Oracle将AI驱动的测试与新的数据库安全方法连接起来。
边疆AI模型揭示了数百人的软件缺陷，迫使数据库提供者进入更大，更快的补丁循环，真正的应用测试正在成为防止这些修复打破生产系统的保障。 同样的AI波也正在破坏写成应用程序的安全规则。
Oracle Corp在两条战线上响应，在将agent控制器移动到数据层的同时，强化了对外部攻击者的代码。 根据Oracle的Oracle数据库技术执行副总裁Juan Loaiza(图片d)的说法，公司在自己的软件上转换了早期接入前沿模型，第一次努力开始。
“早在3月，我们便能及早接触神话模型，" 它出现了一些非常严重的安全问题。 因此，我们称，“嘿，这是一个代码红色时刻。 房子着火了。”
在CUBE, SiliconANGLE Media的直播工作室独家播出时，他们讨论了AI发现的弱点，AI生成的代码和AI agents为何要求企业将安全视为数据问题。 (* 下文披露。 )
真正的应用程序测试遇到更大、更快的补丁
大部分Oracle对AI发现的弱点的反应都是降落在它的常规季度补丁上，结果这些补丁变得更大，Loaiza解释说。 今年早些时候的季度安全更新涉及公司投资组合的1400多个弱点。 客户必须自动补丁并保持电流，但每个大补丁融资担心在不能失败的系统中出现回归。
“我们有名为Real Application Testing的东西，因为人们的应用非常复杂，他们管理着整个国家和整个工业。”
补丁手柄外袭者。 内部AI的使用是一个单独的威胁，因为应用程序传统上是强制推行最终用户安全，而AI现在写了很多这个代码或者直接到数据。 Oracle的答案是Deep Data Security，它于4月开始传输，并将一个经认证的用户身份作为加密的代号传递到数据库。 此后，该公司将这些授权控制扩展到远程数据库、湖屋数据和相关表格。
我所见的新世界是所有这些最终用户安全规则都必须执行，“AI agent必须获得你的身份。 它代表你行事。 如果无法看到数据
商业规则遵循同样的逻辑。 Loaiza指出，申请代码一直封锁超过客户限额的信用卡收费，但要求执行该规则的AI agent可能不遵守。 Oracle正在提供定义此类规则的宣示方式，同时提供SQL Assertions来检查通过SQL所做的修改是否有效。
“有信用限制，有各种不能违反的商业规则。 再说一遍，如果我们把这些都移到数据中，那么无论你如何访问数据，你使用什么机制，AI是如何使用的，它不能违反那些规则，"Loaiza 称。
SiliconANGLE的部分内容和CUBE对Oracle AI World的报导：
(*披露：CUBE是Oracle AI World的付费媒体合作伙伴。 无论是CUBE事件报道的发起人Oracle还是其他发起人，都没有对CUBE或SiliconANGLE上的内容进行编辑控制。 ).
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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 its code against outside attackers while moving agent controls down to the data layer. The first effort began when the company turned an early-access frontier model on its own software, according to Juan Loaiza (pictured), executive vice president of Oracle Database Technologies at Oracle.
“Back in March, we got early access to the Mythos model and we ran it against our code and it started finding all these security vulnerabilities,” Loaiza said. “It’s turning up some very serious security issues. And so we said, ‘Hey, this is a code red moment. The house is on fire.'”
Loaiza spoke with theCUBE Research’s Dave Vellante at Oracle AI World, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed why AI-discovered vulnerabilities, AI-generated code and AI agents require enterprises to treat security as a data problem. (* Disclosure below.)
Real application testing meets bigger, faster patches
Most of Oracle’s response to AI-discovered vulnerabilities is landing in its regular quarterly patches, which have grown much larger as a result, Loaiza explained. A quarterly security update earlier this year addressed more than 1,400 vulnerabilities across the company’s portfolio. Customers must automate patching and stay current, but every large patch raises fears of regression in systems that cannot fail.
“We have something called Real Application Testing where we can capture real workload and run it to make sure that your workload is not affected by these patches,” Loaiza said. “It’s not quite as simple as just apply because the applications that people run are extraordinarily complex. They run entire countries, entire industries.”
Patching handles outside attackers. Internal AI use is a separate threat, because applications have traditionally enforced end-user security, and AI now writes much of that code or goes straight to the data. Oracle’s answer is Deep Data Security, which began shipping in April and passes an authenticated user’s identity as an encrypted token through to the database. The company has since extended those authorization controls across remote databases, lakehouse data and related tables.
“The new world as I see it is all these end-user security rules have to be enforced where AI can’t bypass them, which means all the way down in the data,” Loaiza said. “The AI agent has to get your identity. It’s acting on behalf of you. It can’t leak the data if it can’t see the data.”
Business rules follow the same logic. Application code has always blocked a credit card charge above the customer’s limit, but an AI agent told to enforce that rule may not comply, Loaiza noted. Oracle is providing declarative ways to define such rules, along with SQL Assertions that check whether changes made through SQL are valid.
“There’s credit limits, there’s all sorts of things that are business rules that can’t be violated. And again, if we move those all the way down into the data, then no matter how you access the data, what mechanism you use, how AI is used, it cannot violate those rules,” Loaiza said.
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Oracle AI World:
(* Disclosure: TheCUBE is a paid media partner for Oracle AI World. Neither Oracle, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
Photo: SiliconANGLE
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