AI Compliance Policy in the US: The 2025 Essential Guide

Introduction: What is AI Compliance and Why It Matters in 2025

Artificial intelligence (AI) is no longer on the horizon; it is a foundational part of modern business operations. As organizations across the United States deploy AI to drive innovation and efficiency, they face a complex and rapidly maturing regulatory landscape.

AI compliance is the essential practice of ensuring that these intelligent systems adhere to all applicable laws, regulations, and ethical standards. It is a framework for governance that addresses the risks inherent in AI, from data privacy and security vulnerabilities to algorithmic bias and a lack of transparency. In 2025, a proactive stance on AI compliance is not merely a legal formality but a strategic imperative. For any organization developing, deploying, or utilizing AI systems, a robust compliance strategy is crucial for mitigating significant legal and financial risks, avoiding reputational damage, and building trust with customers and stakeholders.

As enforcement actions become more common, demonstrating transparency, fairness, safety, and accountability is now table stakes.

The US AI Regulatory Landscape

Unlike the European Union's comprehensive, horizontal **AI Act**, the United States has not adopted a single, overarching federal law to govern artificial intelligence.

Instead, the U.S. employs a fragmented, sector-specific approach, relying on a combination of new executive actions, existing legal authorities, state-level legislation, and influential federal guidance.

Federal Actions and Guidance

A significant development in 2025 was a new executive order which revoked the Executive Order 14110 on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (the “Biden Order”), signed by former President Biden in October 2023.

The new policy was called Removing Barriers to American Leadership in Artificial Intelligence, issued by President Trump on January 23, 2025 (the “Trump Order”), and it emphasizes enhancing America's global dominance in AI by reducing regulatory barriers to foster innovation.

Despite this high-level policy shift, several key federal elements remain central to the compliance landscape:

• Existing Laws Applied to AI: Federal agencies, most notably the Federal Trade Commission (FTC) and the Equal Employment Opportunity Commission (EEOC), are actively applying their existing authority to the AI domain. The FTC is leveraging its power under Section 5 of the FTC Act to police unfair or deceptive practices, targeting exaggerated or unsubstantiated claims about AI capabilities. The EEOC continues to affirm that existing anti-discrimination laws, such as Title VII of the Civil Rights Act, apply to AI-driven employment decisions, even as specific guidance documents from the previous administration have been rescinded.

• The NIST AI Risk Management Framework (RMF): The AI RMF from the National Institute of Standards and Technology remains a highly influential, voluntary framework. It provides a structured approach for organizations to govern, map, measure, and manage AI risks. While not a law itself, the NIST AI RMF is widely considered a best practice and is referenced in various legislative proposals and agency guidance, making it a cornerstone of any effective AI governance program.

The Rise of State-Level Legislation

In the absence of a comprehensive federal AI law, states have become the primary drivers of AI regulation. This has created a complex patchwork of rules that companies must navigate. States like Colorado, California, New York, and Utah have enacted their own laws addressing specific AI risks. The Colorado AI Act, set to take effect in 2026, is particularly noteworthy for its comprehensive, risk-based approach similar in some ways to the EU AI Act. Other states have introduced hundreds of AI-related bills, covering everything from transparency in AI-generated content to bias audits for hiring tools.

Sector-Specific Regulations

Beyond broad state laws, organizations must comply with stringent regulations within their specific industries.

• Financial Services: AI models used for credit scoring or risk assessment must adhere to laws like the Fair Credit Reporting Act (FCRA).

• Healthcare: AI used in medical diagnostics or for patient communications is subject to regulations from the FDA and laws like HIPAA. California, for instance, has specific rules for AI use in healthcare utilization reviews.

• Employment and Human Resources: The use of AI in hiring and employee management is a major focus, with laws like NYC's Local Law 144 requiring bias audits of automated employment decision tools.

Key Areas of Focus in US AI Compliance

Across this varied landscape, several core principles form the foundation of AI compliance efforts. Organizations must focus on these key pillars to build a responsible and defensible AI governance strategy.

• Data Privacy and Security: AI systems are data-intensive, making data privacy and security foundational to compliance. Adherence to existing data privacy laws like the California Consumer Privacy Act (CCPA) is essential, and these protections extend to data processed by AI.

• Algorithmic Bias and Discrimination: Ensuring fairness and preventing discrimination is a top regulatory priority. AI models trained on biased data can perpetuate and amplify societal biases in critical areas like hiring, lending, and housing. Regular bias testing and impact assessments are becoming standard requirements.

• Transparency and Explainability: Regulators and consumers are demanding greater transparency in how AI systems work. Organizations must be able to explain how their AI models make decisions, especially for high-stakes applications. This includes providing clear disclosures to users when they are interacting with AI.

• Human Oversight and Accountability: Maintaining meaningful human oversight is a critical safeguard. AI should be a tool to assist, not replace, human judgment. Establishing clear lines of accountability for the outcomes of AI systems is a core principle of governance frameworks.

• Security: AI systems themselves present new security vulnerabilities, from data poisoning to model inversion attacks. A core part of compliance is ensuring that AI systems are resilient and secure from both internal and external threats.

Strategies for Robust AI Compliance

Navigating the complexities of AI regulation requires a proactive and structured approach.

• Stay Informed: The regulatory environment is evolving at an accelerated pace. Organizations must continuously monitor federal and state-level legal developments to adapt their compliance strategies accordingly.

• Implement an AI Governance Framework: Establish a formal, internal governance framework that defines roles, responsibilities, and accountability across the entire AI lifecycle. This framework should be guided by principles from established standards like the NIST AI Risk Management Framework.

• Leverage AI Compliance Tools: Managing compliance across numerous models and evolving regulations is a significant challenge. AI compliance platforms provide essential infrastructure for responsible AI governance. This is what we built at NeuralTrust. Our platform automates the monitoring of AI models for performance and bias, streamlines the creation of model documentation, and provides a centralized registry to manage your entire AI ecosystem, ensuring compliance is an integrated part of the AI lifecycle.

• Conduct Regular Audits and Assessments: Before deploying a high-risk AI system, conduct a thorough impact assessment to identify and mitigate potential risks. Regularly audit AI systems for bias, fairness, and performance drift to ensure they continue to operate as intended.

• Foster a Culture of Compliance: Embed a culture of responsible AI development and use throughout the organization. This includes providing robust training for all personnel involved in the AI lifecycle, from data scientists and developers to the business users who rely on AI-driven insights.

What is the Difference Between US and EU AI Regulation?

The primary difference lies in their fundamental approach. The EU AI Act is a comprehensive, risk-based law that creates a single set of rules for the entire EU market. It categorizes AI systems into risk tiers (unacceptable, high, limited, minimal) and imposes obligations based on that level. Its focus is on protecting fundamental rights and safety. In contrast, the US approach is a combination of sector-specific rules and state laws, with no single federal AI law. The overarching federal policy in 2025 is geared more toward fostering innovation and maintaining a competitive edge, leaving much of the specific rulemaking to individual agencies and states.

Does the EU AI Act Apply to US Companies?

Yes, the EU AI Act has a significant extraterritorial reach. A US-based company can be subject to the EU AI Act if it:

• Places an AI system on the market in the EU.
• Provides an AI system whose output is used within the EU. This means a US company offering an AI-powered service accessible to users in Europe, or a recruitment tool used for jobs in an EU member state, must comply with the Act’s requirements. Given the substantial penalties for non-compliance, US companies with any EU exposure must assess their obligations carefully.

Conclusion: Proactive Compliance is the Path Forward

The AI regulatory landscape in the United States is dynamic and complex. While the federal government currently favors an innovation-first approach, a growing web of state laws and the enforcement of existing regulations by federal agencies create a clear mandate for responsible governance. Proactively embracing compliance is not a barrier to innovation; it is an enabler of trust, a mitigator of risk, and a critical component of a sustainable AI strategy.

Building a robust AI compliance program requires commitment, expertise, and the right tools to manage the lifecycle of your AI systems. As regulations become more defined and enforcement increases, organizations that embed governance, transparency, and fairness into their AI operations will be best positioned for long-term success. Getting started can be the most challenging part of the journey.

To understand how NeuralTrust can help you build a robust and scalable AI compliance framework tailored to your needs, you can request a demo today.