Growth-Stage Companies Need AI Compliance Framework That Scales

Early-stage companies face constant pressure to innovate, increase efficiency, deliver products, and onboard new customers, and artificial intelligence offers opportunities across the board. However, due to the focus on achieving key business objectives, AI governance initiatives often move to the back burner.

The problem is that regulators, customers, and investors are no longer willing to wait. They expect startups to demonstrate responsible AI governance now—not just after the next funding milestone.

Growth-stage companies should adopt a stage-appropriate AI compliance framework centered on governance, intellectual property, and liability management. If done well, this can reduce the cost of retrofitting controls later, increase customer confidence, and strengthen investor diligence outcomes.

Building the program now, scaling it prudently, and treating compliance as an enabler of growth rather than a brake on innovation can also serve as a differentiator in a crowded marketplace.

Governance

Data and AI governance programs help ensure the responsible, safe, and trustworthy adoption of AI. Growth-stage companies can begin this process by mapping out the data the organization collects and processes, as well as the scenarios in which it uses AI tools.

The map should distinguish between categories of data, identify the data’s source(s), list AI tools in use, and indicate where AI is processing the data. It should also identify the consent, licensing, and use obligations for each category of data and AI tool.

Based on this map, businesses should identify and track the risks associated with their data and AI use cases. These risks may include bias, security threats, and lack of transparency. Businesses should consider factors such as whether the AI tool makes autonomous decisions, processes personal data, or operates in a regulated industry.

Companies should then implement policies, procedures, and employee training to ensure that data and AI are only used in a manner that complies with applicable law and mitigates identified risks. This may include:

• Embedding privacy by design principles into product development
• Applying data minimization principles
• Adding a human in the loop to review AI outputs
• Implementing data retention schedules
• Adding checkpoints into the product development process

Organizations should also designate a governance owner—which could be a single individual or a cross-functional committee—with authority to set policy, enforce standards, and escalate issues.

Because AI can fail in novel ways, startups should adopt an incident response plan covering incident identification, escalation, notifications, and remediation.

Intellectual Property

The IP-related questions surrounding AI are among the most significant issues startups face today, and it’s important for early-stage companies to assess them as part of their AI governance programs. They should pay attention to two key issues in particular.

First, the US Copyright Act requires works to be authored by a human being, and works produced entirely by AI systems without sufficient human creative control over the expressive elements aren’t eligible for copyright registration. Even though vibe coding and using AI tools to assist with product development are commonplace, these practices present material risks for growth-stage companies.

To mitigate risks and increase the chance of obtaining copyright protection, workflows should be structured so that humans contribute genuine creative judgment, and contributions should be memorialized for the company’s core IP assets. Managing how and where AI tools are being used to develop materials can help ensure protectability.

Second, using data to train AI models without a clear understanding of the data’s provenance and the applicable restrictions can expose startups to claims under both law and contract. Companies developing AI tools should implement processes to ensure they obtain the necessary rights and licenses to use data for model training.

Similarly, when negotiating vendor agreements for third-party AI platforms, companies should be aware of the licenses being granted, training opt‑outs, data retention, and sublicensing terms to prevent improper exposure of proprietary data and trade secrets.

Liability Management

Liability management is a critical component of addressing AI-related risks. This process is multifaceted and implicates both regulatory and contractual considerations.

From a regulatory perspective, startups must assess whether an AI system is autonomous, provides recommendations for human action, or simply augments human decision-making. Businesses should also consider whether the tool is being used in a regulated context, such as health, finance, or employment.

These classifications will help determine the applicable regulatory obligations, including comprehensive AI laws adopted in several jurisdictions. Meeting the legal obligations for oversight, explainability, transparency, and accountability is critical in reducing potential liability.

Growth-stage companies must also address potential liability via contract when procuring and licensing AI tools. They should draft the representations, warranties, limitations, and indemnities with specificity to AI-related risks, as the provisions typically included in software-as-a-service agreements may not fully address the potential exposure. Moreover, the AI governance program should require that technical stakeholders and counsel review each contract to ensure these issues are addressed in a consistent and compliant manner.

Why Act Now?

Governance is most effective when embedded into existing workflows rather than layered on as an afterthought. The goal is to surface issues early when they are less expensive and easier to address. Retrofitting compliance after a product has been built is exponentially more expensive than building governance into the development life cycle from the outset.

For startups, three practical steps can translate these principles into operational reality. First, designate a governance owner who will be accountable for AI governance as the company scales.

Second, establish documented policies addressing data governance, IP management, and liability allocation. Review and update the protocols regularly in connection with product launches and changes in operations.

Third, integrate compliance checkpoints into the product development life cycle at key stages (such as data sourcing, model training, pre-deployment testing, and post-deployment monitoring) to identify issues before they compound.

Ultimately, early governance structure isn’t just a compliance move; it’s foundational to enterprise credibility and long-term differentiation in competitive markets. It doesn’t require a new department or significant capital investment, and it can be accomplished by establishing a clear framework and documented processes that can grow with the company.

This article does not necessarily reflect the opinion of Bloomberg Industry Group, Inc., the publisher of Bloomberg Law, Bloomberg Tax, and Bloomberg Government, or its owners.

Author Information

Emily T. Strack and Chris Sloan are shareholders at Baker Donelson and co-chairs of the firm’s emerging companies and venture capital team.

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