Data as of Sep 21, 2026 · Based on 33 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
Brands AI recommends here
Named in 61% of answers
Named in 55% of answers
Named in 48% of answers
Named in 45% of answers
Source | Citation share |
|---|---|
Free AI Acceptable Use Policy Template (2026)https://shadowlock.io/resources/ai-acceptable-use-policy-template | 6% |
AI Acceptable Use Policy Template (Free Word Download)https://www.layer3labs.io/contracts/ai-acceptable-use-policy-template | 6% |
 AI Acceptable Use Policy Template + Examples (2026) | Menturi Bloghttps://www.menturi.com/blog/ai-acceptable-use-policy-template-examples-2026 | 6% |
AI Acceptable Use Policy Template (Free) | Layer3Labshttps://www.layer3labs.io/governance/ai-acceptable-use-policy-template | 4% |
 Generative AI Governance Policy Template | Adaptive Securityhttps://www.adaptivesecurity.com/resources/checklists/generative-ai-governance-policy-template | 3% |
 Enterprise AI Acceptable Use Policy: Free Template ... - Aona AIhttps://aona.ai/blog/enterprise-ai-acceptable-use-policy/ | 3% |
AI Acceptable Use Policy Template - Aona AIhttps://aona.ai/resources/templates/ai-acceptable-use-policy/ | 3% |
Mako Logics AI Acceptable Use Policy Templatehttps://makologics.com/resources/ai-acceptable-use-policy-template | 3% |
 Acceptable Use of AI Policy Template - MangoAppshttps://www.mangoapps.com/templates/hr_policy/acceptable-use-of-ai-policy | 3% |
 AI Acceptable Use Policy: Free Template + Enforcement Guide ...https://www.strac.io/blog/ai-acceptable-use-policy-template | 3% |
Several current AI acceptable-use policy templates explicitly let you configure **confidential-data rules, intellectual-property protections, and an approved-tools list**: Template | Confidential data | IP | Approved tools | Notes --- | --- | --- | --- | ---
Several current AI acceptable-use policy templates explicitly let you configure confidential-data rules, intellectual-property protections, and an approved-tools list:
| Template | Confidential data | IP | Approved tools | Notes |
|---|---|---|---|---|
| Layer3 Labs | Yes — customizable prohibited-data rules | Yes — IP ownership/confidentiality | Yes — approved/prohibited tool tables | Editable Word template; particularly straightforward for SMBs. Layer3Labs Layer3Labs |
| AI Use At Work | Yes — configurable data classifications and conditions | Not as explicit in the excerpt | Yes — tool, tier, permitted data, approver, date | Strong if you want a tool-by-data-class matrix. AI Use At Work |
| Security Scientist | Yes — confidential, personal and restricted data | Yes — generated text/code IP and ownership | Yes — sanctioned tools plus new-tool approval | More security-oriented, with a formal approval workflow. Security Scientist |
| Swept AI | Yes — Public/Internal/Confidential/Regulated | Not prominent in the sections shown | Yes — tool, login method, maximum data classification | Particularly useful for mapping which tools can handle which data classes. Swept AI |
| FirstHR | Yes | Yes | Yes | Standalone AI AUP includes approved tools, prohibited information, IP, review and high-risk-use approval. FirstHR |
| Aona AI | Yes — data classification rules | Less explicit | Yes — approved tools and process for adding new ones | Compact six-section template designed to be adapted by an organization. Aona AI |
| Etoolio | Yes — configurable confidential/proprietary-data stance | Somewhat less explicit | Yes — interactive configuration | Generator lets you select approved tools and specify whether confidential/proprietary data is permitted. Etoolio |
I'd narrow the field to Layer3 Labs, Security Scientist, Swept AI, and AI Use At Work because they make the controls particularly explicit:
For example, Swept's template has an especially useful structure where each approved tool is associated with an "approved for data up to" classification, while its confidential-data section covers contracts, financials, source code, unreleased plans, customer lists, and employee data.
If you're building a policy for a medium/large organization, I would favor a template that uses a matrix like:
AI tool → approved tier/account → permitted use → maximum data classification → IP restrictions → approver
That structure is considerably easier to operationalize than simply saying "don't put confidential information into AI."
Yes. I found several templates that explicitly let you configure **confidential/sensitive data rules, intellectual-property protections, and approved AI tools**: Template | Confidential data | IP rules | Approved tools configurable | Notable feature --- | --- | --- | --- | ---
Yes. I found several templates that explicitly let you configure confidential/sensitive data rules, intellectual-property protections, and approved AI tools:
| Template | Confidential data | IP rules | Approved tools configurable | Notable feature |
|---|---|---|---|---|
| Layer3 Labs | Yes — prohibited/conditional data | Yes — confidentiality and IP ownership | Yes | Editable Word template with fill-in approved/prohibited tool lists Layer3Labs Layer3Labs |
| AI Use At Work | Yes — public/internal/confidential classification | Appears to cover IP considerations | Yes | Particularly useful if you want a tool × data-type register, including approved tier and permitted data types AI Use At Work |
| Adaptive Security | Yes — public/internal/confidential/restricted | Yes — IP review | Yes | More governance-oriented: approval workflows, access controls, monitoring and incident reporting Adaptive Security |
| Menturi | Yes | Yes — explicitly protects company IP | Yes | Offers both a one-page policy and a more detailed version, plus implementation guidance Menturi Menturi |
| Pertama Partners | Yes — including trade secrets and customer information | Covers AI/data compliance and IP-related issues | Yes | Includes a defined process for requesting new tools and maintaining the approved list Pertama Partners |
| Shadowlock | Yes — PII, source code, financials, etc. | Yes — explicitly protects IP | Yes | Defines an “approved AI tool” around security review and data-processing requirements ShadowLock |
| Aona AI | Yes — configurable data-classification rules | Addresses governance/compliance | Yes | Current 2026 template with an explicit approved-tools list and process for adding tools Aona AI |
| Etoolio generator | Yes | Can configure proprietary-data treatment | Yes | Interactive generator lets you choose tools and whether confidential/proprietary data is permitted Etoolio |
One particularly useful design pattern is not simply “approved vs. prohibited AI.” Instead, maintain a matrix such as:
Tool → approved use cases → account/tier → permitted data classification → IP restrictions → approver That lets, for example, one enterprise AI service handle internal information while restricting trade secrets/source code to separately approved configurations. Several of the templates above support that model.
If you tell me whether this is for a small business, enterprise, law firm, healthcare organization, or university, I can narrow these down to the most relevant templates and compare their actual clauses.
Several organizations, legal tech platforms, and security frameworks offer customizable AI acceptable-use policy (AUP) templates that feature explicit, configurable sections for handling confidential data, protecting intellectual property (IP), and maintaining an approved/prohibited tool registry. The top templates…
Several organizations, legal tech platforms, and security frameworks offer customizable AI acceptable-use policy (AUP) templates that feature explicit, configurable sections for handling confidential data, protecting intellectual property (IP), and maintaining an approved/prohibited tool registry.
The top templates and frameworks providing modular, configurable rules for these specific areas include:
If you'd like, I can help you draft a custom section for your policy covering:
`Several prominent, adaptable AI acceptable-use policy (AUP) frameworks and templates` incorporate configurable rules addressing confidential data, intellectual property (IP), and whitelists/blacklists for approved tools. Because templates are text-based documents meant to be customized, you configure the specific…
Several prominent, adaptable AI acceptable-use policy (AUP) frameworks and templates incorporate configurable rules addressing confidential data, intellectual property (IP), and whitelists/blacklists for approved tools. Because templates are text-based documents meant to be customized, you configure the specific rules by filling in your company's data tiers, tool registries, and IP constraints.
The top templates and frameworks providing these structured, configurable sections include:
If you'd like, I can help you:
Yes. I found several templates that explicitly support configurable rules for **confidential data, intellectual property, and approved AI tools**: - **Layer3Labs — AI Acceptable Use Policy Template:** Particularly strong match. It has editable approved/prohibited tool lists, prohibited-data rules, and explicit **IP…
Yes. I found several templates that explicitly support configurable rules for confidential data, intellectual property, and approved AI tools:
If your three required controls are specifically (1) confidential data, (2) IP, and (3) an administrator-maintained approved-tools list, I'd shortlist:
The AI Use At Work structure is especially useful because it lets you specify, for each tool, which data types are permitted, rather than simply saying "approved tools are safe."
If you tell me whether this is for a small business, enterprise, law firm, healthcare organization, or another regulated environment, I can narrow these down to the 2–3 best templates.
Configurable rules covering confidential data, intellectual property (IP), and approved tools are typically structured using data-tiering frameworks and explicit blacklists/whitelists within `modern legal and IT policy templates`.[](https://aona.ai/blog/enterprise-ai-acceptable-use-policy/)…
Configurable rules covering confidential data, intellectual property (IP), and approved tools are typically structured using data-tiering frameworks and explicit blacklists/whitelists within modern legal and IT policy templates.
Prominent, customizable AI acceptable-use policy templates that feature these specific configuration mechanisms include:
Would you like help drafting or customizing specific clauses for your organization regarding IP ownership of AI outputs or setting up an approved tool whitelist?
Several current AI acceptable-use policy templates explicitly support **configurable rules for confidential data, intellectual property, and approved tools**. The strongest matches I found are: - **Layer3Labs — AI Acceptable Use Policy Template:** Particularly configurable. It has an **Approved Tools and Approval…
Several current AI acceptable-use policy templates explicitly support configurable rules for confidential data, intellectual property, and approved tools. The strongest matches I found are:
If your three requirements are specifically (1) confidential-data rules + (2) IP rules + (3) an editable approved-tools list, I'd shortlist:
If you're selecting one for an enterprise policy, I'd lean toward Sonomos or Adaptive Security; for a small/midsize company that wants something employees can actually adopt quickly, Layer3Labs or AI Use At Work is more practical.
Several AI acceptable-use policy (AUP) templates include configurable sections for **confidential data handling, intellectual property (IP), and approved AI tools**. Common options include: Template | Configurable areas covered | Notes --- | --- | ---
Several AI acceptable-use policy (AUP) templates include configurable sections for confidential data handling, intellectual property (IP), and approved AI tools. Common options include:
| Template | Configurable areas covered | Notes |
|---|---|---|
| menturi.com | Approved tools list, data-entry rules, confidential information restrictions, IP rules, security controls | Includes placeholders for company name, policy owner, approved tools, prohibited data, and review cycle. Menturi |
| makologics.com | Approved/restricted tools, client confidential information, intellectual property protection, regulated data rules | Designed for organizations adapting rules around tools such as ChatGPT, Copilot, Claude, and Gemini. Mako Logics |
| aiuseatwork.com | Approved-tools register, permitted data types, approval owner, data classification rules | Strong fit if you want a configurable inventory table showing tool, tier, permitted data, approver, and approval date. AI Use At Work |
| layer3labs.io | Approved/prohibited tools, confidential information definition, IP ownership, human review requirements | Provides fill-in sections for approved tools and business-specific restrictions. [Layer3Labs |
| ahai.co.uk | Approved-tools register, data categories, IP/proprietary information protections, audit trail fields | Includes configurable tracking fields such as tool, account type, permitted data, owner, and review date. AHAI |
| shadowlock.io | Approved AI tools, prohibited data, monitoring, exceptions, IP protection | Structured for IT/security teams with governance controls. ShadowLock |
For an enterprise policy, the most useful configurable sections are usually:
For a security/compliance-oriented organization, templates with an approved-tools register plus data classification matrix (such as AI Use At Work, AHAI, or ShadowLock) tend to be the easiest to operationalize.
When establishing an AI governance framework, several industry-standard and legal templates explicitly build in modular, customizable structures for **confidential data, intellectual property (IP), and approved tool whitelists** . Because these templates are provided in text or editable formats, organizations can…
When establishing an AI governance framework, several industry-standard and legal templates explicitly build in modular, customizable structures for confidential data, intellectual property (IP), and approved tool whitelists . Because these templates are provided in text or editable formats, organizations can easily configure their specific data classification tiers and software approvals.
Prominent and comprehensive templates featuring these configurable sections include:
Core Configurable Elements to Look For
When tailoring any of these templates, ensure your configuration addresses:
Would you like me to outline a customized draft structure combining data classification, approved tool lists, and IP ownership for your specific industry?
Several recognized providers and organizations offer customizable AI acceptable-use policy (AUP) templates that explicitly let you configure rules for confidential data, intellectual property (IP), and approved or unapproved tools. Because these are provided as open text or modular frameworks, organizations customize…
Several recognized providers and organizations offer customizable AI acceptable-use policy (AUP) templates that explicitly let you configure rules for confidential data, intellectual property (IP), and approved or unapproved tools. Because these are provided as open text or modular frameworks, organizations customize the bracketed fields or policy lists to fit their precise governance stack.
Key policy templates that build in these configurable sections include:
Would you like help drafting specific clauses for your company's policy regarding source code protection, client data, or a tier-based approved tools list?
