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Oracle is leveraging frontier AI models like Mythos to identify critical security vulnerabilities in its database code, a process that has significantly increased the volume and complexity of quarterly patches. To mitigate the risk of regression from these large updates, the company is integrating Real Application Testing to validate fixes against real-world workloads before deployment. This strategy highlights a shift where AI-driven discovery directly influences patch management cycles and necessitates robust testing protocols for enterprise database security.
Oracle connects AI-driven testing with a new approach to database security
Frontier AI models that uncover software flaws by the hundreds are forcing database providers into larger, faster patch cycles, and real application testing is becoming the safeguard that keeps those fixes from breaking production systems. The same AI wave is also undermining security rules written into applications. Oracle Corp. has responded on two fronts, hardening […] The post Oracle connects AI-driven testing with a new approach to database security appeared first on SiliconANGLE.
Frontier AI models that uncover software flaws by the hundreds are forcing database providers into larger, faster patch cycles, and real application testing is becoming the safeguard that keeps those fixes from breaking production systems. The same AI wave is also undermining security rules written into applications. Oracle Corp. has responded on two fronts, hardening its code against outside attackers while moving agent controls down to the data layer. The first effort began when…
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