Policy development · SaaS & technology
Privacy & Security Policy Development for AI Startups & LLM App Builders
A generic privacy policy with the word 'AI' inserted once will not survive the first engineer who asks whether they can paste a customer record into a coding assistant, or the first enterprise legal team that asks to see your no-training clause in writing. We write the specific policies an LLM product needs: acceptable use, prompt and completion retention, and how a new model provider gets approved before it touches production data. Most teams start this the week an engineer asks a question nobody can answer with confidence.
Reviewed by the Privacy Horizon team · Last reviewed
What you're protecting
The policies an LLM product needs that a generic template skips
A model-backed product creates data-handling decisions a standard SaaS policy set was never written to answer.
AI acceptable-use policy
Which model providers and tools employees may use, what categories of data can go into a prompt, and what requires a specific approval, written clearly enough that an engineer can follow it without asking first every time.
Prompt and completion retention policy
How long prompt and completion logs are kept, who can access them, and when they get deleted, since these logs accumulate the same sensitive content as the rest of the product without anyone treating them that way by default.
No-training-clause policy for customer data
A documented commitment covering whether customer data is ever used to train or fine-tune a model, matched against what your actual model provider contracts say, so the policy and the contracts can't quietly drift apart.
RAG source document handling
Rules for what documents may be indexed for retrieval, how long they stay in the vector store, and how they get removed when a customer offboards or a document is withdrawn.
Model-routing and sub-processor change policy
A defined approval step before engineering swaps to a new model provider or GPU cloud, so a cost or performance decision doesn't silently become a new data flow nobody reviewed.
Regulatory map
Why these policies need to say something specific
Regulator guidance written for this category, not general privacy principles, sets the bar these policies now have to clear.
The OPC's openness and limiting-collection principles
The generative-AI principles expect organizations to be open about how AI systems use personal information and to collect no more than a feature genuinely needs, standards a vague acceptable-use policy does not meet.
PIPEDA's accountability principle
An organization has to be able to demonstrate its policies and practices, not just state that they exist, which is why a prompt-retention policy needs an enforced deletion mechanism behind it, not just a paragraph.
Law 25's documentation expectations
Quebec's incident register and privacy impact assessment requirements assume documented policies already exist to assess against, making policy development a prerequisite for the assessments Law 25 requires, not a parallel task.
The scraping joint statement's provenance question
The regulators' joint statement on scraping treats publicly accessible personal data as still protected, which means a model builder's data-sourcing policy needs to address provenance directly rather than assume public availability settles the question.
What goes wrong
What an unwritten AI policy actually costs you
Most of the damage here happens quietly, well before anyone calls it an incident.
Employees pasting data into unapproved tools
A company-wide ban on generative AI tools followed staff pasting source code into a consumer chatbot, and a written acceptable-use policy with an approved-tools list is the control that prevents the same story from repeating.
A no-training claim nobody verified
Sales or marketing stating that customer data is never used for training, without a policy tying that claim to the actual model provider contracts, creates a warranty the company may not be able to back up under scrutiny.
Retention that nobody is enforcing
Prompt logs and vector-store documents accumulate past any stated retention period once no policy assigns someone to delete them, quietly widening exposure with every month that passes.
A model swap nobody documented
Without a model-routing policy, a provider change made for cost or performance reasons can move personal information to a new sub-processor with no record of when it happened or who approved it.
Our policy development for ai startups & llm app builders
What our policy development covers for an AI company
Policies built around your actual product and model stack, not a template with the company name swapped in.
Custom policies reflecting your stack
We work with your model providers, vector store and orchestration tools to write acceptable-use, retention and vendor policies that describe how your product actually handles data.
Compliance-ready drafting
Policies drafted with PIPEDA, Law 25 and the OPC's generative-AI principles in mind, so they hold up when an enterprise customer's legal team or a regulator reads them closely.
Employee and vendor guidance
Clear rules for your own team on approved tools and data handling, alongside the vendor-facing language your model provider and vector-store contracts need to match.
Ongoing updates as the stack changes
As you add a model provider, launch a new agent feature, or expand into a new market, we help keep the policy set current instead of letting it drift from what the product actually does.
How the engagement runs
How we build the policy set with your team
Grounded in your actual data flows before a word gets drafted.
Step 1
Inventory the AI-specific data flows
We map every model provider, vector store and orchestration tool touching personal information, and identify which decisions need a written policy versus an engineering control.
Step 2
Draft the policies
Acceptable use, retention, no-training-clause and vendor policies are drafted in language your engineers and your customers' legal teams can both work with.
Step 3
Review with engineering and leadership
Draft policies are checked against what your systems actually do, so nothing gets published that your team cannot honestly follow.
Step 4
Publish, train and maintain
Policies are rolled out with a short training session, then revisited as your model stack or customer base changes, so they stay current rather than becoming shelfware.
What it costs
What shapes policy development cost for an AI startup
Cost depends on how many distinct policies are needed, how many model providers and vector stores the acceptable-use and retention policies need to cover, and how much of an existing policy set already exists to update rather than write from scratch. A single-model product needs less scoping than one running several inference vendors across multiple product lines.
This work is frequently delivered inside a Virtual Privacy Office retainer, which keeps the policies current as your model stack changes rather than treating them as a one-time deliverable. We quote standalone policy development after a short review of your current data flows.
AI Startups & LLM App Builders: Policy development questions, answered
At minimum: which model providers and tools are approved, what categories of data may or may not go into a prompt, how exceptions get requested, and what happens if the policy is violated. It should be specific enough that an engineer can follow it without escalating every routine question.
There is no single correct period; it depends on why you're keeping them, whether it's debugging, model evaluation or support, and what your customer contracts and applicable law allow. The policy needs a defined period tied to a stated purpose, plus an actual deletion mechanism, since an undefined retention period is itself a risk.
It should state plainly whether customer or user data is ever used to train or fine-tune a model, matched exactly against what your model provider contracts commit to, and it should be kept current as those contracts or providers change. A policy that overstates the commitment is worse than no policy at all.
Yes, if you fine-tune models or run structured evals, because that data usually started as production data repurposed for a new use, and it needs its own handling rules covering consent, retention and who can access it, distinct from the general prompt-retention policy.
It should require a documented review before a new model provider, GPU cloud or inference vendor goes into production, covering what data that provider will see, what its retention and training terms are, and who signs off. Without this, a cost-driven engineering decision can become an unreviewed sub-processor change.
A public-facing privacy policy tells users broadly how their data is handled; the policies here are internal operating documents that tell your own team and your model providers exactly what is and isn't allowed. Most AI startups need both, and the internal policies are what actually keeps the public one accurate.
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About this service
Answers & guides
- Do you need an AI policy before employees use ChatGPT?
- Does a small business need an AI governance framework?
- Writing an AI Acceptable-Use Policy: A Practical Walkthrough
- Can Your Team Put Customer or Patient Data Into Generative AI? Drawing the Line
- A Right-Sized AI Governance Framework for Small & Mid-Sized Businesses
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