With the exponential advancement of large AI models, global regulatory frameworks are undergoing fundamental restructuring: moving from past "soft constraints" that were limited to principles and voluntary commitments to "hard tests" led by governments, which are proactive and evidence-based. This shift marks the entry of AI regulation into a new era of practical implementation.
1. New Normal: Who Will Conduct "Health Checkups" for AI Models?
Previously, major model developers often conducted self-assessments through internal "red team testing" or by publishing security reports. However, this "examinee grading themselves" model can no longer meet the regulatory needs at the national security level.
Currently, the UK AI Safety Institute (AISI, now renamed the AI Security Institute) and the US Department of Commerce's AI Standards and Innovation Center (CAISI, formerly the US AI Safety Institute) are leading this paradigm shift. Conducting national security assessments before model release has become an industry-recognized "entry ticket."
What Exactly Is Being Tested? The focus has shifted from broad "principle management" to specific technical red lines: Is the model likely to be used for large-scale cyberattacks? Does it lower the threshold for producing dangerous biological or chemical substances? Can it bypass security layers in critical infrastructure applications?
Who Is Undergoing Testing? As of now, global AI leaders such as Google DeepMind, Microsoft, xAI, Anthropic, and OpenAI have all reached agreements with U.S. and U.K. regulatory authorities to cooperate in safety assessments before releasing their models publicly.
2. Collaborative Efforts: Building a Global AI Regulatory "Defense Network"
The power of regulation lies not only in individual countries but also in international information recognition and resource collaboration.
UK-Australia Collaboration: On May 25, the United Kingdom and Australia formally signed a Memorandum of Understanding (MoU), deepening cooperation between the UK and Australian AI Safety Institutes (AISI) in the fields of security assessment and frontier risk research. Both sides will share insights into AI capabilities and jointly promote international best practices for testing, in response to the rapid evolution of global cybersecurity threats.
Transnational Practical Application: Such a collaborative framework means that multinational AI companies facing different market regulatory compliance requirements will face an increasingly unified "pre-release safety assessment" process. This trend is reshaping the ability to conduct safety testing from a single R&D cost into a core qualification for participating in global competition.
3. New Industry Rules: Safety Capabilities as Commercial Competitiveness
For AI startups and major model developers, changes in the regulatory environment have brought profound strategic impacts:
Pre-emptive Constraints on Product Development: The assessment process will be integrated into the model development lifecycle. The stronger the model's capabilities, the more detailed the access permissions and technical materials that the company must provide.
Value Premium of Safety Technologies: With the increasing procurement standards by governments, enterprises, and international compliance requirements, AI products with comprehensive safety protection and proven government testing capabilities will gain significant competitive advantages in the market.
From "Principle Statements" to "Actual Testing Success": Regulators are no longer focused on whether a company has written an "AI safety commitment," but rather whether it has passed real-world pressure tests conducted by professional testing institutions.
4. Conclusion: Moving Toward a More Practical Regulatory Era
The essence of AI governance lies in the dynamic balance between innovation vitality and risk control. Although the "mandatory safety assessment" model implemented by countries like the U.S. and U.K. increases the complexity and technical costs of model deployment, it builds a necessary stabilizer for the long-term development of artificial intelligence.
This regulatory approach, based on real-world issues and evidence-driven, is undoubtedly more complicated and challenging than principle statements, but it is closer to reality, laying the foundation for building a safe, controllable, and trustworthy intelligent society. For companies riding the AI wave, embracing this regulatory trend will no longer be a burden, but a necessary pass to future markets.
