The former US Securities and Exchange Commission (SEC) commissioner and current CEO of an artificial intelligence (AI) company recently highlighted the need for careful planning and implementation when building an AI-enabled future. Expressing optimism about the potential for AI transformation, the executives emphasized that the development and deployment of AI systems must be supported by a robust framework to ensure safety, transparency and accountability. This is in line with broader regulatory efforts in the US and Europe, where governments seek to balance innovation with ethical and legal protection measures.
Recent developments in AI regulation in the United States have shown growing consensus across political boundaries on specific core principles. Both the Biden and Trump administrations have issued executive orders establishing baseline requirements for the use of highly influential AI systems by federal agencies. These directives highlight the need for systematic governance, transparency and human surveillance in the deployment of AI. For example, OMB memos from both administrators require that federal agencies, particularly AI use cases that affect rights or safety, be identified and stocked, and minimum practices such as impact assessments and human review mechanisms are implemented before deploying AI systems (2). These efforts reflect a common perception that AI needs to be used responsibly, particularly in high-stakes domains such as healthcare, employment and critical infrastructure.
However, notable differences remain among the administrations on key issues such as fairness and individual measures. The Biden administration’s approach includes aggressive measures to ease algorithm bias and support fairness, but the Trump administration’s revised guidelines limit such protections to illegal discrimination, as defined by existing law. The Trump administration has also removed individual requirements when AI systems affect decisions. Changes that critics argue reduce transparency and individual rights (2). These diverse priorities highlight the ongoing tensions between risk-based and rights-based regulatory models in AI governance.
In parallel, the European Union has been firmly stanced in shaping global AI regulations, along with the implementation of the world’s first comprehensive AI law, the AI Act. The AI Act imposes strict guardrails on high-risk AI applications, including mass surveillance technologies and requirements for transparency and accountability in automated decision-making processes. This regulatory approach has elicited both support and resistance from US technology companies and policymakers. Companies such as Google, Microsoft and Openai are working on the EU framework to align AI practices with European standards, while others such as Meta have criticized AI practices as being overly restrictive (3). The impact of EU regulations, often referred to as the “Brussels Effect,” could set global norms, enforcing US companies operating in the European market to comply with strict data and AI governance standards regardless of changes in domestic policy (3).
Despite the Trump administration’s push for deregulation to promote our competitiveness in AI, international regulatory trends suggest that strict safeguards are becoming increasingly normative. The global landscape witnesses convergence towards inclusive AI governance as countries such as South Korea, Canada and Australia develop their own regulatory frameworks inspired by the EU model (3). This trend challenges the notion that deregulation alone can ensure global AI domination, as market access for major economies is increasingly dependent on compliance with international standards.
For organizations looking to navigate this complex regulatory environment, tools with AI have emerged as they are essential for compliance monitoring. Modern AI systems automate detection of non-compliant behavior across communication channels, flagging in real time unauthorized promises, data processing issues and policy violations. These tools also provide predictive capabilities, allowing organizations to predict and mitigate risk before they escalate. However, the effectiveness of these solutions depends on integration with existing workflows, transparency in decision-making, and secure data processing practices (4).
As AI continues to redefine industry and governance, the importance of strategic planning, ethical oversight and regulatory alignment will only grow. The balance between innovation and responsibility determines not only the success of individual companies, but also the global trajectory of AI development.
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(1) AI FAQ Series | AI Regulations: Are there any regulations relating to AI? (https://www.orrick.com/en/insights/2025/08/ai-Regulation-ARE-THERE-REGULASSION-ON-AI-AI-FAQ-Series)
(2) Five points of a bipartisan agreement on how to regulate AI (https://www.brookings.edu/articles/five-points-of-bive-agreement-on-how-to-regulate-ai/)
(3) Trump administration AI policy suspended by the EU (https://insidetelecom.com/trump-administration-ai-policy-being-by-the-the-eu/)
(4) AI for compliance monitoring: A practical guide to staying… (https://www.eesel.ai/blog/ai-for-compliance-moniting)
