Laws on artificial intelligence are evolving fast. This is because governments are racing to shape regulatory AI frameworks that balance innovation with safety. In 2024, 78% of organizations reported using AI in some way, which is a big jump from 55% the year before. And with $109.1 billion in U.S. private investment alone, AI isn’t slowing down anytime soon.
If you’re working with AI, investing in it, or simply trying to understand its implications for your business, you probably have many questions. Are the current rules enough? What role will frameworks like the EU AI Act play in shaping the future of AI globally?
In this article, we will walk through where regulations stand now, so you can ensure you are prepared.
The key topics to know in AI regulation
- Risk-Based Frameworks
- Transparency & Explainability
- Human Oversight and Accountability
- Ethical Fairness & Non-Discrimination
- Redress, Contestability & User Rights
- Innovation Support vs Regulatory Rigidity
Before diving into specific laws, it’s helpful to understand what most AI regulations, like the EU AI Act, are trying to achieve. While different regions take different routes, the core ideas are usually the same. Here’s a quick look at the most common priorities in AI regulation:
Risk-based frameworks
Regulatory AI rules depend on how much harm the AI system could cause. The riskier it is (like AI deciding on loans), the stricter the rules.
Transparency and explainability
People need to understand when AI is involved and how decisions are made, especially in sensitive areas like credit scoring or fraud prevention.
Human oversight and accountability
High-risk systems need a real person who can monitor, step in, or shut things down if needed.
Fairness and bias prevention
AI must be trained and tested to avoid discrimination, for example, in lending decisions or hiring platforms.
User rights and redress
Users should be able to contest decisions and know how to report issues if something goes wrong.
Room for innovation
Most frameworks aim to avoid overregulation and include sandboxes or testing programs to help developers stay compliant without slowing down growth.
1. Why do we need artificial intelligence regulation?
Regulating artificial intelligence is about making sure we’re moving in the right direction. As AI becomes more powerful and widely used, the risks grow too. Without clear rules, it’s easy for things to go sideways, especially in areas like finance or wealth management.
- Potential risks & harms of AI
With AI, there are very real risks at play. One of the biggest is bias. If an AI system is trained on incomplete or skewed data (which happens more often than you’d think), it can make unfair decisions. This can show up in job applications, loan approvals, or payment blockings. And in many cases, users have no idea why the decision was made or how to dispute it.
Privacy is another major concern. AI systems handle huge amounts of sensitive financial data, from personal identity details to transaction histories. Without strong laws on artificial intelligence, there is a real danger that this data could be misused or exposed, especially with third-party services involved in payments or lending.
The last one we will mention is the threat of AI-powered fraud. Deepfakes and synthetic identities are already being used to bypass onboarding checks or fake loan applications. Unfortunately, nowadays it’s hard to tell what’s real and what’s not.
- Balancing innovation and safety in AI
AI is evolving faster than most industries can manage. You don’t want artificial intelligence regulation to become a blocker for smart fraud detection tools, personalized financial advice, or real-time payment decisions. At the same time, there’s a clear need for rules that protect customer data, ensure fairness, and prevent fraud. Especially when algorithms can directly impact someone’s credit, access to loans, or ability to open an account.
The EU AI Act is a good example of how regulation can be tailored to risk instead of being one-size-fits-all. It focuses on more risky use cases, like biometric ID checks or credit scoring, where the stakes are higher and the impact on individuals is more serious.
Getting this balance right isn’t easy. But when done well, thoughtful regulation can actually build trust and open the door to more adoption.
2. The EU AI Act as a global pioneer
The EU AI Act is the major piece of legislation that directly targets artificial intelligence. It is designed to create a unified legal framework across all EU countries, offering clear rules for the development and use of AI.
At the heart of the act is a risk-based classification system. Instead of trying to regulate all AI systems the same way, it focuses on where the biggest risks are. For example, if you use AI to evaluate loan applications or detect fraud, those tools likely fall under the high-risk category, which means you’ll need to meet much stricter requirements for transparency, accuracy, and human oversight.
Meanwhile, lower-risk AI systems are allowed with lighter obligations, as long as users are aware they’re interacting with AI.
- Obligations for high‑risk AI systems
Not all AI tools fall under strict regulation. But when they do, it’s usually because they’re considered high-risk. These are systems that could seriously impact people’s rights, safety, or access to essential services like those used in credit scoring, employment, insurance, or biometric identification.
The EU AI Act divides high-risk systems into two main groups:
- AI used in safety-related products (like cars, medical devices, or aviation systems)
- AI listed in Annex III, covering areas like education, HR, public services, and critical infrastructure
Once a system is labeled “high-risk,” two types of organizations have clear responsibilities: providers and deployers.
What providers must do
If your company builds or sells a high-risk AI system, here’s what’s expected under the EU AI Act:
- Register the system in the EU database before launching it on the market
- Set up a quality management system to ensure the AI is built safely and stays compliant
- Manage risks continuously across the system’s lifecycle, including misuse and post-market issues
- Ensure high-quality training data, free of bias and errors, where relevant
- Create detailed documentation, including technical descriptions and risk assessments
- Build transparency and human oversight into the product (clear instructions, stop buttons, etc.)
- Report serious incidents to the authorities as soon as they’re identified
- Design for cybersecurity, ensuring robustness and accuracy
What deployers must do
If your organization uses high-risk AI, for example, in a mobile banking app, credit scoring system, or hiring platform, you also have responsibilities:
- Use the AI system according to its instructions, and keep track of how it performs
- Assign human oversight to make sure a real person can step in or change decisions
- Check the input data quality, if you control it
- Keep system logs for at least six months
- Report incidents quickly if the AI behaves in a risky or harmful way
- Assess the AI’s impact on fundamental rights in some cases (especially in finance or healthcare)
- Inform employees when the system is used in the workplace
Enforcement & penalties
If a company breaks the law, national authorities will investigate, with the European AI Office, which helps ensure that the rules are applied the same way across all EU countries.
Depending on how serious the issue is, fines can go up to:
- €35 million or 7% of global revenue for using banned AI (like social scoring)
- €15 million or 3% for not meeting high-risk system rules
- €7.5 million or 1% for giving wrong or missing info to regulators
Using prohibited AI systems, such as social scoring or manipulative techniques, can lead to fines of up to €35 million or 7% of a company’s global annual turnover, whichever is higher.
Violating obligations for high-risk AI systems, such as failing to follow conformity assessments or transparency requirements, can lead to penalties of up to €15 million or 3% of global turnover.
Providing false or incomplete information to authorities could cost companies up to €7.5 million or 1% of turnover.
Fines can be adjusted for startups and SMEs to reflect their size and financial capacity.
- Global “Brussels Effect”
Even though the AI Act is an EU regulation, it has a global reach. Non-EU companies that want to offer AI products or services in Europe (whether in fintech, healthcare, or consumer apps) must still comply with the EU’s rules. This is part of what’s called the “Brussels Effect”, where strict European laws influence global companies to raise their own standards just to stay in the EU market. Much like GDPR shaped global privacy practices, the AI Act could set the tone for how AI is regulated worldwide.
3. The UK’s pro-innovation approach
Unlike the EU AI Act, which sets strict legal boundaries, the UK is regulating artificial intelligence with a lighter touch. Instead of rushing new laws on artificial intelligence, it’s focusing on flexible principles that support innovation while encouraging accountability.
- Principles before legislation
Instead of jumping into detailed legislation, the UK government introduced five core principles to guide AI development: safety, transparency, fairness, accountability, and redress. This lets businesses build and adapt as the tech evolves without the need to get caught in regulation too soon.
The idea is to keep innovation flowing while still giving developers and regulators a clear ethical foundation to work from.
- Regulatory innovation office
To make that possible, the UK created support systems like the AI Security Institute, which focuses on testing and evaluating advanced AI models for risks before and after deployment. There’s also the FCA’s “supercharged” sandbox, where banks and fintechs can test AI products in a safe, controlled space.
The behind-the-scenes coordination is handled by the Regulatory Innovation Office, helping different industries and regulators stay aligned. This setup lets the UK stay agile while keeping an eye on safety.
4. Broader global regulatory efforts
AI regulation isn’t just a European concern, here’s how the rest of the world is responding.
- Council of Europe and AI Framework Convention
The Council of Europe’s Framework Convention on AI is the first global treaty that makes sure artificial intelligence respects human rights, democracy, and the rule of law. It’s designed to reduce the risk of these values being harmed by AI systems.
- G7 AI principles
The G7 nations adopted eleven guiding principles and a voluntary Code of Conduct for developers of advanced AI as part of the Hiroshima Process. The guidelines focus on a risk-based approach to the entire AI lifecycle, from pre-deployment risk assessments to post-deployment monitoring, transparency about system capabilities, and internal security testing. Their goal is to promote responsible and secure AI development.
Beyond the EU and UK
Outside Europe, countries such as the U.S., Canada, Japan, and China are developing their own strategies to regulate artificial intelligence. While approaches differ, most aim to balance innovation with risk, especially in areas like facial recognition, financial services, and generative AI. The lack of a unified global framework makes international coordination increasingly important.
5. Institutional governance models
Beyond writing the laws on artificial intelligence, it also matters who enforces them. In this part, we look at how the EU, UK, and other regions structure their oversight of AI regulation and compliance.
- EU’s governance architecture
The EU AI Act introduces a layered system to oversee how AI is used and regulated across member states. At the center is the EU AI Office, responsible for coordination, enforcement, and guidance. It works alongside the European AI Board, made up of national regulators from each EU country. A Scientific Panel supports these bodies by offering technical expertise and reviewing emerging risks. Together, they ensure AI systems meet EU standards while supporting a consistent regulatory approach across borders.
- UK oversight via regulators & standards markets
The UK doesn’t have a single AI authority. Instead, it assigns oversight to existing regulators, like the Financial Conduct Authority or the Information Commissioner’s Office. These regulators follow shared guidelines shaped by key AI principles like safety and fairness. It’s a more flexible model, designed to keep up with fast-changing technology without needing new laws for every case.
6. What it means for businesses and developers
If you are building with AI, you’ll need to think about more than just the tech. Here are a few points to consider and keep an eye out for:
- Compliance burden & market access
High-risk AI systems, such as those used in credit scoring or fraud detection, must comply with strict requirements. That includes keeping documentation, running risk assessments, and being audit-ready.
Tip: Start a compliance checklist from day one. Even a lightweight internal process (like tracking model updates and decisions) can go a long way when regulations tighten.
- Build governance into your product early
If you treat governance as part of building your product and not just something to worry about later, you’ll be able to adapt more quickly when rules change. It is important to follow ethical AI principles, set up internal review processes, and keep track of how your models behave. This way, you can make sure your product is ready for whatever comes next.
Tip: Assign someone on your team to act as your “AI risk lead”. This person will track upcoming regulations and keep the team aligned.
- Competitive edge vs cost of compliance
While the EU’s AI Act is stricter than the UK’s guidelines, both recognize companies that take AI governance seriously. If you can show transparency and oversight in your system, it’s easier to enter new markets and build user trust.
Tip: Use regulatory readiness as a selling point. Especially if you’re pitching to banks, insurers, or enterprise clients who need partners they can trust.
7. What’s next for AI regulation?
Businesses working with AI, especially in high-risk areas like fintech or digital identity, should keep a close eye on the road ahead. Here’s a quick look at what’s coming:
EU timeline: full enforcement by 2027
The EU AI Act officially came into force in August 2024. From there, it is implemented in phases:
- By 2025, banned AI systems (like social scoring) must be fully eliminated.
- High-risk AI rules will apply step by step, with full enforcement by mid-2027.
If you are a company building or using AI in the EU, it is important to start preparing now, especially if you fall under the high-risk category.
You’ll need to:
- Check if your system is considered high-risk
- Register it in the EU database if required
- Put risk management and human oversight in place
- Keep records and technical documentation
- Be ready for audits or inspections from regulators
UK next steps
While the EU moves forward with the AI Act, the UK is taking a flexible, pro-innovation approach. No specific AI law exists yet, but regulators continue to adopt guiding principles. The government is also working on a data use roadmap, which includes work on model transparency and AI standards for the public sector.
Likely regulatory mix
As more countries move forward with their own AI rules, it’s clear there won’t be one global law everyone follows. Instead, we’re heading toward a regulatory mix, where different regions adopt their own frameworks, but still try to stay loosely aligned.
For companies operating globally, this means you’ll likely need to navigate multiple laws on artificial intelligence, not just one.
8. Recommendations for organizations
Regulating artificial intelligence isn’t easy, but there are practical steps you can take to stay compliant and build safer AI systems.
- Know what you’re working with
If you’re still defining your use cases, an AI transformation workshop can help clarify where risk lives and what to prioritize. Because your products might fall under the “high-risk” category, it’s good to get ahead of that now.
- Adopt risk management & documentation processes
Start keeping records about trained models, used data, or even who reviewed the outputs. Besides helping regulators, it will help your own teams make better decisions later.
- Implement explainability & human in the loop
No matter how advanced your AI is, make sure there’s someone who understands how it works and can step in when needed. Regulators really care about this, especially in banking or credit.
- Test in a safe space first
Use regulatory sandboxes or internal testing to trial your systems. It’s much better to catch risks there than when the product is already live.
- Prepare for audits, conformity, and cross-border compliance
If you’re operating in the EU, the UK, or globally, expect to deal with different rules. Some will require audits, others documentation, or risk assessments. It’s not fun, but being prepared can save you time (and legal headaches) later.
Use AI regulations to your advantage
AI regulation is moving fast, and while the EU AI Act and related laws on artificial intelligence bring clarity, they also add pressure. If you’re working in fintech, the rules can get complex quickly. What counts as high-risk? Or will your onboarding flow need human oversight?
At Vacuumlabs, we help fintechs and banks prepare for what’s next. It means building compliant onboarding flows and KYB tools, or designing AI automation and fraud detection that aligns with regulations. We combine deep product experience with clear technical execution. Maybe you’re improving existing digital lending products or launching something new, we make sure it’s built with compliance in mind.
No matter where regulatory AI is headed, the best time to get ready is now
Sources:
https://en.wikipedia.org/wiki/G7
https://hai.stanford.edu/ai-index/2025-ai-index-report
https://artificialintelligenceact.eu/article/99/
