AI Risk Worries Insurers & Businesses Alike
As companies adopt AI, many insurance firms are explicitly excluding AI risks, while others are forging ahead to create the right framework. What risks can firms reasonably manage?
Adoption of new technologies can alter an organisation’s attack surface, requiring security controls, testing, and risk management to change.
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Background for this topic.
Adoption is the extent to which people and organizations begin using a security technology, control, policy, or practice and incorporate it into routine work. In cybersecurity, adoption is more than purchasing or deploying a capability: it includes correct configuration, user participation, and continued use. Examples include enabling multifactor authentication, applying security patches, using secure coding practices, and collecting logs from systems that require monitoring.
Adoption matters because uneven or incomplete use leaves exploitable gaps. A partially deployed authentication control may protect some accounts while others remain exposed; delayed patch adoption can leave known vulnerabilities available to attackers; and missing or poorly configured logging can limit detection and investigation. Practitioners therefore assess coverage, exceptions, configuration quality, and whether controls operate as intended. Training, usable workflows, staged rollout, and measured policy compliance can improve adoption without encouraging insecure workarounds or unnecessary collection of personal data.
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As companies adopt AI, many insurance firms are explicitly excluding AI risks, while others are forging ahead to create the right framework. What risks can firms reasonably manage?
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