Organizations are scrambling to secure a rapidly expanding attack surface. AI is now a major part of that challenge. Employees are experimenting with generative AI to boost productivity, automate tasks, and move faster.
But without guardrails in place, experimentation becomes a new exposure category.
This is where a disciplined AI acceptable use policy (AUP) and a mature continuous threat exposure management (CTEM) program intersect. An AI AUP defines the rules. CTEM makes those rules enforceable. Together, they give organizations a practical, measurable way to reduce AI-driven risk without slowing innovation.
AI acceptable use policies define how employees interact with AI tools and services. They provide clear guidelines for correct, ethical, and legal AI use, and should include:
Over the past few years, the use of unsanctioned generative AI has skyrocketed. Research from Fortune suggests that workers at 90% of companies use AI chatbots. The problem is that many of them are hiding it from IT - and that creates risk.
Unsanctioned AI use - also known as "shadow AI" - is a real problem for security teams. Employees might, for example, input sensitive information into public AI models. That can balloon an organization's cyber exposure. But preventing staff from using AI is also a risk. If your employees can’t experiment and innovate, you'll likely fall behind your competition.
Developing a GenAI acceptable use policy is the middle ground between excessive risk and stifled innovation. These policies define what employees can and cannot do with AI tools. They clarify data handling expectations. They also grant security teams the authority to monitor, block, or enforce restrictions on high-risk services. That's how you keep exposure in check.
In short, an AI AUP is the foundation of an effective continuous threat exposure management (CTEM) framework. It serves as the strategic control layer. Let's look a little deeper at how that works.
CTEM, or simply exposure management, is the next evolution of vulnerability management. Instead of focusing on isolated flaws, it covers your entire attack surface. And, crucially, it connects technical risks to business impact.
In practice, exposure management in cybersecurity gives security teams continuous visibility into every asset, its dependencies, and the realistic attack paths an adversary could use to move laterally. By blending threat intelligence, business context, and real-world exploitability, it ensures teams can focus on the exposures that matter.
CTEM helps break down the silos between IT and security. It aligns cyber risk with business priorities to give leaders a clear view of why certain issues deserve attention first. The result is a more accurate understanding of your true risk - and a more effective way to shrink the attack surface over time.
Think of it like this: an AI AUP gives you the framework for governance. But CTEM gives you the operational muscle to enforce it. To manage AI risk, you need three things most organizations don’t currently have:
Without those, AI exposure stays invisible and unmanageable. With them, you gain a complete, real-time picture of the AI activity happening across your enterprise. Whether it's intentional or accidental.
Many organizations underestimate how many AI tools their employees actually use. With CTEM-driven discovery, security teams can identify:
This inventory becomes the baseline for scoping and monitoring the AI attack surface.
AI exposure management is about more than determining what tools staff are using. Security teams must also understand what staff are putting into them. The best exposure management platforms extend visibility down to the prompt level, revealing:
This context is critical. Without it, you can’t meaningfully prioritize AI-related exposures.
And, finally, we come to enforcement. CTEM gives security teams the intelligence needed to:
When it comes to security, you can’t necessarily trust staff to always do the right thing. CTEM verifies and enforces policy continuously, just as it does for other exposure categories.
It’s important to recognise AI for what it is: a powerful tool, but a dangerous exposure vector. One that spans users, data, integrations, and third-party ecosystems. Treating AI policy as a foundational element of CTEM ensures you’re proactively reducing risk.
By integrating an AI AUP with discovery, visibility, enforcement, and continuous assessment into the CTEM cycle, organizations get:
This is the level of operational maturity enterprises need as AI adoption accelerates.
Your AI AUP gives you the rules. Your CTEM platform helps you enforce them. Together, they mean you can embrace generative AI – without losing control of your attack surface.
About the Author: Josh Breaker-Rolfe
Josh is a Content writer at Bora. He graduated with a degree in Journalism in 2021 and has a background in cybersecurity PR. He's written on a wide range of topics, from AI to Zero Trust, and is particularly interested in the impacts of cybersecurity on the wider economy.