeper into industries like healthcare, finance, and transportation, its risks—ranging from data breaches to biased outputs—are becoming increasingly apparent. A recent article from Law.com titled “AI Risks Becoming More Evident—But Not How to Insure Against Them” highlights the growing concern that while AI’s potential for harm is clear, the insurance industry is struggling to keep pace with tailored solutions. Described as the “Wild, Wild West” by Butler University professor Thomas Faulconer, AI insurance is an emerging frontier fraught with complexity, leaving businesses vulnerable to uncovered liabilities. This article explores the risks, the state of AI insurance, and the challenges in safeguarding against this transformative technology.
The Rising Risks of AI
AI’s rapid adoption has brought a spectrum of risks to the forefront, as documented in recent reports:
- Data Security and Privacy: A Cybernews report flagged 970 AI-related security issues across 327 S&P 500 companies, with 158 tied to financial services and insurance. Data leakage, especially of sensitive customer information, is a top concern, with 35 documented cases in these sectors alone.
- Bias and Discrimination: Algorithmic bias can lead to unfair outcomes, such as skewed underwriting or pricing in insurance. Cybernews identified 22 bias-related cases in financial firms, risking reputational damage and legal challenges.
- Intellectual Property (IP) Infringement: AI models trained on copyrighted data can produce outputs that infringe on IP rights, potentially leading to lawsuits. This is a growing issue with generative AI, as noted by Deloitte Insights.
- Performance Failures and Hallucinations: AI systems can produce inaccurate or fabricated outputs, known as “hallucinations.” For example, a flawed AI diagnosis in healthcare could lead to misdiagnosis or even fatalities, as highlighted by Swiss Re.
- Silent AI Risks: Unseen liabilities, termed “Silent AI” by Armilla AI, arise when AI failures trigger claims under broad, non-AI-specific policies, exposing insurers to unanticipated losses.
Stanford University’s AI Index reported a 2,500% increase in AI incidents from 2012 to 2023, with 260 controversies in 2023 alone, underscoring the escalating risk landscape.
The State of AI Insurance
The insurance industry is beginning to respond, but the market for AI-specific coverage is nascent and fragmented:
- Emerging Products: Major reinsurers like Munich Re have offered AI insurance since 2018, targeting startups and developers with coverage for performance failures and IP risks. Insurtechs like Armilla AI provide warranties guaranteeing AI model performance, reducing financial exposure from underperformance.
- Market Potential: Deloitte Insights projects AI insurance could reach a $4.7 billion global market by 2032, growing at an 80% compound annual rate, mirroring the post-2009 cyber insurance boom.
- Existing Policy Gaps: Standard policies like Commercial General Liability (CGL), cyber, or professional liability (E&O) often exclude AI-specific risks or have narrow definitions that don’t fully cover issues like biased outputs or IP infringement. For instance, CGL policies may exclude software-related liabilities, leaving companies exposed.
However, the industry faces significant hurdles:
- Lack of Standardization: Unlike cyber insurance, which matured over decades, AI insurance lacks clear frameworks for assessing and pricing risks, leading to inconsistent coverage offerings.
- Underwriting Challenges: Quantifying AI risks is difficult due to their novelty and unpredictability. Underwriters struggle to evaluate risks like algorithmic bias or systemic failures in autonomous systems.
- Silent AI Exposure: Insurers may inadvertently cover AI-related claims under broad policies, leading to unexpected liabilities. Armilla AI emphasizes the need for explicit AI riders or exclusions to manage these risks.
The Regulatory and Practical Landscape
Regulatory frameworks, like the EU’s AI Act, which began enforcement in August 2024, aim to address AI risks but add complexity for insurers. The Act’s risk-based approach bans certain AI practices and imposes strict requirements on high-risk systems, increasing the demand for compliance-related coverage. However, regulators themselves are under-resourced, complicating enforcement and leaving businesses to navigate ambiguous compliance landscapes.
Businesses are advised to:
- Conduct Risk Assessments: Identify AI systems in use and their risk profiles, especially for high-stakes applications like hiring or medical diagnostics.
- Negotiate Tailored Policies: Work with insurers to add AI-specific riders or explore specialty products from firms like Armilla AI or Munich Re.
- Implement Governance: Adopt AI acceptable use policies and human-in-the-loop processes to mitigate risks like bias or data misuse, as recommended by AMBART LAW.
Critical Perspective
The insurance industry’s lag in addressing AI risks reflects a broader challenge: AI’s rapid evolution outpaces traditional risk management frameworks. While the projected $4.7 billion market suggests optimism, the lack of standardized underwriting and regulatory clarity risks creating a patchwork of coverage that may favor large corporations with legal resources over smaller firms. The “Wild, Wild West” analogy is apt—insurers are experimenting in uncharted territory, and early adopters face the highest uncertainty. Moreover, the focus on financial and IP risks may overshadow ethical concerns like bias, which could have broader societal impacts if left uninsured.
Conclusion
As AI risks become more evident, the insurance industry is playing catch-up, with innovative products emerging but no comprehensive solutions yet. Businesses face a precarious landscape where standard policies may not cover AI-specific liabilities, and regulators are still defining enforcement. Until the industry matures, companies must proactively assess their AI exposures, seek specialized coverage, and implement robust governance to mitigate risks. The path to insuring AI is as complex as the technology itself, but with collaboration between insurers, businesses, and regulators, a clearer framework may yet emerge.
Sources: Information drawn from Law.com, Deloitte Insights, Cybernews, Swiss Re, Armilla AI, and posts on X. Always verify with trusted sources, as X posts may contain unverified claims.