America’s AI Action Plan: What Enterprise AI Leaders Need to Know About Safety

In July 2025, the White House unveiled the America’s AI Action Plan, a policy pivot that emphasizes speed, deregulation, and global competitiveness in artificial intelligence. At first glance, the Plan offers a pro-innovation blueprint. But for enterprise AI leaders, this deregulation-heavy strategy doesn’t eliminate the need for safety. It amplifies it.

With the federal government stepping back from key oversight mechanisms, the responsibility for AI safety and governance now falls squarely on enterprise builders. This post breaks down what the Action Plan entails, the risks it creates for enterprise environments, and how product executives, CISOs, and AI/ML leads can respond with actionable safety strategies.

A Brief Overview of the Action Plan

America’s AI Action Plan is built around three pillars:

1. Accelerating AI Innovation
   The plan removes regulatory friction, rescinds previous guidance perceived as burdensome, and instructs agencies to eliminate requirements related to misinformation mitigation, Diversity, Equity, and Inclusion (DEI) objectives, and environmental safeguards.
2. Building National AI Infrastructure
   The government will fast-track permitting for data centers and fabs, expand the energy grid, and streamline cybersecurity reviews in order to reduce deployment delays.
3. Leading in AI Diplomacy and Security
   Export incentives for full-stack AI systems will be expanded, particularly to allied nations. At the same time, national frameworks like NIST’s AI Risk Management Framework will be revised to exclude politicized language and shift away from proactive safety auditing.

On the surface, this may look like a green light for rapid AI scaling. But beneath that, the federal retreat from safety standards creates a vacuum that enterprise teams must now fill.

What’s Changing, and Why It Matters for Safety

1. Dismantling Centralized Guardrails

By rolling back Executive Order 14110 (issued in 2023) and rewriting guidance from the National Institute of Standards and Technology (NIST) and the Office of Science and Technology Policy (OSTP), the Plan eliminates federal expectations for:

• Red-teaming before deployment
• Bias and fairness auditing
• Transparency reporting
• Misinformation detection protocols

That means no more national baseline. But enterprise models will still be expected to behave safely, avoid hallucinations, and protect users, especially in regulated sectors like finance, healthcare, and education.

Implication: There’s no longer a national baseline. If your LLM fails in the wild, the liability, reputational fallout, and regulatory exposure fall entirely on you.

2. Faster Infrastructure, Higher Stakes

Fast-tracked permitting for data centers, relaxed compliance for energy infrastructure, and reduced cybersecurity bottlenecks might accelerate your go-to-market plans, but they also eliminate the friction points where safety used to be stress-tested.

Implication: Infrastructure speed doesn’t excuse model immaturity. Accelerated deployment means models must be hardened before scaling, especially when oversight is no longer externally mandated.

3. Rewriting the Procurement Rulebook

The Plan introduces new language for federal contracts, rewarding “objective” AI systems and discouraging models perceived as being “ideologically biased.” This could disadvantage LLMs tuned to mitigate harmful or false content, particularly if those mitigations were trained on red team feedback or safety-aligned instruction tuning.

Implication: Even as procurement criteria evolve, customers still expect models to be safe, inclusive, and aligned. Companies that cut safety corners to meet short-term procurement checklists risk long-term credibility and adoption.

How Enterprise Leaders Should Respond: A Safety-First Playbook

With federal oversight fading, enterprise AI teams must establish their own internal safety scaffolding, built to scale and ready for scrutiny. Here’s how.

1. Make Red-Teaming an Internal Mandate

• Run continuous adversarial testing across abuse areas: prompt injections, jailbreaks, and emergent capabilities
• Red-team across diverse language, cultural, and user personas to detect fairness or bias blind spots
• Simulate real-world attacks, misuse, and edge cases before every release

If you’re deploying without red-teaming, you’re flying blind. No federal law will catch the failures for you.

2. Operationalize Model Observability

• Log and surface hallucination rates, response drift, and offensive content triggers in production
• Implement real-time dashboards with human-in-the-loop review pipelines
• Track input/output safety violations, even in post-deployment, to support auditing and retraining

Observability is the new audit trail. In a deregulated environment, it’s your best defense and your only early warning system.

3. Build and Enforce Internal Governance Frameworks

• Define acceptable use criteria, alignment goals, and escalation procedures for AI incidents
• Create internal AI risk boards that operate independently of product shipping pressures
• Document all evaluations, model changes, and known failure modes in preparation for future regulatory or legal scrutiny

With national frameworks now weakened, enterprise AI teams must build their own internal “NIST.” Start with existing frameworks, including NIST’s AI Risk Management Framework, OWASP Top 10 Risk and Mitigations, and MITRE ATLAS, and go further.

4. Invest in Third-Party Safety Tooling

• Leverage vendors for red-teaming automation, toxic content classifiers, explainability tools, and synthetic data simulations
• Use third-party audits to strengthen procurement bids and assure global customers
• Choose tooling that integrates directly into your model evaluation, tuning, and deployment workflows

Safety isn’t a checkbox. It’s an ecosystem. And with Washington stepping back, the private sector must fill the gap.

 

Staying Competitive Without Sacrificing Security

Some enterprise leaders may interpret the Action Plan’s rollback of DEI or misinformation guidance as a green light to relax safety constraints. But that would be a strategic error.

Here’s why:

• Global buyers, investors, and civil society groups still demand AI systems that are safe, fair, and explainable
• States and international regulators may enforce their own laws (e.g., EU AI Act), requiring safety regardless of U.S. policy
• Customers and employees expect companies to demonstrate responsibility, especially when deploying foundational models with open-ended use cases

Enterprises that move fast and build internal safety cultures will outlast those that cut corners in response to political tailwinds.

Final Thoughts: A New Phase of Enterprise Responsibility

America’s AI Action Plan marks a significant transition in national AI strategy. By removing federal guardrails and shifting emphasis toward speed and scale, the Plan places the burden of responsible development on enterprise AI builders.

That doesn’t diminish the importance of safety, it actually makes it more urgent. In the absence of centralized oversight, enterprise teams are now the front line for risk mitigation, model accountability, and public trust.

This is not the time to scale recklessly. It’s the moment to strengthen internal safety frameworks, invest in evaluation and observability, and build AI systems that can withstand real-world scrutiny, regardless of where the regulatory pendulum swings next.

At ActiveFence, we help enterprises meet this moment by providing the infrastructure for AI safety at scale. Our tools support red teaming, content risk detection, and policy-aligned observability, empowering teams to build and deploy GenAI systems responsibly, even as national standards recede.