Pawan K. Pradhan - Navigating the New Era of AI Law

Navigating the Global Shift in AI Governance

Date: 11/18/2025

An analysis of the EU AI Act, US State Legislation, and the strategic imperative for compliance in 2025.

For the past decade, the artificial intelligence (AI) sector has operated under a tacit focus on speed and scale—often summarised by the Silicon Valley mantra, "move fast and break things." As we move through late 2025, that era has decisively ended. We have entered a new paradigm of Regulated Intelligence, defined not by potential, but by accountability.

With the European Union’s AI Act now enforcing critical milestones and a formidable "patchwork" of US state laws coming online, the legal landscape has shifted from theoretical ethics to hard compliance. For developers and business leaders, understanding this shift is no longer optional—it is a license to operate.

The New Global Standard: A Tale of Two Models

The global regulatory environment has bifurcated into two distinct philosophies:

The EU's Risk-Based Model, and
The US's Transparency & Enforcement Model.

The EU AI Act: The "Product Safety" Approach

The EU has adopted a comprehensive, omnibus approach, treating AI in a manner similar to consumer product safety. As of mid-2025, we are seeing the rollout of its tiered compliance timeline:

Prohibited Practices (Banned Feb 2025):

The "Red Lines." Systems that pose unacceptable risks are now illegal. This includes social scoring by governments, predictive policing based solely on profiling, and emotion recognition in workplaces or schools.

General Purpose AI (GPAI) Rules (Effective Aug 2025):

Providers of powerful models (like LLMs) must now adhere to strict transparency rules, including detailed summaries of training data and compliance with EU copyright law.

High-Risk Systems (Upcoming Aug 2026):

AI used in critical sectors—healthcare, employment, infrastructure, and law enforcement—will soon require conformity assessments, fundamental rights impact assessments, and rigorous data governance before hitting the market.

The E.U. AI Act, outlines 4 types of risks and classifies them.

The US Approach: The "Patchwork" & Enforcement

In the absence of a sweeping federal "US AI Act," regulation is driven by aggressive state legislatures and federal agency enforcement:

California’s Dominance:

The Transparency in Frontier AI Act (SB 53) and the AI Transparency Act (SB 942) effectively set a national floor. They mandate that developers of large models disclose safety protocols and that consumer-facing AI systems (like chatbots) carry a clear, watermarked label of artificiality.

The "Colorado Model":

The Colorado AI Act focuses on preventing algorithmic discrimination, requiring companies to perform impact assessments if their AI plays a role in "consequential decisions" like hiring or lending.

FTC Enforcement:

The Federal Trade Commission is using existing consumer protection laws to police "AI washing" and data misuse, establishing a precedent that "there is no AI exemption to the laws on the books."

The "Missing Middle": Critical Gaps in the Legislation

While these frameworks are robust, they leave significant gaps that future policy must address to balance innovation with protection.

The Open-Source Innovation Gap:

Current laws often fail to distinguish between a trillion-dollar corporation and a two-person open-source research team. We need a clearer "Research Safe Harbor" that exempts open-source maintainers from heavy compliance burdens until their models are commercially deployed.

Mandatory Labour Impact Assessments:

The current focus is heavily on safety and bias.

However, we lack a requirement for Displacement Analysis. Companies deploying enterprise-level automation should be required to disclose not just the technical risks, but the human capital risks—specifically, how they plan to reskill or transition workers displaced by these systems.

A "Right to Reality" Standard:

Labelling deepfakes is a good start, but it is insufficient.

We need a unified, cross-platform technical standard (like C2PA) that cryptographically proves media provenance, allowing users to verify the origin of a file instantly, rather than just relying on a platform's "AI-generated" tag.

Strategic Implementation: Moving Beyond "Check-the-Box"

For organizations, the goal should not be minimum viable compliance, but strategic trust.

Here is how to operationalize these requirements effectively.

NIST AI RMF

A. Adopt the NIST AI Risk Management Framework (RMF)

Do not reinvent the wheel. The NIST AI RMF is the gold standard for mapping, measuring, and managing AI risk. It aligns well with both EU and US requirements and provides a professional vocabulary for discussing risk with stakeholders.

B. Beware of "Algorithmic Disgorgement"

This is the most severe penalty in the regulator's toolkit. If you train a model on data you did not have the right to use (or data collected deceptively), regulators like the FTC can force you to delete not just the data, but the model itself.

Precedent: The FTC actions against companies like Rite Aid, Everalbum, and WW International forced the deletion of algorithms built on improperly obtained data.
Action: Maintain impeccable "Data Lineage" records. If you cannot prove where a data point came from, do not let your model learn from it.

C. The "Human-in-the-Loop" Reality

For high-stakes use cases, "human oversight" cannot be a rubber stamp. The EU AI Act requires that human reviewers have the technical competence and authority to override the AI.

Ensure your workflows empower humans to be the final decision-makers, not just passive observers.

Conclusion

The passage of these laws marks the maturing of the AI industry. While the compliance overhead is real, it offers a competitive advantage. In an era of deepfakes and "hallucinations," the companies that can prove their systems are lawful, transparent, and safe will win the most valuable asset in the digital economy: User Trust.

We are done breaking things. It is time to build things that last.