SEB Marketing Team 

It’s official: Generative AI (GenAI) is no longer a futuristic concept—it’s a powerful productivity co-pilot running on employee desktops right now. While the speed and efficiency gains are undeniable, the operational and legal risks it introduces are severe.

This isn’t about blocking innovation; it’s about governance. Unmanaged, GenAI can turn into an open door for data leaks, legal battles, and deep-seated compliance headaches. As HR and Tech leaders, the responsibility to manage this risk falls squarely on your shoulders.

You need a policy that is robust, actionable, and ready for deployment. This isn’t a suggestion; it’s a necessity. Here are the five absolute compliance must-haves for your internal Generative AI policy.

1. Zero Tolerance for Inputting Proprietary and Sensitive Data

The fundamental risk is simple: When employees paste proprietary data, internal meeting notes, or Personally Identifiable Information (PII) into a public Large Language Model (LLM), that data can be ingested and used for future model training. This is a direct data security and Intellectual Property (IP) breach. You cannot allow it.

Your policy must contain an explicit and non-negotiable prohibition.

• The Mandate: Employees are strictly forbidden from inputting any non-public company information, client data, or PII into any non-approved GenAI tool.
• The Solution: Establish an approved, secure, enterprise-level gateway or sandbox environment. Insist that all GenAI experimentation and usage must take place within these protected walls. If a secure tool isn’t available, the data doesn’t go in. Period.

2. A Formal, Mandatory Bias Auditing Protocol

GenAI models are trained on massive, often messy, historical datasets. When these tools are integrated into critical HR processes—like screening résumés, optimizing job descriptions, or analysing performance—they can inadvertently perpetuate and amplify pre-existing systemic biases. This opens the door to costly anti-discrimination lawsuits and damages your reputation as an equitable employer.

Compliance here requires active, intentional oversight.

• The Mandate: Designate a specific Compliance/HR committee to conduct regular, formal Bias Audits on all GenAI applications used in employee-facing or evaluation processes.
• The Solution: Implement a Human Oversight rule. No significant HR or operational decision derived from GenAI output can be executed without final review and validation from a trained human decision-maker. The human must check the algorithmic math for equity.

3. Clear Disclaimers on Copyright and IP Ownership

The legal landscape around GenAI-generated content is a gray area, creating significant risk for copyright infringement. Is the output truly original? Did the underlying training data include copyrighted work? If your employee posts an AI-generated image or text that infringes on a third party, is your company is liable?

You must inoculate your organization against this uncertainty.

• The Mandate: Require employees to treat all GenAI output as non-original work unless verified. The company should not automatically claim copyright ownership over AI-generated content.
• The Requirement: Employees must be explicitly tasked with verifying the source and originality of any component they intend to publish or use externally. If in doubt about the IP source, it must be flagged for Legal review. Avoid using AI-generated content for mission-critical or highly sensitive IP creation.

4. Establishing Ultimate Human Accountability

GenAI is a tool, not an autonomous agent that can absorb legal responsibility. When a model provides incorrect, biased, or illegal output, the organization—and the specific employee who executed the command—is ultimately on the hook. You need a clear line of sight for accountability.

Don’t let the tool become the scapegoat.

• The Principle: Explicitly state in the policy that the human employee is solely and ultimately responsible for any output generated by an AI tool under their supervision. The ‘Human in the Loop’ carries the liability.
• The Process: Establish a clear chain of command and a designated committee (e.g., the AI Governance Committee) responsible for overseeing the policy’s implementation and addressing violations.

5. Mandatory Training, Transparency, and Enforcement

A perfect policy hidden in a binder is a policy failure. Compliance only works when it’s understood and enforced.

• Mandatory Training: Implement continuous, mandatory AI Literacy Training for all employees, focusing specifically on data input rules and ethical use cases. This is non-negotiable onboarding material now.
• Transparency: Employees must clearly indicate when GenAI has been used to assist in the creation of content. Transparency reduces legal risk and fosters trust.
• Enforcement: Clearly define the disciplinary ladder—from warnings to termination—for policy violations, especially those involving PII or IP leakage. Show that you are serious about compliance.

The Path Forward: Governance

Generative AI is a powerful tide lifting the entire ship of industry. But that tide also carries debris and hazards. Your role is not to fear the future, but to frame it—to build the compliance framework that ensures your organization can leverage GenAI’s power without succumbing to its pitfalls. A strong, clear, and enforced policy is your best defence.

Would you like me to create an executive summary of this policy checklist for C-suite distribution?