In the fast-paced world of startups, AI adoption is skyrocketing, with 66% of founders reporting that AI tools have accelerated their pace of startup building. However, many early-stage companies overlook the importance of establishing a clear AI policy, assuming compliance is a concern only for large corporations.
To break down the essentials, we spoke with legal and AI policy experts Netanella Treistman and Roy Keidar about practical steps startups can take to create an AI policy that is both innovative and legally sound.
Their advice? Don’t wait. Here’s a simple three-step approach to get ahead of AI policy challenges before they snowball.
Step 1: Map Out Your AI Tools and Usage
According to Netanella, the first step is to comprehensively map out all AI tools used within the company. This includes both in-house AI development and third-party tools used for various business functions.
“Startups need to document what AI tools they’re using, the purpose of each tool, what data is being fed into them, and what outputs they generate,” says Netanella. “Additionally, companies need to consider whether they require IP rights for any deliverables.”
One of the biggest mistakes startups make is underestimating how widespread AI tool usage is within their teams. Often, employees—especially in HR, marketing, and product teams—use AI without formal approval.
When asked who should be responsible for this mapping, Netanella suggests that while legal counsel can provide valuable guidance, the responsibility should fall on an internal stakeholder.
“There’s no need for a lawyer to handle this directly. Typically, it should be managed by the compliance officer, CISO (if one exists), or another designated project manager who ensures alignment across departments.”
Step 2: Conduct a Risk Analysis
Once AI tools are mapped out, the next step is to conduct a thorough risk analysis. According to Roy Keidar, this means identifying potential legal, ethical, and operational risks.
“Companies need to surface the risks associated with AI use, including IP ownership concerns, privacy risks, liability issues, and data leakage,” explains Roy. “The goal is to understand how AI could impact the company’s operations and ensure these risks are addressed before they become problems.”
For this process, external legal advisors can provide valuable insights, but Roy stresses that the startup’s internal leadership must play a key role.
“We guide our clients through risk identification, but ultimately, the company knows its own operations best. The CISO, general counsel, or a member of the executive leadership team should spearhead the risk assessment.”
Step 3: Develop a Risk Mitigation Plan
The final step is to create a risk mitigation policy tailored to the company’s needs. This involves balancing AI’s benefits against potential risks and implementing safeguards to minimize exposure.
“At the end of the day, companies need to ask themselves: Is the value we’re getting from AI worth the risks?” says Netanella. “This might involve selecting alternative tools, setting stricter internal policies, or ensuring contractual protections.”
In many cases, multiple departments—including product, legal, and security—must collaborate to ensure AI tools can be integrated safely and effectively.
“This is where legal teams often have the most work,” Netanella adds. “They need to review terms of service, integration possibilities, and any implications for the company’s rights.”
Ultimately, the company must decide what level of risk it is willing to accept, balancing compliance with business growth.
Conclusion: Start Now, Iterate Later
For startups, the key takeaway is simple: don’t wait until AI policy becomes a problem. Start with a basic framework, map your tools, analyze risks, and implement safeguards. AI policy doesn’t need to be perfect from the start—but ignoring it entirely is a mistake no startup can afford to make.
As Roy puts it: “AI policy should evolve alongside your business. It’s not about perfection—it’s about having a clear, adaptable approach from day one.”
