At-Bay

At-Bay operates in the specialty cyber insurance market, where underwriting accuracy depends on understanding a policyholder's real-time internet exposure — open ports, misconfigured services, unpatched software, and shadow IT. Traditional underwriting workflows relied on static questionnaires and point-in-time assessments that quickly became outdated, leaving insurers blind to evolving attack surfaces. For a cyber insurer writing policies at scale, slow or inaccurate risk assessment translates directly into mispriced premiums and adverse loss ratios. The inability to continuously monitor insured organizations meant At-Bay could not maintain the data-driven underwriting discipline the cyber insurance line demands.

At-Bay embedded Censys internet intelligence into its core underwriting technology stack, replacing manual or incomplete risk signals with continuous, automated scanning of policyholders' internet-facing assets. Censys was selected over competing attack surface management tools specifically for its scanning reliability and asset coverage accuracy — qualities that directly affect underwriting model inputs. The integration feeds real-time exposure data into At-Bay's predictive ML models, which evaluate risk signals across each insured organization's external footprint. This architecture supports recurring, automated scans rather than one-time assessments, meaning the underwriting engine receives fresh data throughout the policy lifecycle and can flag material risk changes as they emerge.

The integration reduced At-Bay's underwriting decision time to under 2 minutes — a threshold that redefines what automated cyber insurance processing looks like at the carrier level. Beyond speed, the continuous scanning capability means risk assessments remain current rather than reflecting a moment frozen at application time.

• Decision time: Under 2 minutes per underwriting determination
• Coverage: Continuous, recurring scans of insured organizations' internet-exposed assets
• Process change: Automated risk assessment replaced manual, questionnaire-driven workflows

The result is a repeatable, scalable underwriting process that positions At-Bay to grow its book without a proportional increase in underwriting headcount.

• Data quality is the underwriting model. In automated cyber underwriting, the accuracy of your internet intelligence provider directly determines policy pricing quality — vendor selection is a core actuarial decision, not a procurement one.
• Recurring scans outperform point-in-time assessments. Cyber risk changes continuously; underwriting systems that only capture initial exposure miss material changes mid-policy.
• Speed and accuracy are not trade-offs when the underlying data infrastructure is sound — sub-2-minute decisions become achievable without sacrificing risk rigor.
• Integration with existing ML pipelines matters. Feeding external intelligence into predictive models requires reliable, structured data outputs from the scanning layer.