Autonomous vehicles (AVs) are set to revolutionize modern transportation, bringing in enhanced driving experience, safety, and efficiency.
However, it also brings in a complex set of challenges including cyber risk due to connected technology and systems. Traditional vehicles are relatively isolated and typically exposed only to physical attacks. But the vulnerability of AVs is far higher due to their technology architecture, the devices and systems they are connected to, making them more susceptible to cyberattacks. These attacks can be remotely initiated through connected components such as mobile devices, Wi-Fi, and Bluetooth. Since AVs rely heavily on wireless communication and external data inputs, the cyberthreat landscape is considerably larger (see Figure 1), exposing the vehicle, the occupants, and the wider transportation infrastructure to cyber risks.
For insurers, AVs represent a new paradigm as they shift liability away from drivers, challenging traditional risk models, and changing the way property and casualty (P&C) insurers approach risk assessment and pricing. In addition, AVs add a new dimension in the form of cyber risk that insurers will need to consider while bundling insurance covers, developing new products, and determining liability during claim settlement. As AV technology continues to evolve, insurers must constantly adapt by incorporating real-time data usage, AI-driven liability assessments, and robust cyber underwriting and claims practices. This shift will necessitate redefining processes to handle digital complexity, impacting the entire insurance value chain spanning underwriting, policy administration, claims management, and regulatory compliance to ensure competitiveness in the increasingly technology driven insurance sector.
Underwriting risks in the AV space differ sharply from traditional auto insurance.
The technology in AVs spans sensors, AI, and complex software systems, which introduce new and evolving risks that are harder to quantify. Traditional underwriting models, which rely heavily on historical claims data and human error, are not equipped to assess risk exposures when decisions are made by machines and failures can be systemic.
The absence of clear liability frameworks, possibility of large-scale losses from a single failure, and the involvement of multiple parties across hardware, software, and data layers further compound the situation. In such scenarios, assigning fault becomes difficult and the risk to the insurer increases. Let us examine the key underwriting risks associated with AVs and how they may play out in real-world situations.
Multi-party liability: In AV accidents, the fault may not lie with a single party and liability can extend to original equipment manufacturers (OEMs), software companies, sensor providers, or other third-party vendors. This complexity is amplified when accidents are triggered by cyberattacks, such as remote hijacking or system spoofing. This may stem from vulnerability in one component but will affect the entire AV system. This creates ambiguity, since traditional underwriting models are built around driver-centric liability and not equipped to assess distributed fault scenarios.
System malfunction: AVs rely heavily on connectivity and systems such as LIDAR, radar, and AI based decision-making. A fault in these systems, whether due to technical failure or a cyberattack, can impair vehicle functions and cause accidents without any human involvement. Cyber induced malfunctions can further complicate fault attribution. Traditional models struggle to quantify risks from system failures. Limited data combined with technical complexity and potential failure modes such as sensor failure, software glitches, loss of GPS connectivity or manipulation, and hardware malfunction make it harder to anticipate risks.
Cyber vulnerabilities: AVs are connected to external networks for updates, navigation, and communication, exposing them to cyberattacks, ranging from ransomware and remote hijacking. The challenge is to assess exposure from both frequency and severity standpoints.
Data security: AVs rely on highly connected data platforms to function and continuously collect and store data, which can also be sensitive in nature. This exposes AVs to data breaches with the potential for regulatory penalties and class action litigation compounded by ambiguity of liability.
Clearly, resolving challenges in underwriting and claims will require AV insurers to move beyond traditional models and embrace AI.
Insurers must adopt generative AI (GenAI) and AI agents to design a scalable and operationally viable solution encompassing dynamic profiling and automated data analysis. GenAI has the potential to transform processes across the insurance value chain from underwriting to claims management. Furthermore, AI agents too are making inroads into the insurance sector, especially in claims processing.
In the context of AVs, insurers have to contend with the increasing complexities of managing cyber risks that arise from interconnected digital systems and networks. Cyber insurance for autonomous vehicles brings a different level of complexity. Insurers not only deal with evolving risks but also large volumes of data, regulatory pressure, and multi-party liability. AI-backed insurance workflows can help insurers navigate ambiguity, enhance decision-making, and build resilient models for managing AV-related risks.
To address the deficiencies in traditional models and efficiently manage cyber risks in AVs, insurers must embrace a combination of composite AI technologies and intelligent workflows. Figure 2 illustrates how GenAI and AI agents work together to address cyber risk across the four critical areas of multi-party liability, system malfunction, cyber vulnerabilities, and data security.
The adoption of composite AI will deliver crucial benefits to insurers by enabling them to assess emerging risks with greater accuracy, reduce manual intervention, and accelerate decision-making. Beyond automation, these technologies enable insurers to act proactively by creating a foundation to help insurers build resilience and adapt to a perpetually evolving AV environment.
Delivering seamless insurance services in the AV space requires tight integration between customer-facing, decision-making, and operational functions.
This will demand collaboration through real-time data exchange, AI-driven insights, and workflow automation across the front-, middle-, and back-office (see Figure 3).
Autonomous vehicles will become deeply integrated into connected ecosystems. As a result, contact centers will transform into intelligent risk communication hubs, playing a proactive rather than a reactive role in detecting cyber vulnerabilities before issues escalate. Contact centers will help in orchestrating collaboration between insurers, manufacturers, and policyholders, playing a vital role in the safety and resilience of connected mobility.
The middle-office serves as the analytical and governance layer. While AI tools may assist in surfacing insights, the middle-office ensures that outputs align with the policy and regulatory requirements. GenAI and AI agents act as cross-layer enablers, enhancing customer engagement, supporting dynamic underwriting, and streamlining claims and compliance, creating an agile, intelligent ecosystem for effectively managing AV risks.
As cyber insurance in the AV space matures, operational roles across the value chain will evolve with transactional support functions becoming strategic enablers, providing scale and flexibility. To accommodate this shift, insurers must expand the scope of their back-office function, moving from routine, simple tasks to managing complex activities. As AI and GenAI adoption matures, underwriters and adjusters will shift from using AI for routine tasks to functions demanding human judgment.
Insurers are increasingly focusing on achieving strategic goals such as cost optimization, scalability, and market expansion. With back-office modernization efforts accelerating, insurers must reevaluate core functions such as underwriting, claims, and customer engagement through a strategic transformation lens, identifying opportunities for simplification and automation to drive operational efficiency. As the AV and connected mobility market evolves, demand for real-time cyber risk assessment, proactive engagement models, and operational resilience will increase, putting pressure on insurers to continuously adapt to the changing risk landscape.
One of the most significant disruptions today in the insurance industry is the rise of AVs.
As cyber risks around (AVs) evolve, so will the way insurers underwrite, manage claims, and engage with customers. GenAI and AI agents are only part of the story. What will matter just as much is how carriers rewire their operations to handle cyber complexity at scale. The question is not just about automation, it is about infusing the readiness and adaptability to nimbly respond to perpetual change in a landscape characterized by sophisticated fraudsters adept at using new technologies to launch attacks—insurers that can blend AI fluency with execution depth will play a key role here.
However, this will not be easy, insurers must consider partnering with an IT consultant with the requisite domain expertise and implementation experience for hassle-free execution and integration. Now is the time to act—P&C insurers must embrace AI to reimagine business processes and usher in innovation, positioning themselves for sustained growth and resilience in the ever-evolving AV landscape.