It's clear that AI is here to stay. Even if your organization hasn't formally adopted it, chances are your users already have.
Technology advancements come thick and fast, and policy tends to either lag behind or be written without a real understanding of how and why it's being implemented.
AI policies are similar to computer use policies in that they need to be tailored to the organization: its risk profile, security levels, and any industry or government regulations it must align with.
In practice, a workable AI policy isn't about permission versus prohibition. Rather, it revolves around visibility, data boundaries, and shaping governance around how the organization actually uses (or wants to use) AI.
Given there's no one-size-fits-all approach, this blog discusses different aspects of an acceptable AI use policy and the nuances behind each one.
Is shadow AI the new shadow IT?
Field Effect secures many organizations across different sectors and industries, and the amount of AI-related activity has continued to rise exponentially.
Seeing firsthand the disconnect between what organizations think their AI use looks like and what it actually is suggests that shadow AI could become a bigger risk than shadow IT ever was.
And historically, there have always been gaps when a new policy is introduced, creating uncertainty for both end users and the teams enforcing the policy. This is exactly where security problems tend to arise.
Take this event as an example.
Field Effect MDR detects an unusual volume of downloads from SharePoint and issues an alert to the organization. An investigation reveals that the activity is driven by ChatGPT, shortly after a user connects it to their Microsoft 365 account.
The IT team works to determine whether this is expected or even allowed, but that takes time, and every minute spent gathering context is a minute the account stays active and the data keeps flowing. This friction in getting clear answers delays the decision to disable the account or block the application until further notice.
This could have been a legitimate workflow, an oversight, or a case of documents being unintentionally shared outside the organization. Without the correct AI use policy in place, it's difficult to quickly tell which one it was.
Why “just say no” fails
Organizations cannot "just say no" to AI adoption, as this will ultimately push end users to thwart company policies or circumvent the restrictions intended to stop the use of AI tools. End users will resort to browser tools, utilize personal accounts, or adopt unsanctioned tools, which worsens the current visibility issues that arise with AI and goes against the goals of the policy. Any unmonitored tool brings inherent risk of malicious or accidental data leakage, or unmanaged identity and access vectors.
Some organizations are highly regulated and handle classified or PII data at scale, where a stricter default policy is the only option. Rather than whole-scale blocking of AI, considering what types of applications align with the organization's risk tolerance and requirements can help clarify what's actually acceptable. Identifying the correct applications, and then target-blocking non-approved applications will funnel users toward those tools and help with overall AI containment.
Whenever a hard restriction is put in place, someone will find a way to circumvent it. So the question in this scenario becomes: why are we restricting AI to the degree that we are? And the follow-up question becomes: is there an alternative approach we can use that aligns with our organization's adoption of AI?
No universal policy, only universal questions
While there may not be a universal policy organizations can implement, there are certainly universal questions many organizations can start with to help them understand how to position themselves for an acceptable AI use policy.
- What data can legitimately leave our environment and does that change when an AI tool is involved?
- Who is accountable for the output of AI-generated data when it's wrong, biased, or leaked?
- Which AI use cases create business value, compared to those that create risk with no potential benefit?
- Do we have visibility of sanctioned and unsanctioned AI use within our organization?
- How does this align with existing compliance obligations? (HIPAA, SOC 2, PCI DSS)
The spectrum of organizational needs
Answers to these questions will differ wildly between organizations due to size, industry, or even government regulations, and that's expected.
What stays consistent is the process for getting to those answers: starting from the same core questions, working through them in the context of your organization's risk profile, and landing on a policy shaped by your specific answers. The result won't look the same from one organization to the next, but the path to get there can.
Let's take a regulated or high-data-sensitivity organization, such as a healthcare business or legal firm, and consider what the policy likely requires. Data classification will, or should, already be enforced, so classification labels can be used with a stoplight analogy:
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Green-labelled data can be used within any AI platform
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Yellow-labelled data must be used within pre-approved, managed AI systems
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Red-labelled data must never touch an AI platform
Organizations must account for the legitimacy and truth of data generated by AI platforms. For example, in legal cases, this means confirming that AI-suggested precedents actually exist and were not hallucinated.
An engineering or tech firm that utilizes AI coding assistants must consider code or intellectual property leakage through these platforms. Their use of AI generally won't put client data at stake, but a company's internal secrets being leaked can make or break the business. Intellectual property ownership and the licensing of products that utilize AI will need to be understood and accounted for within the policy.
A leaner organization without a dedicated security team or legal staff will need a more lightweight, enforceable AI use policy, not a 40-page document that will likely be skimmed during onboarding and permanently shelved thereafter. The risk might be lower; however, there is never zero risk when using corporate data on third-party platforms.
Lastly, a creative or customer-service organization will face higher reputational risk from using AI systems, especially when it comes to hallucinations, brand alignment, and disclosure to customers. A policy for this type of organization will focus less on data leaks and more on content reliability and transparency, where AI might be taking on a role previously held by a human employee.
What “practical” actually means in a policy
What makes a policy practical? Ease of enforcement, and the organization's willingness to revisit it regularly to ensure it still aligns with its vision. Keeping it simple, direct, and to the point will allow end users to easily digest the policy, and avoid dread when they revisit it in 6, 12, or 24 months' time.
Some simple considerations can be:
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What is allowed?
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What is not allowed?
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Why is that the case?
Three simple sections for an employee to read, digest, and carry with them when using AI platforms.
For organizations that already have data classification in place, using a known scheme can reduce the complexity for users trying to understand, at a glance, what data can and cannot be put into these platforms.
Consider outlining approval or exception workflows that encourage users to ask if they can use a given product, to help reduce the amount of shadow AI taking place.
Since this space is moving fast and changing week by week, it will benefit an organization to have someone, or some team, take ownership of the policy and act as the go-to source for staff questions.
From policy to practice
Having any form of an AI policy is more about building a detection and response capability than a legal one.
As AI use grows, the line between legitimate and anomalous or malicious use continues to blur, as local agents often rely on the same reconnaissance and remote execution techniques that threat actors have been known to use.
Rather than creating an AI use policy just to have one, the goal is to get to a point where you can say: "we understand what AI use violates our risk factors, and how our use of AI could be exploited."
That's where visibility (and Field Effect's AI Detection & Response) comes in. A policy only becomes actionable once you can see how AI is actually being used and misused across your environment. Field Effect AIDR makes it easy to do just that by providing the visibility and control an organization needs to successfully enforce its policy.
But this isn't where it ends. The AI frontier will continue to flourish in the coming weeks, months, and years. So keep the five earlier questions in mind, and remember that the policy must evolve with the technology and the business.
Frequently asked questions
What is shadow AI?
Shadow AI refers to the unsanctioned or unmonitored use of AI tools within an organization, similar to how "shadow IT" describes unauthorized software or devices. It often surfaces when employees connect personal AI accounts to corporate platforms, or use browser-based AI tools without approval, creating visibility gaps that can lead to data exfiltration or leakage.
Should organizations just block AI tools altogether?
Blocking AI outright usually isn't the right approach for most organizations. Hard restrictions tend to push users toward unsanctioned tools, personal accounts, or workarounds, which worsens visibility issues rather than solving them. A more effective approach is identifying which applications align with your risk tolerance, then targeting the blocking of non-approved tools to funnel users toward approved ones.
Is there a one-size-fits-all AI use policy we can copy?
No. AI policies should be tailored to each organization's risk profile, security levels, and any industry or government regulations it must align with, much like computer use policies. That said, there are universal questions every organization can start with, regardless of size or industry.
What are the key questions to ask before writing an AI use policy?
At minimum, organizations should ask: what data can legitimately leave the environment (and does that change with AI involved); who is accountable for AI-generated output that's wrong, biased, or leaked; which AI use cases create real business value versus unnecessary risk; whether there's visibility into sanctioned and unsanctioned AI use; and how AI use aligns with existing compliance obligations.
How often should an AI use policy be reviewed?
A practical policy should be revisited regularly to make sure it still aligns with the organization's vision. Every 6, 12, or 24 months is a reasonable starting point, though the right cadence will depend on how quickly your organization's AI use is evolving.
Is an AI use policy just a legal or compliance document?
Not really. It's more about building detection and response capability than fulfilling a legal checkbox. As AI use grows, the line between legitimate and malicious use continues to blur, so the goal is to understand what AI use violates your risk factors and how your use of AI could be exploited, not just to have a policy on paper.
How does Field Effect AIDR help with AI policy enforcement?
Field Effect's AIDR helps close the gap between having a policy and actually enforcing it, by giving organizations the visibility and control needed to see how AI is being used and misused across their environment.
