AI compliance: How to use artificial intelligence safely and in compliance with the law in your company

Artificial intelligence has long since arrived in everyday business life. Whether automated processes, intelligent analyses or generative AI - the potential applications are growing rapidly. At the same time, however, the requirements for data protection, transparency and legal security are also increasing. This is precisely where the topic of AI compliance comes into play. In the context of implementing AI regulations, risk management and ethical issues, the term AI compliance is often used to support companies in using AI systems in a responsible and legally compliant manner.

If you use AI systems in your company or plan to use them, you must ensure that all legal requirements, internal guidelines and ethical standards are adhered to. Failure to do so could result in legal risks, fines or reputational damage.

Providers of AI systems have special legal obligations and responsibilities in this regard, particularly with regard to transparency, documentation and risk management in accordance with the AI Regulation. With the EU AI Act and existing regulations such as the GDPR, regulation is also becoming more specific and binding.

AI compliance helps you to identify risks at an early stage, define clear responsibilities and manage the use of AI in a structured manner. Non-compliance with data protection regulations such as the GDPR can lead to high fines. This not only creates legal certainty, but also strengthens the trust of customers and partners.

In this article, you will receive a compact overview of what AI compliance means, what requirements you will face and how you can use AI solutions in your company in a secure and compliant manner.

Definition: What does AI compliance mean?

AI compliance refers to adherence to all legal, regulatory and internal company requirements when using artificial intelligence. AI compliance encompasses various aspects - in particular legal, ethical and organizational challenges - as well as integration into existing governance structures.

The aim is to develop, operate and monitor AI systems in such a way that they are used in a legally compliant, secure, transparent and responsible manner. An effective AI compliance system is based on clear structures and proven building blocks to manage the complexity of regulatory requirements.

AI compliance thus combines legal requirements with technical and organizational risk management. This allows you to retain control over your systems and ensure that AI is used reliably and trustworthily in your company.

Practical guide: Implementing AI compliance step by step

You need a clear and structured roadmap to ensure that AI compliance does not just exist on paper in your company. Effective compliance management, established compliance processes and the involvement of compliance officers and compliance officers are crucial for the successful implementation of AI compliance strategies and guidelines. With a three-stage approach, you can proceed systematically, use your resources efficiently and make rapid initial progress.

The continuous implementation of audits and the constant improvement of compliance processes are essential in order to identify compliance violations at an early stage and achieve sustainable results.

In the practical implementation of AI compliance, providers, operators, products, services, AI models, AI training and the training of AI play a central role in meeting regulatory requirements and making innovative solutions legally compliant.

The involvement of users and inclusive communication are important aspects of addressing all relevant target groups in the context of AI compliance and promoting equal opportunities.

Phase 1: Analysis and preparation

In the first phase, you lay the foundation for all further measures:

• Create an AI inventory: Completely record all AI applications in use - including algorithms that are integrated into existing standard software.

• Set up a project team: Form an interdisciplinary team from compliance, data protection, IT and legal with clearly defined responsibilities. Consider the various aspects of AI compliance and actively involve compliance officers, compliance officers, providers and operators in the analysis and preparation to ensure comprehensive AI governance and effective AI risk management.

• Conduct a gap analysis: Check where there are currently gaps in processes, documentation or legal requirements and prioritize according to risk and effort. Integrate the analysis of products, services, AI models, AI training and the training of systems as well as the consideration of users to cover all relevant compliance requirements. Regular audits and assessments help to identify and resolve potential difficulties at an early stage.

• Identify quick wins: Start by implementing measures that can be realized quickly and with little effort. This will help you achieve early success. Ensure compliance with legal principles and best practices to safeguard the implementation of AI compliance right from the start.

Phase 2: Strategy and guidelines

Based on the analysis, you develop the strategic and organizational framework:

• Define AI compliance strategy: Determine how AI will be used in a safe and compliant manner and how the measures will support your business objectives. Strategies, a clear AI policy, comprehensive AI governance and effective compliance management are essential to meet regulatory requirements and minimize risks.

• Develop internal guidelines: Create concrete guidelines and instructions for typical deployment scenarios, taking into account all relevant legal areas. Compliance officers, compliance officers, providers and operators play a central role in the development, implementation and monitoring of these guidelines. They ensure that AI products and services are developed and used in accordance with the rules, that AI models are trained responsibly and that users are given appropriate consideration.

• Evaluate tools: Check specialized AI compliance or governance solutions that support you with documentation, risk assessment and monitoring. Make sure that the tools cover AI training, model training and compliance requirements for all products and services.

• Plan change management: Ensure acceptance within the company through communication, training and clear processes. The training sessions cover legal basics, practical implementation steps and best practices and offer the opportunity to actively exchange ideas with the speakers and other participants.

Phase 3: Implementation and rollout

Now you put the planned measures into practice:

• Start a pilot project: test new processes initially with selected AI systems to gain experience and optimize processes. Providers and operators of AI products as well as compliance officers and compliance officers should work closely together to ensure that all compliance processes are implemented correctly from the outset.

• Roll out step by step: Roll out the defined workflows company-wide and accompany them with training and support. It is important that compliance officers and compliance officers are regularly trained and certified to ensure compliance with the AI regulation. Users of AI products and services should also be involved in the processes.

• Establish monitoring: Implement control mechanisms, metrics and clear reporting channels to regularly review effectiveness. Automation through AI models and targeted AI training significantly reduces the manual review effort, increases efficiency and ensures more consistent and accurate compliance assessments. Compliance officers, providers and operators are responsible for monitoring, documentation and early detection of compliance violations. Training the AI models with high-quality data helps to achieve reliable results.

• Continuously improve: Continuously adapt your measures to new regulatory requirements and technological developments. Regular audits and assessments help to identify potential difficulties and compliance violations at an early stage and ensure consistent results.

With this structured approach, you ensure that AI compliance is firmly anchored in the company and does not just end up as a one-off project.

AI compliance vs. AI security: what's the difference?

The terms AI compliance and AI security are often used together or even equated. However, the two approaches actually have different focuses. If you want to use AI systems responsibly, you should clearly distinguish between the two areas - and at the same time think about them together.

AI compliance focuses primarily on compliance with legal and regulatory requirements. The aim is to ensure that your AI works in a legally compliant, transparent and traceable manner. This includes data protection requirements, documentation obligations, risk assessments and internal guidelines in accordance with the GDPR and EU AI Act.

AI security, on the other hand, deals with the technical and organizational security of your AI systems. The aim is to protect models, data and infrastructure from attacks, manipulation or misuse.

Typical differences in practice:

• AI compliance: fulfilling legal requirements, documenting processes, defining responsibilities, ensuring transparency

• AI security: protection against cyber attacks, access controls, secure data storage, protection of models against manipulation or data leaks

Example: If an AI tool processes personal data without a legal basis, this is a compliance issue. However, if the same tool is compromised by a hacker attack or training data is stolen, this is a security problem.

For you as a company, compliance without security is not enough - and vice versa. You can only minimize risks holistically if both areas work together. While compliance sets the legal framework, security ensures technical protection in everyday life. Together, they form the basis for a trustworthy and stable use of AI.

Risks and dangers due to a lack of AI compliance

Without clear AI compliance structures, you not only expose your company to legal risks, but also jeopardize processes, reputation and economic success. AI systems often intervene deeply in business processes and process sensitive data. If they are used uncontrolled or without binding rules, the consequences can quickly become serious.

Typical risks are, for example

• High fines and legal consequences: Violations of the GDPR or, in future, the EU AI Act can result in severe penalties. For example, if an AI system processes personal data without a legal basis or makes automated decisions without sufficient transparency, fines and official requirements may be imposed.

• Incorrect or discriminatory decisions: Insufficiently checked training data can lead to biased results. An AI tool in recruiting could systematically disadvantage applicants or favor unsuitable candidates. This can not only have legal consequences, but can also damage your employer image.

• Lack of transparency and loss of control: If you cannot understand how an AI arrives at its results, it becomes difficult to explain or correct decisions. This is particularly critical when it comes to credit checks, pricing or automated approvals. Erroneous decisions often remain undetected for a long time.

• Data breaches and security problems: If sensitive data is fed into AI models without protection or external tools are used without checking, data leaks or unauthorized access can occur. In addition to financial damage, the trust of your customers suffers above all.

• Reputational damage: Negative headlines about discriminatory algorithms or data protection breaches spread quickly. Even individual incidents can damage your brand image and customer loyalty in the long term.

• Inefficient processes and shadow IT: Without clear guidelines, employees often use AI tools independently. This results in uncontrolled isolated solutions, duplication of work and additional security risks.

These examples show: A lack of AI compliance not only affects the legal department, but the entire company. With clear processes, responsibilities and controls, you can ensure that your AI systems are reliable, secure and trustworthy.

What legal framework conditions influence the use of AI

If you use AI in your company, you are not operating in a legal vacuum. The GDPR and the EU AI Act in particular form the central legal framework for dealing with artificial intelligence. While the GDPR regulates the protection of personal data and transparent data processing, the EU AI Act sets out specific requirements for the development, use and risk assessment of AI systems. Both sets of regulations oblige you to systematically check risks, document processes and define clear responsibilities. Providers of AI systems are particularly obliged to ensure transparency, comprehensive documentation and effective risk management in order to comply with the legal requirements. This is the only way to operate AI in a legally compliant and secure manner.

EU AI Act

The EU AI Act is the world's first comprehensive law on the regulation of artificial intelligence. It follows a risk-based approach: the higher the risk of an AI system for security, fundamental rights or users, the stricter the legal requirements for development, operation and control.

According to Article 6, the classification is made into four risk categories, the so-called risk classes. These risk classes are used to classify AI applications in accordance with the requirements of EU regulation and determine which regulatory measures are required for the respective application:

• Minimal risk: only minor requirements apply to these applications, for example basic training and awareness-raising for employees.

• Limited risk: Systems such as chatbots or AI-supported assistants must comply with transparency obligations. You must clearly inform users that they are interacting with an AI.

• High risk: AI solutions in sensitive areas such as human resources, education, healthcare or law enforcement are subject to strict compliance requirements. These include risk management, high data quality, comprehensive documentation and human oversight. Providers of high-risk AI systems are obliged to implement comprehensive documentation, transparency and risk management measures and to train their staff accordingly.

• Unacceptable risk: Certain practices such as social scoring or manipulative influence are generally prohibited. Biometric real-time identification in public spaces is also only permitted to a very limited extent.

In addition, special requirements apply to general purpose AI (GPAI), such as large language models. They are subject to more extensive information, transparency and documentation obligations as well as clear rules of conduct.

GDPR

As soon as your AI systems process personal data, the requirements of the GDPR apply. For you, this means that you not only have to implement technical solutions, but also comply with and document clear data protection requirements.

The relevant regulations on the handling of personal data in the GDPR can be found in particular in the basic principles of data processing in Art. 5, the legal bases for processing in Art. 6, the regulations on special categories of data in Art. 9, the regulations on automated decisions in Art. 22, the transparency obligations in Art. 13-15, the requirements for data protection through technology design in Art. 25 and the provisions on order processing in Art. 28.

The following points in particular play a role here:

• Automated decisions: Profiling and purely automated decisions are only permitted under certain legal conditions.

• Rights of data subjects: Data subjects are entitled to information about how automated decisions are made and can object to processing.

• Transparency and traceability: AI systems should be designed in such a way that decisions can be explained and processes are documented in a comprehensible manner.

• Data usage: Principles such as data minimization and purpose limitation also apply when training AI models. Only data that is really necessary should be used. AI models should only be trained with data that complies with data protection regulations in order to ensure compliance.

• Data protection impact assessment: A data protection impact assessment is mandatory for AI applications with an increased risk to the rights and freedoms of individuals.

It is important that you integrate data protection requirements into your AI projects at an early stage. The integration of data protection into AI projects is known as "privacy by design" and should be taken into account from the outset. Plan appropriate checks from the outset and document all processing steps in a comprehensible manner. This reduces legal risks and builds trust.

Governance structures for AI compliance in the company

The introduction of governance structures is a key success factor for the legally compliant and responsible use of artificial intelligence in companies. Particularly in view of the increasing regulatory requirements resulting from the AI Regulation (AI Regulation), the AI Act and the GDPR, it is essential to establish clear responsibilities and processes that cover the entire life cycle of AI applications.

At the center of this is the AI Compliance Officer. This role acts as a central point of contact for all compliance issues relating to the use of AI systems. The AI Compliance Officer monitors adherence to legal and internal compliance requirements, coordinates the performance of risk assessments and ensures the implementation of effective compliance measures. They are also responsible for ensuring that data protection and IT security are considered and implemented in all AI projects from the outset.

Overall, establishing governance structures for AI compliance lays the foundation for the secure, efficient and legally compliant use of artificial intelligence. A dedicated AI compliance officer, clear processes and close collaboration between the relevant teams are the key to fully exploiting the opportunities offered by AI while ensuring that all compliance requirements are met.

AI compliance as the basis for the safe use of AI

AI offers you enormous opportunities for efficiency, innovation and competitiveness. At the same time, however, regulatory requirements and expectations regarding transparency, security and responsibility are increasing. AI compliance is therefore not an optional extra, but a central prerequisite for the sustainable and legally compliant use of artificial intelligence. AI compliance, compliance management, compliance officers, compliance officers, strategies, aspects, AI governance and AI risk management are crucial to ensure the safe and responsible use of AI systems.

Providers, operators, products, services, AI models, AI training, the training of models and the involvement of users play a central role in the implementation of AI compliance and adherence to legal requirements.

If you create clear structures, processes and responsibilities at an early stage, you reduce risks, avoid fines and strengthen the trust of customers and partners. At the same time, you gain more control over your AI systems and can exploit their potential in a targeted manner.

At FIDA, AI compliance is therefore an integral part of our AI consulting and AI training. We help you to understand regulatory requirements, derive specific measures and integrate AI solutions securely into your organization. In this way, you combine innovation with legal certainty - and make AI a real added value for your company.