Cyber Insurance Market Set for Dramatic Growth as AI Fuels 202% Spike in Phishing Attacks
The cyber insurance market is experiencing significant growth fueled by increased capacity and new market entrants, with buyers currently finding widespread premium reductions and expanded capacity, even as the risks of cyberattacks grows, according to analysis by Risk Placement Services.
The global cyber insurance market is projected to reach $16.3 billion by the end of 2025, according to Munich Re data, with premiums on track to double by 2030. The growth is occurring even as artificial intelligence is driving a 202% increase in phishing email attacks in the second half of 2024, the report noted. Munich Re projected that gross premiums will double by 2030 due to growing awareness of cyber risks and available insurance solutions.
This cyber insurance expansion has created notable market anomalies, with some renewals seeing clients receive higher limits for lower premiums.
“Does that always happen? Absolutely not. But the fact that it can happen demonstrates the anomalies that we’re seeing in the market,” said Steve Robinson, Risk Placement Services National Cyber Practice Leader. “A lot of carriers are trying to get creative so they don’t lose premium or market share by offering products that are of greater value.”
Meanwhile, the threat landscape is rapidly evolving with AI technology becoming a central factor. Hackers using generative AI tools have created a surge in sophisticated attacks, according to the report. In addition to the surge in with phishing email attacks, credential phishing attacks increasing 703% in the latter half of 2024, the report noted. Currently, 82.6% of phishing emails use AI technology in some form, and hackers can deploy phishing campaigns 40% faster thanks to generative AI and automation.
Business email compromise continues to lead in attack frequency, the RPS report said, while ransomware attacks have increased 126% this year, with North America accounting for 62% of global targets.
“There has also been more attention paid to third-party and vendor-related incidents, which is interesting because just about every industry sector has its top two or three vendors that specifically serve that industry,” Robinson said. “When that vendor encounters ransomware attacks or network outages of any kind, it has a downstream effect on their customers, highlighting the interconnectedness of modern digital ecosystems.”
New Vulnerabilities in Traditional Industries
Previously low-risk “blue collar” industries including construction, manufacturing and wholesale distribution are becoming attractive targets for cybercriminals, according to RPS. These sectors, historically considered lower hazard from a data breach perspective, often handle large wire transfers that make them vulnerable to social engineering attacks.
“A lot of these types of companies are handling large ACH wire transfers to pay vendors or suppliers in the course of their day-to-day business,” explained Zach Piern, RPS Area assistant vice president. “Ironically, some of the industry sectors that were previously thought of as lower hazard are the ones that are getting hit the hardest, especially on financial fraud.”
Strategic Implications for Market Participants
Cyber insurance carriers are responding by offering preventative resources including training, risk assessments, and security tool access, the report noted. Many provide free or discounted Endpoint Detection and Response services while adapting policy wording to address vendor risks and AI attack vectors.
However, coverage gaps remain, particularly around AI liability. While policies generally cover attacks perpetrated via AI, they don’t clearly address liability from biased AI models or data hallucinations created by organizations implementing AI systems, according to the report.
“Cyber policies are doing a good job of either implicitly covering or specifically stating that they’re covering attacks that are perpetrated via AI means,” Robinson says. “But what hasn’t really been clear, and still isn’t, is what happens when an organization creates or changes an AI model and that model later has bias in it or hallucinations in the data.”
View the full report here. &

