The AI hype is real—and in many ways, entirely justified. No technology has transformed our economy, the way we live, and the way we conduct business as rapidly as AI. While everyone talks about the opportunities, it seems as though hardly anyone is consistently addressing the risks—presumably not because they are unimportant, but because they are uncomfortable, seem difficult to grasp, or are simply supposed to be dealt with “later” in day-to-day project work. At the latest when AI scales up productively, this will come back to haunt us.
This is precisely where effective, AI-integrated risk management begins: AI risks do not belong as a footnote in a tech project, but rather within the company-wide risk and control system. This means identifying risks, assessing them, deriving measures, defining responsibilities, and—crucially—consciously recognizing, accepting, or transferring residual risk. Prevention and technology form the foundation. But for scenarios that threaten the company’s very existence, a financial safety net is also required. Insurance is part of risk management: it provides protection where significant damage can occur despite robust controls.
Two-tier protection: technical and financial
After all, AI rarely fails in a dramatic way. There’s no crash, no black screen. AI usually fails quite quietly: a prediction is “only” slightly off, a bias goes unnoticed, a model drifts after the next update, or a cleverly worded prompt causes the system to circumvent rules. In practice, these deviations are often the most costly—because they’re detected too late.
AI requires safeguards on two levels: technical and financial. Robust IT architecture, security by design, testing, monitoring, and sound product design are essential. But they cannot replace a strategy for scenarios that threaten the company’s very existence. Those who use and develop AI responsibly ask not only, “How quickly can we go live?” but also, “What happens if our AI makes mistakes—and who bears the risk then?”
AI risks are different from traditional software risks
Companies that use or develop AI are taking on new risks that are not yet explicitly covered by many existing insurance policies. This is not merely an “insurance problem.” It is an innovation problem. Because as long as companies do not know whether an AI-related loss is insurable (or whether coverage gaps will emerge in the event of a claim), projects are slowed down, budgets are held back, or risks are unconsciously shifted within the organization. The good news: Insurance today can be much more than just “protection in the event of a loss.” When approached correctly, it becomes an innovation enabler.
When it comes to security, many companies start with the idea that “AI is just software, after all.” That’s true—and yet it isn’t. AI systems are often:
- probabilistic rather than deterministic (not a fixed "if-then" relationship, but probabilities),
- dynamic (models change through retraining, fine-tuning, updates, and new data),
- depending on data and model supply chains (foundation models, APIs, open-source components),
- difficult to test and explain (black-box effects, complex error patterns).
Regulatory requirements are becoming a financial risk – cybersecurity is now mandatory
When it comes to AI, the line between a problem and a financial risk is increasingly determined not only by technical factors but also by regulatory ones. The EU AI Act makes it clear, particularly for high-risk AI, that companies must demonstrate risk management throughout the entire lifecycle—including requirements for accuracy, robustness, and cybersecurity.
The risk for companies lies not only in penalties, but also in pressure from audits and corrective actions, product recalls, contractual consequences, and reputational damage if documentation, security measures, and operational controls are not robust. It is important to make this distinction: Compliance itself cannot be “insured away”—it must be implemented both technically and organizationally. However, the consequential costs can often be insured if a security or compliance error results in a loss event: e.g., defense/legal costs, incident response, restoration and additional costs, or third-party liability claims. Prerequisite: AI risks are explicitly described in the policy and covered accordingly.
Why Standard Policies Often Aren't Enough
Many insurance solutions on the market do not explicitly cover AI risks—or the risk analysis is not thorough enough due to a lack of technical understanding. Often, one of three things happens:
- AI is “silently” covered (silent AI): It sounds good, but in the event of a claim, it quickly leads to disputes because the terms, triggers, and exclusions are not specifically formulated for AI.
- The policy is “silent” —but not in your favor: Cyber insurance often provides good coverage for security incidents, but it does not necessarily cover pure model failure or contractual performance guarantees. At the same time, new approaches are emerging on the market with innovative solutions, such as AI warranty insurance.
- New exclusions are emerging: Globally, we are seeing an increase in AI-related exclusions and greater fragmentation of coverage—driven by uncertainty, definitional issues, and potentially systemic loss events.
In short: If AI risks are not deliberately integrated into the insurance architecture, there is often no clear answer to the most important question in the event of a claim: “Is this covered?” Risk-appropriate coverage is of paramount importance—especially in a market that is changing as rapidly as AI.
About protectifAI: Understand. Negotiate. Insure.
At protectifAI, there are no “off-the-shelf” solutions. We start with a joint risk analysis: a collaborative review of AI use cases, architecture, data and model dependencies, SLAs, contracts, and realistic loss scenarios—followed by the translation of these technical realities into clear, robust coverage logic. This is necessary because many risk carriers are still unable to adequately assess the new digital and AI-specific exposures today: terminology is inconsistent, loss patterns are new, and the impacts are often systemic—and without clear classification, coverage and exclusions remain vague. It is precisely at this intersection that protectifAI brings both worlds together: The company makes risks understandable, negotiable, and structurable in a way that yields tailor-made solutions. This enables innovation while simultaneously mitigating losses that threaten a company’s very existence.
AI Risk Assessment: 7 Questions Every Company Should Be Able to Answer
Anyone who develops or uses AI (even "just" internally) should ask themselves these questions at the outset:
- Where is AI used? (Products, internal processes, shadow AI, tools, plug-ins, agents)
- Which outputs are business-critical? (Decisions, customer communication, pricing, compliance, safety)
- What commitments are included in contracts? (SLA, warranty, "AI-powered" promises, liability assumptions)
- What AI dependencies are involved? (Model providers, APIs, open source, data sources, RAG corpora)
- What are the potential attack vectors and vulnerabilities? (e.g., prompt injection, data exfiltration, supply chain)
- What is the worst-case scenario in terms of cost and time? (Revenue, additional costs, project suspension, damage to reputation)
- Is the risk explicitly defined in the policy—or is it “implicit”? (Definitions, triggers, exclusions, sublimits)
Those who can answer these questions make risk negotiable—from a technical, contractual, and underwriting perspective.
Bottom line: The AI safety net isn't just a nice-to-have
AI is changing everything—including how we manage risk. When AI becomes a driver of growth, risk management isn’t the end of innovation; it’s its safety net. Not every risk needs to be insured. But existential risks should be covered. Or as we put it at protectifAI: “Innovation, without fear.”
About the Author
Sarah Günther is the Managing Director of protectifAI (a member of the cooperative since November 2025) and a Certified Risk Manager (CRM) under ISO 31000. She combines technical expertise with in-depth insurance knowledge and sees herself as a bridge between innovation and risk carriers. Her professional qualifications include, among others, the certificate program in Cyber Insurance Management at TH Köln and the Fraunhofer personal certification in the field of Distributed Ledger Technology / Blockchain.