For CIOs, the management challenge is no longer whether to adopt AI, but how to govern its use without creating a larger attack surface. The articleโs core warning is that AI changes both the speed and the shape of cyber risk: attacks can be automated, but internal use of AI can also introduce new exposure through data, access and third-party models. That pushes the issue beyond security tooling into decision rights, policy enforcement and operating discipline.
The first management implication is clearer accountability. If AI is being embedded across business apps, someone must own approvals for model use, data classification, and exceptions for higher-risk use cases. CIOs and CISOs should test whether current controls answer practical questions such as: who can connect a model to sensitive datasets, who signs off on external model providers, and who can suspend a risky deployment when evidence changes?
The second implication is portfolio trade-off. Not every AI initiative deserves the same controls, but weak segmentation is dangerous. Leaders need a risk-tiered approach that distinguishes between low-risk productivity tools, customer-facing applications, and agentic use cases with elevated autonomy. That means funding should cover not only innovation, but also monitoring, identity, logging, and response automation. A useful next question is whether AI spend is being matched by security and governance spend at the same pace.
The third implication is operating model readiness. Manual response will not keep up with AI-enabled phishing, deepfakes and automated exploitation. CIOs should ask whether SOC processes, identity verification, and awareness training are designed for machine-speed threats, not just human-scale incidents. The practical test is simple: can the organisation detect, verify and interrupt an AI-assisted attack before business damage spreads across finance, identity and reputation?
When AI cuts both ways
The risk cuts both ways, Massey said. AI can be used by attackers to breach organizations, but the AI risk extends beyond external attacks. An organization’s own use of AI can open the door to new internal attack surfaces if it is poorly governed. That tension is now a central concern for CIOs and CISOs, said Rik Turner, chief analyst for Omdia’s cybersecurity team. According to Turner, as enterprises “rush to ‘AI-ify’ multiple apps,” embedding large language models (LLMs) and GenAI tools to drive business productivity, security leaders are grappling with how to drive adoption and get value from AI without increasing their attack surface in the process.Top three AI security challenges
The overall security risk from AI can be examined as a threefold problem for CIOs and CISOs, Turner said.-
The data security problem. CIOs need to ensure that training and inference data is clean and that AI models aren’t trained on sensitive or confidential information.
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The application security problem. CIOs need to understand the origin of open source models and third-party models that are embedded in organizational applications, including how those models are maintained and the risks they could pose.
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The access problem. Employee misuse of LLMs, especially around SaaS-delivered generative AI platforms such as ChatGPT or Claude, can create new vulnerabilities if user access is not well managed.
The danger of deepfakes
In addition to potential threats from agentic AI, AI-generated deepfakes and the AI vibe coding trend present cybersecurity problems, Turner said. Deepfake technology uses AI to generate convincing fake images, videos and audio. Deepfakes can lead to executive fraud, in which voice and video spoofing is used to deceive employees into transferring funds or disclosing sensitive information, Massey said. AI-generated deepfakes are becoming more prevalent and are “extraordinarily dangerous and difficult to guard against,” said Brian Greenberg, CIO of RHR International. One approach RHR is considering to address deepfakes is implementing out-of-band authentication (OOBA)ย techniques, which rely on a separate communication channel to verify the user’s identity. The OOBA method can send a push notification or pose a question that only the real person could answer.Fighting AI with AI
To address AI-generated and more traditional cybersecurity risks such as phishing, CIOs and CISOs will need to continue regular security awareness trainings for employees, Turner said. At RHR International, employees are required to participate in quarterly security trainings. The organization sends fake phishing emails to test if employees can recognize and report phishing attempts, Greenberg said. In addition to training exercises, automated responses to AI-based attacks will be critical, Turner said. While the use of AI as a defensive security measure is still in relatively early stages, automated threat detection and response — together with skilled SOC employees — will play a central role in supporting organizations’ ability to thwart AI-based cybersecurity threats, he added. “CIOs must fight fire with fire and use AI defensively,” Massey said, pointing to behavior-based detection tools that create a baseline for user actions by analyzing and monitoring their activity to identify anomalies. Greenberg echoed that view, noting that RHR International is implementing identity provider tools to centralize the user authentication process, in addition to deploying a zero-trust framework. “We’re just trying to make it easier to fill the gaps between platforms that naturally seems to occur,” Greenberg said.Enjoyed this article? Sign up for our newsletter to receive regular insights and stay connected.