Generative AI has moved from experimentation to business using it every day faster than any technology in recent memory. In less than two years, teams went from casually testing AI tools to relying on them for almost all business functions. The challenge is that the technology has spread much faster than the policies, visibility, and controls that security teams require.
This isn’t something that can be fixed by issuing another security control or policy document. AI models have access sensitive and proprietary data, they integrate with other business systems, retain context, and operate with autonomously. But not all organizations have visibility into what tools employees are using or what business data is connected.
The speed of AI integration creates a governance gap that can’t be ignored. A recent Deloitte survey found that nearly two-thirds of companies will deploy agentic AI within the next two years, but only some of those have established a governance strategy.
AI adoption is outpacing oversight, but that doesn’t mean that you should avoid AI altogether. Leaders know that AI delivers productivity gains and business impact. The goal is to create visibility, accountability, and safeguards as soon as possible.
Recent research from a leading consultancy shows that AI is now used across nearly every industry with most businesses using it in at least one business function. 1
And it’s led to a behavioral shift in how people work. Gallup reports that half of U.S. employees use AI in their role,2 and the AI market itself is growing.
One AI company reports more than a billion weekly active users with billions of dollars in recurring revenue. These are numbers that sounded unrealistic just a year ago. 3 Another AI provider is seeing similar momentum, with its enterprise demand driving revenue growth to a $30 billion run rate (up from about $9 billion at the end of 2025).4
ISACA’s 2026 AI Pulse Poll found that only a 38% of surveyed organizations have a comprehensive AI security policy (but that’s up slightly from 28% in 2025).5
That can be a problem because IT systems like email and other communication platforms, cloud drives, internal networks, and more are connected, and when employees use shadow AI, they don’t realize how data can be stored, processed, or exposed.
What’s more, AI-generated outputs can influence important business decisions. Teams use AI summaries, recommendations, generated reports, or automated analysis because it’s fast and convenient, but that AI reliance is risky without established review standards.
The risk is even greater when AI is used to process data from outside the company, including vendor proposals, client communications, and third-party reports. These look like typical business transactions, but when AI touches them, every touchpoint becomes part of the attack surface.
These were built for predictable environments and meant protecting the network perimeter, monitoring endpoints, detecting malicious code, and managing user identities. The security model worked because most IT ecosystems behaved according to fixed logic and predefined rules.
Today’s AI tools don’t just execute instructions, they interpret language, infer context, and generate responses in real time. It may seem harmless, but ungoverned AI expands the attack surface even further to prompts. That means AI can be manipulated by attackers using deceptive prompts or poisoned data.
And because AI is now integrated into e-mail, cloud platforms, CRMs, internal databases, and operational tooling, it’s a gateway to your entire IT ecosystem. The Cloud Security Alliance describes this threat landscape as the “cognitive cyber domain.” It’s a category of security risk that includes manipulating trust, context, and human interaction. And it’s one reason that conventional security strategies are not robust enough for AI tools.
1. Inventory your AI assets
Teams can’t govern AI when they don’t know where it’s being used. Employees use tools on their own (shadow AI), and when used on company devices, they can connect to internal systems.
2. Define who can use AI
Not every AI use case carries the same risk. Using AI to draft internal meeting notes is very different from relying on it to summarize confidential client information, generate regulatory documentation, or support financial decisions.
3. Authorize sensitive use cases with human overview
Teams increase risk by using AI-generated outputs for high-stakes decision-making without proper human judgement and overview. AI can produce convincing summaries, recommendations, and analysis. But convincing is not the same as accurate.
4. Invest in AI security training
Team members using generative AI many not fully understand risks. Review details and how to spot prompt injections, manipulated content, false outputs presented as facts, and unsafe data-sharing behaviors.
5. Partner with managed security providers
External security partners who specialize in AI security can support small teams and help create a governance strategy to reduce blind spots and establish controls.
NexusTek partners with customers to create and manage AI security models that keep up with adoption, close governance gaps, and empowers them to take full advantage of AI's productivity benefits. Contact us to start the conversation.
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