How AI and LLMs are revolutionizing cyber insurance

Predictive attack paths play a crucial role in improving cyber insurance models and reducing risks by providing proactive rather than reactive cyber defense. Traditional insurance models that socialize risk and cover isolated incidents don't work for cyber insurance. Advanced AI and large language model (LLM) technologies help identify and anticipate potential routes attackers might take to exploit vulnerabilities within an organization's infrastructure.

Predictive attack paths provide the real-time insights needed to reduce risk and the probability of an attack. Reducing risk helps keep premiums affordable and policies feasible for a broader base of clients. They also bring greater stability to cyber insurers by reducing the potential of a widespread risk of simultaneous, large-scale cyber events.

In summary, predictive attack paths enable cyber insurers to have a more accurate understanding of an organization's risk profile, allowing them to offer tailored coverage and pricing. This empowers organizations to make informed decisions and adequately protect themselves against potential cyber incidents. Furthermore, predictive attack paths help insurers provide proactive threat intelligence to their policyholders, fostering a collaborative relationship and encouraging a culture of cyber risk mitigation.

Cyber insurers are increasingly relying on AI to streamline their processes and reduce costs associated with risk assessments and claims processing. AI is being used to improve real-time risk assessments, underwriting, claims processing, and resilience planning.

For real-time risk assessments, AI is being utilized to quantify risk in ways that were not possible before, enabling insurers to make smarter underwriting decisions. AI-based systems are reducing the time and costs of real-time risk assessments, which can cost between $10,000 to $50,000 per assessment and take between four to six weeks to complete.

In underwriting, AI is helping to streamline the process, reducing the typical workflow from weeks to days and improving efficiency by up to 70%. Traditional claims processing costs an insurer an average of $15,000 per claim due to manual handling, which can take up to six months. AI-based systems are cutting claim processing times by over 80%.

Companies like At-Bay, Corvus Insurance, Cowbell Cyber, Paladin Cyber, and Resilience Insurance are providing AI-based solutions to help streamline cyber insurance4. CrowdStrike's Falcon for Insurability program is another example of how AI and LLMs are revolutionizing cyber insurance by giving cyber insurers the flexibility they need to provide their clients with AI-native cyber protection using the CrowdStrike Falcon cybersecurity platform at preferred rates.

AI is also being used to identify and anticipate potential routes attackers might take to exploit vulnerabilities within an organization's infrastructure, providing proactive rather than reactive cyber defense. This reduces risk and the probability of an attack, helping to keep premiums affordable and policies feasible for a broader base of clients.

In summary, AI is helping cyber insurers to better understand and manage risk, improve efficiency, and reduce costs, making cyber insurance more accessible to a wider range of organizations.