Establishing AI Policy as a Pillar of Exposure Management

Date: 15 December 2025

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. 

What is an AI acceptable use policy?

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: 

• Approved tools: Both organization-wide and business-unit specific.
• Data handling rules: Clear guidance on what employees can and cannot input into AI tools.
• Usage guidelines: Expectations around content generation, accuracy, and ethical boundaries. 
• Restrictions: Prohibited behaviors, sensitive data classifications, and non-negotiable safeguards. 
• Consequences: What happens when staff violate policies. 

Why do I need an AI acceptable use policy? 

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.

What is continuous threat exposure management? 

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.

How does CTEM help govern AI and enforce an acceptable use policy?

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:

• Visibility into how employees use AI

• Context around what data is being exposed

• Controls to enforce policy when usage crosses the line

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. 

Discovery: Inventorying AI usage across the enterprise

Many organizations underestimate how many AI tools their employees actually use. With CTEM-driven discovery, security teams can identify: 

• All generative AI interactions across their workforce

• Shadow AI tools employees use without approval

• High-risk usage patterns, such as personal accounts or unvetted browser plugins

This inventory becomes the baseline for scoping and monitoring the AI attack surface.

Deep visibility: Understanding what’s actually being shared

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:

• The types of data employees are submitting

• Whether sensitive, regulated, or confidential information is being exposed

• Which business units or workflows pose the greatest data-leakage risk

This context is critical. Without it, you can’t meaningfully prioritize AI-related exposures.

Enforcement: Turning policy into practice

And, finally, we come to enforcement. CTEM gives security teams the intelligence needed to:

• Enforce AI acceptable use policies consistently

• Block tools that violate governance rules

• Prevent sensitive data from being shared with external AI models

• Trigger investigations when usage patterns look risky

• Educate users when they misapply AI in ways that introduce exposure

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. 

AI governance as a core component of exposure management

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: 

• A complete picture of AI-related exposures

• Controls that scale with AI adoption

• Confidence that sensitive data stays protected

• A governance model that supports – not restricts – innovation

This is the level of operational maturity enterprises need as AI adoption accelerates. 

AI AUP and CTEM: Better together

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.