If your company has not bought a dedicated AI compliance framework yet, the temptation is obvious: write a giant “AI policy,” slap it in the handbook, and declare victory.
That instinct is understandable. It's also usually not where the real value is.
The better starting point, in many cases, is not a sprawling standalone AI policy. It is a tighter, more disciplined review of the policies you already have, plus a few targeted additions where the technology creates genuinely new risks. Across the reference materials we reviewed, a consistent theme shows up: for many companies merely using AI tools, existing confidentiality, security, privacy, acceptable use, and IP rules should be broadened and reinforced before scrambling to draft a large, complex AI policy.
That same body of guidance also points to a practical middle path. If an organization needs immediate guardrails, a short interim AI use policy can make sense. But it should sit alongside refreshed core policies and operational procedures, not replace them.
The Real Question is Not “Do We Need an AI Policy?”
The real question is: what decisions, data, workflows, and risks are actually being changed by AI inside the business?
That matters because “AI” is not one policy issue. It is a stack of policy issues.
In practice, most organizations need to think in terms of:
Confidentiality and trade secret handling
Privacy and personal data use
Acceptable use of public and enterprise AI tools
Human review for high-impact outputs
Vendor diligence and contract controls
Accuracy, claims substantiation, and marketing truthfulness
IP, training data, and output ownership
Logging, monitoring, and incident response
This is how serious governance materials from legal, privacy, security, and audit organizations tend to frame the problem. The useful pattern across those sources is consistent: privacy and data governance, fairness, security, accountability, vendor controls, monitoring, training, and consumer protection all show up repeatedly, because AI governance is broader than any single policy document.
So if you are a company in the “we use some copilots, maybe some internal automation, maybe a few customer-facing AI features” phase, the practical next steps usually look something like this:
Tighten existing policies first
Add a short AI use policy if employees need immediate guardrails
Create role-based procedures for higher-risk AI uses
Save the full management-system architecture for when your use cases, customers, or regulators actually require it
Start Here: Drata’s General AI Usage Policy Template
This is the practical starting point we'd put in front of most organizations still early in their AI governance journey: a stripped-down, business-friendly policy template that covers the essentials, without claiming to be a full NIST AI Risk Management Framework, ISO 42001, or AIUC-1 program.
General AI Usage Policy Template
The point of this template is simple. It gives teams a reasonable baseline for approved use, restricted data, human review, vendor diligence, and reporting, while making it clear that higher-risk use cases still need heavier governance.
A Lightweight AI Use Checklist
Before using a new AI tool or new AI use case, ask yourself:
Is this a legitimate business use?
Is the tool approved?
Am I using any confidential, customer, personal, or otherwise restricted data?
Does this output need human review before anyone relies on it?
Could this use affect customers, employees, legal rights, security, or public claims?
Do I need privacy, security, legal, procurement, or management approval first?
If the answer to any of those questions creates uncertainty, stop and seek guidance before proceeding.
This template is a practical starter policy for general business AI use. It is not a substitute for legal advice, a formal AI governance framework, or a full set of risk-based procedures for high-impact AI systems.
Growing Into a Formal Framework
The General AI Usage Policy Template above is designed for exactly what it says: everyday AI use, at the stage most companies are in right now. But at some point (more AI-embedded products, more regulated use cases, more customers asking hard diligence questions) a single policy needs operational reinforcement, no matter how well it was written.
That's the point where dedicated framework support starts to matter. When customers adopt our AI-related framework solutions, they get more than a policy label. Drata's policy-to-control mapping includes framework-specific content such as an AI Governance Policy, AI Risk Management Policy, and AI System Development and Evaluation Policy — and for ISO 42001, an Artificial Intelligence Management System plan that ties those documents into a full management system.
Drata's ISO 42001 framework overview describes a Policy Library with customizable, pre-built policies aligned to ISO/IEC 42001 requirements, plus an Artificial Intelligence Management System template built to accelerate policy development for AI risk management, accountability, and transparency.
In our AIUC-1 materials — the newest AI agent compliance framework supported by Drata — the same pattern holds: pre-built requirements, controls, and policy templates mapped to AIUC-1's six domains; data and privacy, security, safety, reliability, accountability, and societal risk.
The mental model is simple: a mature AI program isn't one document. It's a policy stack connected to controls, evidence, review, and monitoring; and that's exactly what a framework adds on top of a standalone policy.
A Few Outside References for Your Your Back Pocket
The General AI Usage Policy Template above will cover most day-to-day needs. If you want to sanity-check it or borrow language, these five are worth knowing:
1. SANS: Artificial Intelligence Acceptable Use Standard
SANS Artificial Intelligence Acceptable Use Standard
Why it matters: SANS is a long-standing cybersecurity training and research organization, so its materials tend to be operational and security-minded rather than abstract which is useful for grounding language around approved tools and sensitive inputs. Pairs well with existing acceptable use and data handling policies.
2. ISACA: Artificial Intelligence Acceptable Use Policy Template
ISACA Artificial Intelligence Acceptable Use Policy Template
Why it matters: ISACA is a widely known governance, risk, audit, and security association, so its templates tend to land well with compliance and audit-minded teams.
3. ICO: AI and Data Protection Risk Toolkit
ICO AI and Data Protection Risk Toolkit
Why it matters: the ICO is the UK’s data protection regulator, so this is a particularly credible source for privacy-heavy AI governance. If your AI use cases touch personal data, this is one of the most useful template-adjacent resources on the market because it is less “policy prose” and more “show me the risks, questions, and lifecycle checks.”
4. IAPP: Texas Responsible AI Governance Act Compliance, A Sample Policy Framework
Why it matters: IAPP, the International Association of Privacy Professionals, is one of the best-known privacy and AI governance communities in the market. This is a modern policy skeleton with governance roles and review cadence, not just principles.
5. ACC: Artificial Intelligence Toolkit for In-house Lawyers
ACC Artificial Intelligence Toolkit for In-house Lawyers
Why it matters: ACC, the Association of Corporate Counsel, is a well-known professional organization for in-house legal teams. This is a broader toolkit that brings together practical AI governance guidance, checklists, and policy-oriented materials in one place.
Putting It All Together
A single document rarely covers AI governance on its own, so here's what you are looking for if you want to get the job done.
What most companies need first is:
A short, usable AI use policy
Refreshed confidentiality, privacy, security, and IP policies
A lightweight intake and review process for higher-risk AI use cases
Documented vendor and contract expectations
Training that tells employees what they can and cannot do
Start with the checklist above. Adopt the General AI Usage Policy Template. Get those five things in place, and you've covered the vast majority of everyday AI risk — without spending months building infrastructure before you actually need it.
Then, when the business matures, when customers start asking harder diligence questions, or when a formal framework becomes necessary, that is when a formal framework like ISO 42001, NIST AI RMF-aligned governance, or AIUC-1 becomes necessary.
That's what matters most: not every company needs a massive standalone AI policy today. But every company using AI needs policy coverage for the risks AI creates. The smart move isn't to overreact — it's to govern the real problem in front of you, with the smallest policy stack that still gets the job done. And when you're ready for more, Drata's AI governance frameworks pick up right where the template leaves off. Book your demo now.
