Generative AI Governance

Generative AI governance is the set of policies, technical controls, vendor terms and monitoring that let an organization use tools like ChatGPT, Copilot and custom LLM applications without leaking data, publishing wrong answers or losing track of what the systems are doing. It differs from governance of earlier software because the outputs are probabilistic, the inputs are typed by anyone, the data leaves your boundary with every prompt, and the models change under you. The workable version for organizations of every size has five parts: a short usage policy, controls that enforce it, contract terms that cover data and model changes, logging you can investigate, and an incident process.

What is different about generative AI

Most governance programs were designed for deterministic software: the same input produces the same output, the data stays in a database you control, and changes arrive through a release you approved. Large language models break each of those assumptions, so a responsible AI framework for generative tools has to start from the differences.

• Outputs are probabilistic. The same prompt can produce different answers, and a fluent answer can be wrong. Testing has to be statistical, with evaluation sets and thresholds, not a single pass or fail.
• Everyone is a user and an integrator. Prompts are written by staff with no training in data handling, and any of them can paste a contract, a customer record or source code into a tool in seconds.
• Data leaves with every request. Unless the model runs inside your environment, prompts, attachments and retrieved documents go to a vendor under that vendor’s terms, which may include retention and training.
• New attack surface. Prompt injection hides instructions in a document, a web page or an email that the model then follows. When the model can take actions through tools or agents, injection becomes a way to exfiltrate data or trigger transactions.
• Provenance and ownership are unsettled. Training data rights, output ownership and the licensing status of generated code vary by vendor and are still being tested in courts.
• Models change without a release. Vendors update models, system prompts and safety filters on their own schedule, and a workflow that worked last month can degrade silently.

None of this means generative AI should be avoided. It means LLM governance has to add evaluation, data-flow controls and change monitoring to the policy and access controls you already run. The structure of a full program is in our guide to building an AI governance framework; this post covers the generative layer.

Policy

A generative AI policy should fit on one or two pages and answer the questions staff actually have. Longer documents become shelfware, and shelfware produces shadow AI. The policy should cover:

• Approved tools by data class. Public and internal data: any approved tool. Confidential data: only tools under enterprise terms with training disabled. Regulated data (health, payment, personal data under privacy law): only tools inside the compliance boundary with the right agreements in place.
• Prohibited inputs. A short list that never goes into any tool: passwords and keys, full customer records, unreleased financials, legally privileged material, anything covered by a customer NDA unless the tool is covered too.
• Output review rules. Anything sent to a customer, published, used in a decision about a person, committed to a codebase or relied on as fact should be reviewed by someone accountable for it.
• Disclosure. When AI-generated content must be labeled, and when customers must be told they are interacting with AI, which several states require in certain situations.
• Prohibited uses. Impersonation, content about real people without a business reason, bypassing security controls, and any use that would be unlawful if a human did it.
• Request and exception path. How to get a new tool or use case approved, who decides, and how fast.

Our AI acceptable use policy template is written to this outline and can be adopted by a five-person company or adapted into a corporate policy set. The policy is the same for a founder and a division of a large organization; only the approval path changes.

Technical controls

Policy without enforcement is a suggestion. The controls below are listed in the order most organizations should add them; the first four apply to almost everyone.

1. Enterprise tenants with single sign-on. Move staff from personal accounts to company-managed ones under enterprise terms. This gives you identity, offboarding, usage visibility and the contractual protections in one step.
2. Identity provider app control. Require approval for OAuth grants to AI apps and review the permissions they request. A summarizer that asks to read all mail and files should not be a one-click install.
3. Data loss prevention for AI destinations. Warn or block when classified data is pasted into AI tools, starting with regulated data. This depends on at least a basic data classification.
4. Browser and device controls. Extension allow-lists on managed browsers and gateway categories for generative AI domains, set to warn rather than block for most users.
5. A gateway for custom applications. Route your own LLM applications through a single API layer that handles authentication, logging, rate limits, model routing, secrets and redaction: one place to enforce policy and to switch vendors.
6. Access-aware retrieval. In retrieval-augmented applications, the model must only see documents the current user may see. Enforce document permissions at query time; do not rely on the model to withhold information.
7. Guardrails and filters. Input and output checks for prompt injection patterns, personal data, toxicity and off-topic requests. Filters reduce risk; they do not remove it.
8. Least privilege for agents. When a model can call tools, scope its permissions narrowly, require confirmation for consequential actions, and treat every tool result as untrusted input.
9. Evaluation sets and release gates. Keep a test set of real prompts with expected properties for every production use case, run it before release and after each model or prompt change, and define the threshold that blocks a release.
10. Red-teaming. Before launching anything customer-facing, have someone try to make it leak, misbehave or take an unintended action, and record the results.

The choice between retrieval, fine-tuning and a custom model changes which controls matter most; our guide to RAG vs fine-tuning vs custom LLMs covers that decision.

Vendor terms

Most generative AI risk is contractual before it is technical. Read the terms below in full for every tool that will see confidential or regulated data, and summarize them in your AI inventory for the rest.

Term	What to look for	Why it matters
Training on your data	Explicit statement that prompts, files and outputs are not used to train or improve models, for every plan and feature you use	Consumer and some business plans permit training by default
Retention and deletion	How long prompts, outputs and uploads are kept, whether zero-retention options exist, and how deletion is confirmed	Retained prompts are a breach and discovery exposure
Sub-processors and model providers	Which companies see your data, including the model provider behind a reseller or an app	Many AI products are thin layers over another vendor’s model
Data location and segregation	Where data is processed and stored, and whether your data is isolated from other customers	Privacy law and customer contracts often restrict location
Security attestations	Independent reports such as SOC 2, and regulated-data agreements such as a BAA or a data processing agreement	Your compliance obligations flow down to the vendor
Model change notice	Commitment to notify you before model versions, system prompts or safety settings change, with version pinning where offered	Silent changes break evaluated workflows
Output rights and indemnity	Who owns outputs, and whether the vendor indemnifies you for intellectual property claims over generated content	Determines who carries the risk of a copyright claim
Incident notification	Timeframe and content of breach and incident notices	Your own notification duties depend on hearing promptly
Exit	Data export, deletion on termination, and portability of prompts, evaluation sets and fine-tuned assets	Avoids lock-in and leaves no data behind

Keep the signed terms and the review date with the inventory record for each tool, and re-check them when the vendor changes plans or is acquired.

Logging and incidents

You cannot investigate what you did not record. For approved tools, enable the usage and audit logs the vendor offers. For custom applications, log the prompt, the retrieved context, the model and version, the output, the user, the tool calls and the latency, with personal and regulated data redacted according to your classification rules. Set a retention period for these logs; they are a new data store and should be governed like one.

Then define what an AI incident is, so people know when to report one:

• Data exposure: regulated or confidential data entered into an unapproved tool, or returned by a model to a user who should not see it.
• Harmful or wrong output acted upon: a hallucinated fact in a customer document, a wrong figure in a report, generated code that introduced a vulnerability.
• Prompt injection or agent misuse: a model that followed hidden instructions, called a tool it should not have, or was manipulated into disclosing data.
• Vendor incident or change: a breach at the provider, or a model update that degraded an evaluated workflow.
• Policy violation: a prohibited tool or input, discovered through logs, DLP or a report.

Route these through the incident process you already have, with an AI-literate reviewer added. Each incident should end with a decision: a policy change, a control change, a vendor conversation or a note that the controls held. Trustworthy AI is built from that loop more than from any single safeguard, which is also the approach the NIST AI RMF and its generative AI profile describe.

Where PS3G fits

PS3G works on both sides of generative AI. Our AI governance services write the policy, build the inventory, set up the identity, device and data controls, review vendor terms and define the logging and incident process, scoped to a small team or a corporate program and aligned with NIST AI RMF and ISO 42001. Our AI development team builds custom LLM applications, retrieval systems and agents with the gateway, access-aware retrieval, guardrails and evaluation sets described above designed in from the start. We apply the same responsible AI practices to our own software products.

Related reading

• AI Acceptable Use Policy Template for Employees (Free, Editable)
• AI Governance Framework: How to Build One (Structure + Examples)
• RAG vs Fine-Tuning vs Custom LLM: Which Approach Fits Your Use Case?
• AI governance services and AI development services from PS3G

Frequently asked questions

How is generative AI governance different from general AI governance?

General AI governance covers every automated decision system, including traditional predictive models. Generative AI governance adds controls for the specific behaviors of language models: probabilistic outputs that need statistical evaluation, prompts typed by any employee, data leaving the boundary with each request, prompt injection, unsettled content ownership and models that change without a release. The organizational structure is shared; the controls are added on top.

Is it safe to use ChatGPT or Copilot at work?

It can be, under enterprise terms that exclude training, set retention, provide single sign-on and audit logs, and are covered by the right agreements for any regulated data. Personal accounts on the same products offer none of that. The policy should name which plan and which data classes are approved, and the identity provider should make the approved account the default.

What is an LLM gateway and do we need one?

An LLM gateway is a single API layer that your own applications call instead of calling model vendors directly. It centralizes authentication, logging, redaction, rate limits, cost tracking and model routing, and it makes switching vendors a configuration change. If you have more than one custom application, or any application that handles regulated data, a gateway is usually worth it.

How do we test a generative AI feature before release?

Build an evaluation set of real prompts with the properties a good answer must have, such as grounding in approved sources, no personal data in the output and correct refusal of out-of-scope requests. Run it on every model or prompt change and set a threshold that blocks release. Add red-teaming for injection, data leakage and unintended actions before anything customer-facing goes live.

Which frameworks should a responsible AI framework for generative tools reference?

NIST AI RMF, including its generative AI profile, is the most common reference in the US and scales to any size of organization. ISO/IEC 42001 adds a certifiable management system for vendors and larger organizations. The OWASP list of top risks for LLM applications is the practical reference for application security teams. HIPAA, state privacy acts and state AI laws set the minimum requirements for regulated data and consequential decisions.