Artificial intelligence (AI) has reached impressive heights. What started as a differential product feature meant to draw attention is now the key system component around which the entire SaaS product is built.
But we all know the devil runs in the details.
As AI became more popular and widespread, the need for compliance became apparent.
At the moment, AI is subject to formal rules and regulations regarding risk, transparency, and accountability.
This fact makes SaaS product developers and business owners ask three practical questions:
Which laws focus on my product?
When does AI become subject to additional regulations?
Is there a way to reduce compliance obligations?
Let’s dive into AI compliance and see what it means from a SaaS builder’s perspective.
What are the 4 AI compliance pillars?
Did you know that there are currently over 72 countries that have launched 1000+ AI policy initiatives? Of course, they differ, but they are concentrated across four key areas.
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Safety requirements: Assessing AI products from perspectives like core values of health, safety, respect for human rights, democratic values, and environmental protection.
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Transparency obligations: Your AI product should not mislead users into thinking this feature is not used.
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Data governance: The product should consider and align with data protection regulations.
Accountability frameworks: There must be a person assigned to verify how the AI is employed within the product itself.
Important Point
Regulators are not assessing AI, the concept, but rather the impact it has on users and the way it is applied.
What are the main AI compliance regulations?
As mentioned earlier, the effort to regulate the AI market is strong. Some countries are exploring initiative ideas, while others have already implemented them.
Major regulatory frameworks include:
ics:
EU: The EU AI Act is a complete law that focuses on the risk-based approach, banning specific products and imposing strict rules on “high-risk” systems.
US: AI laws occur at the state level, with regulations focused on hiring, credit decisions, consumer transparency, and identity protection. There is no single regulation.
UK: Existing regulations on privacy, finance, healthcare, or competition apply to AI products.
ASIA: In South Korea, AI products are subject to a national framework that focuses on innovation support and labeling obligations. China’s AI Act looks at synthetic content labeling, traceability, and platform responsibility, particularly to address misinformation and impersonation.
What does High-Risk involve in AI compliance?
Understanding what high-risk means in AI compliance regulations is essential. So, let’s figure out what this concept means. AI is considered high-risk when it influences:
- The job market: this includes all the phases of the process (hiring, promotion, termination, performance)
- Financial aspects: credit decisions, pricing, fraud scoring, access to financial services
- Access: housing, insurance, healthcare, education
- Identity: voice cloning, impersonation, deepfakes
Keep In Mind
SaaS developers need to be aware of two compliance points:
Assistive AI can be considered by compliance frameworks: If the user relies on the tool’s output, it is often considered a component of the decision process.
Internal systems are analyzed: It is considered that if internal AI outputs impact users, AI compliance regulations need to apply.
AI Models vs. AI Platforms: The Difference That Actually Matters
AI compliance may be complicated, but it can become even more complex when treating all AI the same. They are not.
AI models and AI platforms are two different concepts, and they need to be treated as such.
| AI Model | AI Platforms |
| AI models are trained systems that generate outputs. (examples are OpenAI or Anthropic) |
AI platforms refer to the system that sits on top of the AI model. |
|
- Is trained on large datasets - Carries training data, output, and safety risk Often comes with enterprise contracts, documentation, and limited indemnities |
- Routes API calls to different models - Does not train models - Does not own training data - Typically does not provide compliance documentation or indemnities |
Keep In Mind
Platforms alter experiences. They do not change who is responsible for the process.
Can AI compliance be “shifted” to a vendor?
The answer is: partially at most. Here is why:
Regulators around the world hold the company employ the AI technology responsible for the final output. This is because they are expected to understand the AI model when implementing it into their workflow.
The AI model provider is responsible for the infrastructure, but the AI platform is expected to handle how the model is applied in real scenarios.
Even if important brands like Microsoft, Google, or OpenAI have “Copyright Shifts”, if you intentionally prompt a model to infringe on a trademark, their protection typically vanishes.
Quick Roadmap To AI Compliance
Staying AI-compliant does not stall your product development. It does mean switching from reactive fixes to continuous administration. That said, SaaS companies need to prioritize:
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Complete visibility: Map your entire AI usage, including third-party integrations.
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Access management: Decide the type of data that AI is allowed to access and why.
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Active monitoring: Keep an eye of AI behaviour to make sure no regulations are breached.
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Audit readiness: Track all documents that assess decision-making, controls, and fixes.
Create a strategy around these points to ensure your AI product meets regulatory frameworks, answers user expectations, and does not impact product innovation.
AI Compliance Thoughts for 2026
Since rumors are that 2026 is the year for switching from Chatbots to Autonomous Agents, the obvious concept that arises is delegated authority. Let’s exemplify:
If you are using an AI tool to book a trip and make the expected payments, but the agent makes a financial error, who is to blame?
It is a discussion, but the fingers seem to be pointing at the SaaS provider, not the model creator.
GDPR rises again, but this time, it’s set on AI. If before AI we were talking about simple, straightforward data deletion, this time the correct term would be unlearning. If a user wishes to have their data deleted under GDPR, you will be required to also remove them from the training data set. Yes, it will be a nightmare for Devs.
The AI targeted by compliance regulations is the one impacting the user. But another side seems to be gaining interest. And that is the AI technology used internally by devs in building the product. It can generate a compliance leak, and that should be regulated.
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Final Thoughts
We are living in a world dominated by AI. Simply turning our heads away from regulations will not do. AI products are subject to compliance requirements, and it is important to do your homework in this area before it is too late or too expensive.
So, keep in mind:
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Risk is determined by the impact your AI product has on users.
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Understand the compliance regulations in different regions and the approaches implemented.
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AI model selection impacts potential compliance exposure.
Ioana Grigorescu
Ioana Grigorescu is PayPro Global's Content Manager, focused on creating strategic writing pieces for SaaS, B2B, and technology companies. With a background that combines Languages and Translation Studies with Political Sciences, she's skilled in analyzing, creating, and communicating impactful content. She excels at developing content strategies, producing diverse marketing materials, and ensuring content effectiveness. Beyond her work, she enjoys exploring design with Figma.
