What Companies Need to do to be EU AI Compliance

Artificial Intelligence (AI) is revolutionizing all industries, providing new opportunities and challenges for growth and innovation. However, with great power comes greater responsibility. The European Union (EU) has recognized the urgent need for ethical and transparent AI practices to protect individuals' rights and to ensure fair and accountable use of AI technologies. This article aims to guide companies on what they must do to comply with EU AI regulations.

Written by:
Jaime Vélez
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Understanding EU AI Compliance

To ensure the ethical and responsible use of AI, the EU has established guidelines and regulations under the General Data Protection Regulation (GDPR) and the upcoming Artificial Intelligence Act (AIA). Compliance with these regulations is essential for companies operating within the EU or dealing with EU citizens' data.

The cornerstone of the AI Act is a classification system that determines the level of risk an AI technology could pose to the health and safety or fundamental rights of a person. The framework includes four risk tiers: unacceptable, high, limited and minimal.

Let's explore the key steps companies need to take to achieve EU AI compliance.

Conducting a Data Protection Impact Assessment (DPIA)

Before implementing AI systems, companies must conduct a Data Protection Impact Assessment (DPIA). A DPIA is a comprehensive assessment that helps identify and minimize the risks associated with AI implementation. It evaluates the potential impact on individuals' privacy, data protection, and other fundamental rights. Through a DPIA, companies can proactively identify and mitigate potential risks to ensure compliance with EU regulations.

Key Elements of a DPIA

A DPIA should include: the following elements:

  1. Identifying the need for a DPIA:  first we need to determine whether the AI system involves processing personal data or poses potential risks to individuals' rights and freedoms. If so, a DPIA is necessary.
  2. Description of the AI System: you need to provide a detailed description of the AI system, including its purpose, functionality and intended use.
  3. Assessing Necessity and Proportionality: You need to evaluate whether the AI system is necessary and proportionate to achieve its intended purpose. Consider alternative approaches and weigh the benefits against potential risks.
  4. Identifying and Assessing Risks: we need to identify and assess the risks associated with the AI system's implementation, including the potential impact on privacy, data protection, and other rights. We need to evaluate the likelihood and severity of these risks.
  5. Implementing Mitigation Measures: It is necessary to develop and implement appropriate measures to minimize the identified risks. This may include technical and organizational measures, such as data anonymization, encryption, access controls, and transparency mechanisms.
  6. Monitoring and Reviewing: you need to continuously monitor and review the AI system's performance, impact, and compliance with data protection regulations. Regularly reassess the need for a DPIA based on changes to the system or its context.

Ensuring Transparency and Explainability

Transparency and explainability are crucial aspects of AI compliance. Companies must ensure that AI systems' decision-making processes are transparent and understandable to individuals affected by those decisions. This means providing clear and accessible information about how the AI system operates, the data it processes, and the potential impact on individuals' rights.

Meeting Transparency Requirements

To meet transparency requirements, companies should consider:

  1. Clear and Concise Information: povide individuals with clear, concise, and easily understandable information about the AI system's purpose, functionality, and potential impact on their rights.
  2. Rights to Explanation: inform individuals about their right to obtain an explanation of the AI system's decisions that significantly affect them. Companies should provide meaningful information about the logic, significance, and consequences of these decisions.
  3. Accessible Explanations: ensure that explanations are accessible to individuals, taking into account their level of technical understanding. Avoid overly complex or technical jargon that may hinder comprehension.
  4. Human Review: consider implementing mechanisms for human review and intervention in AI system decision-making processes, particularly in high-stakes situations that may have a significant impact on individuals' lives.

Data Protection and Security

Data protection and security play a vital role in EU AI compliance. Companies must implement robust measures to protect personal data processed by AI systems, ensuring confidentiality, integrity, and availability.

Data Protection Measures

To ensure data protection and security, companies should focus on:

  1. Data Minimization: collect and process only the necessary personal data required for the AI system's intended purpose. Avoid excessive or irrelevant data processing.
  2. Secure Data Storage: implement appropriate technical and organizational measures to safeguard personal data against unauthorized access, loss, or destruction. This may include encryption, access controls, regular backups, and secure data storage facilities.
  3. User Consent and Rights: obtain individuals' informed consent for data processing activities related to the AI system. Respect individuals' rights, such as the right to access, rectify, and erase their personal data.
  4. Data Transfer Considerations: if personal data is transferred outside the EU, ensure that adequate safeguards are in place to protect the data and comply with EU data protection regulations. This may involve using standard contractual clauses or relying on the EU-U.S. Privacy Shield Framework, where applicable.

How Edge Computing can help companies with EU AI compliance?

Edge computing refers to the decentralized processing of data at the edge of the network, closer to the data source, rather than relying solely on centralized cloud infrastructure. This emerging technology has the potential to assist companies in achieving EU AI compliance in several ways:

Enhanced Data Privacy

One of the fundamental principles of EU AI compliance is protecting individuals' privacy and ensuring secure data processing. By leveraging edge computing, companies can minimize the need to transfer sensitive data to the cloud or other remote servers. Instead, data can be processed locally on edge devices or gateways, reducing the risk of unauthorized access or data breaches during data transmission. Edge computing enables data to be processed closer to the source, ensuring a higher level of data privacy and minimizing the exposure of personal information.

Reduced Data Transfer

Under the EU AI compliance framework, companies are encouraged to limit data transfers outside the EU to ensure compliance with data protection regulations. Edge computing allows for local processing, reducing the reliance on transferring vast amounts of data to centralized servers or cloud platforms. By processing data at the edge, companies can minimize data transfer requirements, thereby reducing the potential compliance risks associated with cross-border data transfers.

Real-time Decision-making

Edge computing enables faster and real-time decision-making by processing data locally, without the need for round-trip communication with a centralized server. This capability is particularly relevant for AI systems that require quick responses or operate in time-sensitive scenarios. By processing data locally at the edge, companies can ensure compliance with EU AI regulations that require transparency and explainability of AI decisions while minimizing latency and response times.

Enhanced Data Security

Edge computing provides an additional layer of security for AI systems. By processing data closer to the source, companies can implement robust security measures tailored to the edge environment. This can include encryption, access controls, and secure communication protocols to safeguard data and prevent unauthorized access. Enhanced data security measures can contribute to EU AI compliance by minimizing the risk of data breaches, protecting individuals' personal information, and ensuring the integrity and confidentiality of data processed by AI systems.

Lower Bandwidth Requirements

Edge computing reduces the strain on network bandwidth by processing data locally, thereby reducing the amount of data that needs to be transmitted to remote servers. This is particularly beneficial for companies operating in areas with limited or unreliable network connectivity. By leveraging edge computing, companies can achieve AI compliance by ensuring that data is processed efficiently, without relying heavily on network resources.

Edge Computing empowers companies to navigate the complexities of AI regulations more effectively. Leveraging edge computing technologies can help companies strike a balance between innovation and compliance, fostering the responsible and ethical use of AI within the EU regulatory framework.

Barbara, the Edge Platform for AI Compliance

Barbara Industrial Edge Platform is a powerful tool that helps organizations simplify and accelerate their Edge AI Apps deployments, building, orchestrating, and maintaining easily container-based or native applications across thousands of distributed edge nodes.

  1. Real-time data processing: Barbara allows for real-time data processing at the edge, which can lead to improved operational efficiency and cost savings. By processing data at the edge, organizations can reduce the amount of data that needs to be transmitted to the cloud, resulting in faster response times and reduced latency.
  2. Improved scalability: Barbara provides the ability to scale up or down depending on the organization´s needs which can be beneficial for industrial processes that have varying levels of demand.
  3. Enhanced security: Barbara offers robust security features to ensure that data is protected at all times. This is especially important for industrial processes that deal with sensitive information.
  4. Flexibility: Barbara is a flexible platform that can be customized to meet the specific needs of an organization. This allows organizations to tailor the platform to their specific use case, which can lead to improved efficiency and cost savings.
  5. Remote management: Barbara allows for remote management and control of edge devices, applications, and data, enabling organizations to manage their infrastructure from a centralized location.
  6. Integration: Barbara can integrate with existing systems and platforms, allowing organizations to leverage their existing investments and improve efficiency.

Want to scale your Edge Apps at scale efficiently? Request a demonstration

Frequently Asked Questions (FAQs)

What are the consequences of non-compliance with EU AI regulations?

Non-compliance with EU AI regulations can result in severe penalties, including fines of up to €20 million or 4% of the company's global annual turnover, whichever is higher. Additionally, non-compliance can lead to reputational damage, loss of customer trust, and potential legal actions by affected individuals.

Do these regulations apply only to EU-based companies?

No, these regulations apply to any company that processes personal data of individuals located in the EU, regardless of the company's location. If a company operates within the EU or deals with EU citizens' data, it must comply with EU AI regulations.

Are there any exemptions to EU AI compliance requirements?

While there may be specific exemptions for certain AI systems or applications, it is essential to consult legal experts or data protection authorities to determine if these exemptions apply to your specific case. Generally, companies should strive to comply with EU AI regulations to ensure ethical and responsible AI use.