AI is the new technological revolution of the day. Everyone is talking about it, devising new use cases, and releasing their AI-powered solutions to the market. Across industries, organizations are rushing to deploy generative models, copilots, and AI assistants, fearing that if they move too slowly, they will lose relevance and market share. The specter of Kodak’s failure to adapt to digital imaging looms large.
With speed comes risk. In this early era of AI adoption, the technology is still poorly understood. Vulnerabilities are being probed in real time. Organizations will suffer breaches before AI use matures. In the meantime, it is the responsibility of each CISO to ensure their organization is not the cautionary tale of future generations. Already, 78 percent of organizations report using AI in at least one business function, and generative AI usage alone jumped from 55 percent to 75 percent between 2023 and 2024.
Though AI is the revolution in front of us, we’ve faced similar upheaval before. This rapid proliferation mirrors the early cloud adoption surge of the 2010s. Just a decade ago, cloud migration was taking off. Today the cloud is ubiquitous: 94 percent of companies now use cloud services.
The adoption curve that once felt novel is now assumed. In other words, the cloud era matured. The risks were serious then; the risks are serious now. In this blog, we reflect on the cloud security era and distill lessons for organizations embracing generative AI and large language models. The parallels are striking. The strategies we applied then remain relevant—but must evolve.
During the first wave of cloud adoption, individuals often created personal service accounts and charged them to corporate credit cards. As Patrick Ethier, Chief Technology Officer at SecureOps, recalls, “People were signing up to their own AWS accounts, putting in their company credit card and building applications out.” Only later did organizations discover the security implications of these shadow systems.
Today, AI experimentation follows the same pattern. Teams test new models or plug-ins independently, assuming short-term harmlessness. Governance and policy lag behind adoption, leaving unmonitored data paths and unapproved toolchains proliferating inside enterprises.
Every major technology cycle, APIs, virtualization, cloud, and now AI, follows this sequence: initial chaos, rushed integration, and eventual discipline. Patrick summarizes the present phase bluntly: “We’re in the wild west with AI for the next two, three years at least.” The lesson from previous revolutions is to recognize this stage early and establish control internally before the market imposes it through breach or regulation.
As organizations rush to integrate AI into daily workflows, the human element remains both the greatest asset and the weakest link. The success of any AI deployment depends not just on technical safeguards, but on how well people understand and manage the tools at their fingertips.
A major risk in the current AI adoption wave is that users simply don't understand how these tools work. They may interact with AI in honest, well-intentioned ways, such as trying to increase productivity or contribute to efficiency, yet inadvertently expose sensitive data because they are unaware of how the AI processes, retains, or transmits information.
The pressure to innovate exacerbates this risk. In the race to keep up, security can be seen as an obstacle. As Patrick notes, this mirrors past adoption cycles where “the first thing to go out the door is the security aspect because nobody’s going to sit there and say, ‘I can't deploy this because my security is so important.’ And then watch their competitors get all the customers.” This environment creates conditions where mistakes happen out of a speed-driven necessity.
Curbing this risk requires robust security awareness training that is analogous to modern phishing education programs. Users must be taught to stop, reflect, and assess the potential implications of their actions before engaging with an AI tool. They need to know not just what the AI is doing, but where their data goes, who can see it, and how it's stored. The core message, according to Patrick, should be to instill a habit of critical thinking:
“Stop and think about what you’re doing. Are you interacting with PII? Are you manipulating PCI data? If you are, then is what you’re doing an approved process or are you just winging it?”
Training reinforces that even well-meaning experimentation carries tangible exposure risks. As with phishing, a thoughtful pause is the most effective security control of all.
AI adoption is accelerating faster than governance can keep pace. The real risk emerges when a user integrates a third-party AI that accesses the internet or bridges applications. As Patrick explains, “you can run an AI agent locally on your laptop and it’s restricted. But now, you’re putting an external AI service in the in-between. Do you know who else that AI is talking to?”
Lessons from the cloud era demonstrate that early missteps become disasters without structure. Effective internal governance is critical for creating a framework for safe innovation. This includes:
Following these disciplined steps ensures that early mistakes become correctable lessons rather than catastrophic failures.
Internal governance alone isn't enough. The external vendor landscape adds a layer of complexity that can undermine AI security. Many AI platforms claim to have enterprise-grade security but may only partially implement the necessary controls. As Patrick warns, “Some of these tools say they do something but it’s only half implemented. Test it before rolling it out to everyone.” For example, a configuration option in a user interface may exist in appearance but provide no functional protection. Technical validation is essential in your evaluation process.
Understand the data path of your AI applications, meaning the full journey your information takes from its origin through any AI platform. An employee might use an AI tool that seems to operate locally, but if the workflow routes requests through an external, third-party service, sensitive data can be exposed without anyone realizing it. This is one of the most significant hidden risks of AI integration. Patrick highlights the danger clearly:
“Everything you see on your screen through a third-party AI, the AI is able to see as well. If you don’t understand what that service is doing, you could unintentionally expose sensitive information.”
Due diligence requires full visibility into this data flow. Organizations must test each platform, document how it handles data, and ensure that all points of exposure are accounted for in risk assessments.
The generative AI era mirrors the cloud migration era, not in identical threats, but in dynamics. Rapid innovation, immature governance, rogue experimentation, and evolving vendor landscapes define both.
Organizations that succeed will be those that adopt fast but govern deliberately. Recognize that, as Patrick explained, we are still in the “wild west” of AI. Prioritize user education, change control, and vendor scrutiny. Treat early errors as an investment in your maturity, not as defeats.
In the words of Patrick, the ultimate goal is to create a culture where "mistakes happen not out of negligence, but out of a genuine effort to do better.”