Pandamatics is building an AI-driven insurtech platform for holistically assessing the cyber risk of an organization.
Pandamatics aims to deploy its underwriting expertise alongside artificial intelligence technologies to assess the cyber risk profile of organizations, providing specific recommendations for risk reduction.
By focusing on analysis of information provided by technological solutions, Pandamatics will leverage snapshots of forensics and security control configuration data to determine where cyberattacks are likely to infiltrate business perimeters by market and industry segments. This data informs Pandamatics’ methodology, enabling pattern recognition of factors that contribute ultimately to a breach.
As financial damage predominantly occurs once infiltration beyond the endpoint level is achieved, Pandamatics logically focuses its analysis and technological development on assessing risk at this level in order to help clients limit losses.
We endeavour to better assess and ultimately price cyber risk whilst raising the cyber risk resiliency of our insureds.
We believe cyber risk should be
accurately measured and fairly priced.
Cyber insurance products often focus on a one-dimensional approach to cyber risk, namely, the organization's defensive cybersecurity perimeter — oftentimes bypassing or missing critical cyber risk data residing at the endpoint level, or the “last line of defense” before the attackers reach mission critical assets or data.
Many insurers also tend to approach cyber risk in the same manner that they would review traditional risks, without understanding the underlying cyber risk exposure. These approaches to measuring risk often lead to sub-optimal pricing for insureds and inaccurate understanding of total exposures for underwriters.
However, by assessing the specific range of factors or security settings that most often exist prior to a breach, both insurance underwriters and insured clients can better understand their vulnerabilities and take the appropriate actions to reduce the probability of loss by mitigating potential damages.