Whether we’re ready for it or not, artificial intelligence has taken the world by storm. AI has been adopted by 78% of global businesses, which is a steep increase from 55% in 2023. Its massive potential indicates that its adoption rate will continue to climb.
But in this technological boom lies a growing concern and need for the ethical use of AI, especially as it has already shown its potential for bias, discrimination, and error.
As a result of this, and many other issues concerning data privacy, policymakers all over the world are developing new regulations and rules to control the development and use of AI.
Let’s take a closer look at AI regulatory compliance and how it’s affecting the business landscape of today.
How We Define AI Regulatory Compliance
En tant que Gouvernance de l'IA experts, we define AI regulatory compliance as a set of practices that keep an organization’s use and management of AI technologies in line with applicable laws, regulations, and policies.
And speaking of trust, implementing AI regulations also requires strong cybersecurity and gestion des risques strategies to ensure AI systems remain protected from malicious exploitation.
From shadow AI to gaps in compliance, most organizations are flying blind — and exposed. Take a closer look inside the current state of AI governance: its key risks, current regulations, industry-specific pain points, and more.
Why We Should Care About AI and Regulatory Compliance
Responsible and ethical AI use and development are at the core of these compliance policies.
Regulatory compliance (AI specifically) is designed to help businesses mitigate the risks (legal, financial, etc.) associated with the use of AI models, such as:
- Violations de données
- Personal data mishandling
- Human biases in training data
AI compliance frameworks protect companies from potential penalties and liabilities, which could be substantial. One of the most famous examples of AI-related fines is the one involving Clearview AI, a facial recognition tech company that scraped pictures of people’s faces from all over the internet without their consent to create a biometric database. The company was fined €22 million.
It’s not just heavy fines that businesses need to worry about. Achieving and maintaining compliance safeguards businesses from reputational damage, as it demonstrates their commitment to ethical practices.
Existing AI Regulations
Within the regulatory landscape, AI can be difficult to control because of the sheer velocity of innovation. It’s challenging for regulators to create comprehensive laws, which also makes it challenging for businesses.
Some AI-specific regulations are already in place, but companies should also be made aware of additional regulation compliance requirements, for example, for those governing cybersecurity and data privacy. As is the case with many data protection laws, compliance isn’t always a matter of where your company is based but rather where you conduct business.
Let’s take a closer look at some existing regulatory compliance requirements for AI systems:
In the United States
Le Artificial Intelligence Research, Innovation, and Accountability Act of 2024 (AIRIAA) provides a framework for balancing transparency, accountability, and risk mitigation with AI innovation.
The first state law to require the disclosure of training data for generative AI systems will take effect in 2026. California’s Generative AI Training Data Transparency Act will promote transparent AI development, create specific protections regarding personal information, and give users a better understanding of how AI works.
In Colorado, the Consumer Protections for AI law, which seeks to protect residents from algorithmic discrimination, will take effect in 2026.
In Texas, the Responsible AI Governance Act, which will oversee the development, deployment, and use of artificial intelligence systems in the state, will also be written into law in 2026.
In terms of data privacy laws, the Loi californienne sur la protection de la vie privée des consommateurs (CCPA) et le Virginia Consumer Data Privacy Act (VCDA) had, until recently, been the only states with official laws protecting consumer data. At the time of this writing, there are now a total of 20 states with comprehensive privacy laws, and more are expected to pass shortly.
In Europe
In the EU, there are two main regulations that regulate AI: La loi européenne sur l'IA and the GDPR.
La loi européenne sur l'IA
Europe is home to the world’s first comprehensive AI framework and impacts AI providers and deployers both within and outside the European Union (if their AI systems are placed on the EU market). Known simply as the EU AI Act, this piece of legislation classifies AI into four categories:
Risque inacceptable : All AI practices that fall into this category are prohibited within the EU.
There are currently eight prohibited AI applications in Europe, including:
- Subliminal manipulation to alter behavior
- Exploitation of vulnerabilities (e.g., age or disability)
- Social scoring that leads to unfair treatment.
- Predicting criminal activity
- Inferring emotional states in schools or workplaces.
- Scraping images of people from the internet or CCTV to expand a facial recognition database
- Real-time biometric identification and categorization of people based on sensitive attributes such as race, religion, or sexual orientation.
Risque élevé : These are the most regulated systems within the EU. High-risk AI includes any system that could have potentially negative consequences on a person’s health and safety, their rights, as well as the environment. This category is considered to have more benefits than risks, which is why it isn’t banned.
Risque limité : A smaller subset of AI applications fall under this category. Limited-risk AI is considered any system that still presents a risk of manipulation or deceit. Deployers and developers must also provide documentation to lawmakers and users to maintain a level of transparency and to ensure users understand the risks involved in using AI.
Risque minimal : All other AI systems fall into this category. These systems are currently unregulated, but human oversight and non-discrimination are recommended.
Where do ChatGPT (generative AI) and general-purpose AI (GPAI) fall? It has been historically difficult to classify this form of artificial intelligence, as its risk depends on its use case.
GDPR
The GDPR is a major regulation that oversees the collection, processing, storage, and management of personal data of European residents. It gives data subjects (EU residents) certain rights and controls over their data, such as the right to:
- Reject and withdraw consent to process personal data
- Know what information is collected about them
- Amend the information collected about them
- Be forgotten
- Deny certain automated processes
Companies that process personal data of EU citizens must be GDPR compliant and meet certain requirements, such as:
Having a legal basis (of which consent is one) for collecting and processing personal data; Collecting only the minimum amount necessary for their purpose (purpose limitation); and not keeping data longer than necessary (data minimization)
- Being transparent about what the data will be used for
- Maintaining accurate and up-to-date data
- Allowing data subjects to exercise their rights without prejudice
- Documenting their record-keeping policies for auditing processes
- In case of a data breach, notifying authorities within 72 hours
- Hiring data protection officers (DPO) when involved in high-risk data processing
- Facilitating consent management for users
- Updating privacy policies to include GDPR requirements
- Appointing an EU representative if the business is outside the EU
Even though the GDPR is not explicitly an AI regulation, the development and deployment of AI models must adhere to GDPR requirements in terms of data subject rights and data minimization.
In Asia
En China, le Interim Measures for the Management of Generative Artificial Intelligence Services (2023) drives a similar balance to the US’s AIRIAA in that it seeks to balance innovation with transparency and responsible use of public-facing generative AI services.
In South Korea, the South Korean AI Basic Law (SKAIA) seeks to mitigate AI risks and promote trustworthy AI practices while increasing industry innovation and exports. The law consists of three main points:
- It establishes the National AI Committee and an AI Safety Research Institute.
- It promotes AI development.
- It establishes safety measures regarding the use of high-risk and generative AI.
No matter when you read this, AI governance will continue to be a moving target, so the first step in creating internal policies for your AI systems should be to brush up on the most up-to-date regulations that apply to your organization.
Steps to Meeting AI Compliance Requirements
Now that we’ve explored some of the leading AI and data privacy regulations around the world, let’s discuss how businesses can strengthen their AI compliance efforts:
Identify and Inventory Existing Data and AI-Based Systems
The first step for using AI responsibly within your organization is to conduct an AI audit. The goal is to ensure that the company’s use of AI is aligned with established principles.
This also includes an audit of how your organization collects, manages, and catalogs unstructured data, or information that lacks standard formatting, like emails and documents.
Understanding what data you have and where to find it can help mitigate risks and avoid compliance violations.
Establish AI Governance Frameworks
AI governance establishes the frameworks, processes, and policies that keep a company’s AI systems compliant with AI regulations.
In an industry that’s known for its complexity and potential for ethical misuse, the best way to ensure that AI systems are developed and used legally, ethically, and in the best interests of people is to create an AI governance framework. This encourages transparency, which in turn, builds trust in AI.
Your framework should clearly outline your company’s values, principles, and policies around responsible AI development, and should provide guidelines for managing risk, data privacy, accountability, etc.
Invest in AI Security and Governance Tools
Why complicate data protection and governance with manual processes and outdated workflows when you can invest in a simple solution that manages AI systems and data across your entire environment?
Including AI in regulatory compliance solutions gives your company a more dynamic and accurate system of data governance. For example, BigID’s Sécurité et gouvernance de l'IA solution manages trust, risk, and security with advanced features and capabilities like:
- Auto-discovery for AI data and assets
- Protection and governance
- Data hygiene improvement
- Cataloging and curation
- Risk Identification and remediation
- Risk reduction for Microsoft Copilot
Our solutions put data privacy, security, and compliance at the forefront of your AI regulatory compliance initiatives.
Find out how to secure and govern your AI data with risk-aware context and control.
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