CISO Guide: How to Create a Generative AI Safe Use Policy in the Enterprise

Generative AI is no longer thefuture; it's on your teams' laptops and mobiles, composing emails, debuggingcode, and generating reports at a speed previously unthinkable. However, thisrevolution in productivity has a critical trade-off: its unchecked adoptionexposes your organization to massive data leaks, intellectual propertyvulnerabilities, and new cyberthreats that compromise its security. The sametechnology that helps your team become more efficient is also used to create sophisticated new phishing threats thatare increasingly difficult to detect.

As a CISO, CIO, or manager, yourchallenge isn't to hit the off button. The real challenge is to govern AI,not ban it. To achieve this, you need a robust generative AI policy that balances innovation with the securityand compliance required by directives such as NIS2. The goal is to establisha clear framework for use for generative AI before human error becomes acorporate crisis.

This guide is not a theoretical treatise.We provide you with a practical, actionable framework so you can designand implement a safe use policy that works. The goal is clear: to protect yourorganization's critical assets without holding back the incredible potentialthat this technology offers.

The 3 Pillars of a RobustGenerative AI Policy

An effective generative AI policy is not a 200-page document that no one reads. It is based on threefundamental, clear and enforceable principles that form the basis of a  solid security and governancestrategy.

1. Governance and Access Control

Not all AI applications are createdequal, and not all staff need access to the full feature. The firststep is to abandon the idea that AI is a one-size-fits-all tool. Yourpolicy should clearly differentiate between public versions (which can use yourdata for training) and enterprise solutions that ensure data privacy bycontract.

It establishes a list of approved toolsand defines access levels based on the role and the actual need to handlesensitive data. Your policy should answer key questions: Can themarketing team use a public tool to generate slogan ideas? Probably yes. Canthe finance department use that same tool to analyze quarterly results?Absolutely not.

2. Data Security and PromptHygiene

This is, without a doubt, the mostcritical pillar and the most vulnerable point in data leak prevention. Prompt hygiene is the fundamentalprinciple: never enter sensitive, confidential or personal information into apublic AI model. It is a non-negotiable rule. A  poorly formulated prompt can turn intoirreversible data exfiltration, as that information could be used to train themodel. Prompt management  is so vital to securitythat it directly aligns with risks identified by experts, such as those detailedin the OWASP Top 10 for Large Language Model Applicationsproject.

Your policy should be explicit about whatinformation is strictly prohibited in unapproved tools:

●     Customer or employee personaldata (subject to GDPR).

●     Intellectual property, trade secrets, or patents in development.

●     Proprietary source codesnippets or infrastructure details.

●     Business plans, marketing strategies, or non-public financial data.

●     Any information classified as "Confidential"or higher in your data policy.

3. Liability and Acceptable Use

AI is a tool, not a freelance employee. The userTheuser is ultimately responsible for the content they generate and use, notartificial intelligence. Policy should make clear this principle ofresponsibility on generative AI. The output of AI is a draft, not anabsolute truth.

In addition, it must explicitly prohibitthe use of generative AI for any activity that is illegal, unethical, orviolates the company's code of conduct. Equally important is to require that all AI-generated information be verified for accuracy, potential bias, andoriginality before being used in any deliverable, decision, or officialcommunication. Blindly trusting AI is not an option.

Key Elements Your AI PolicyShould Include (Practical Checklist )

To materialize the strategy, this checklistdetails the components that your policy must include to be complete and,above all, applicable in the day-to-day of your organization.

Definition and Scope

Avoid ambiguity from the front line.Clearly define what you mean by "Generative AI" in the context of thepolicy (text, code, image generation tools, etc.) and specify without a doubtwho it applies to: all employees, managers, temporary staff, and contractorswith access to the company's systems and data.

List of Approved and ProhibitedTools

Ambiguity is the main enemy ofsecurity. Create a live list, managed by the IT orSecurity team, that is easy to consult. Be explicit; for example:

●     Approved: ChatGPT Enterprise (with DPA and no dataretention settings).

●     Forbidden: Public or free versions of any LLM to process, analyze, or generatecontent based on corporate data.

Data Classification Rules in Prompts

This is where your AI policy integrateswith your data governance framework. The rule should be simple andstraightforward: the classification of the data determines which tool it canbe used in. For example: "Data classified as 'Public' can be used onapproved platforms. 'Internal' or 'Sensitive' data is strictly prohibited in any public solution." Knowing the sectors most vulnerable to data leakage by AI reinforces the need to be inflexible with this standard.

Intellectual Property andCopyright

He establishes two key points. First,that the output generated by an employee using corporate resources andtools is the intellectual property of the company. Second, it warnsabout the risk of infringing on the copyrights of others. Using AI-generatedcontent does not relieve the employee of the responsibility to verify itsoriginality and ensure that it does not violate the intellectual propertyof others.

Consequences of Non-Compliance

A policy without consequences is just asuggestion. It details that the violation of these rules will be treated withthe same seriousness as any other security breach, aligning disciplinarymeasures with the company's code of conduct. Setting these rules now not onlyprotects the organization, but also prepares it for regulatory frameworks sush as the future EU Artificial Intelligence Act.

Implementation: From Policy toSafety Culture

A document, no matter how perfect, doesnot change behaviors on its own. Implementation is the critical phase thatdetermines whether your guidelines are effectively adopted or ignored.

Communication and ContinuousTraining

Posting the policy on the intranetand expecting it to be enforced is not a strategy. Launch a communicationcampaign that not only details the rules, but also explains the "why"of each one to achieve the involvement of the teams. The goal is totransform politics into a true culture of safety. To do this, integratespecific modules on the risks and correct use of generative AI withinyour AI awareness and training programs.

Monitoring and Control Tools

The formation is the first line ofdefense, but it needs technical support. It implements technological systemssuch as DLP (Data Loss Prevention) and CASB (Cloud Access SecurityBroker) solutions  to monitor theuse of AI applications, detect anomalous data transfers, and block unauthorizedplatforms. This layer of AI monitoring gives you the visibility you needto ensure that the policy is complied with in practice, proactively protectingthe business.