AI Data Compliance: All You Need To Know About DevOps Data Protection

Last Updated on February 28, 2025

The evolution of artificial intelligence has been rapid thus far. By 2030 the AI market is projected to reach $1.81 trillion. Technology supported by AI has been useful in many areas of life such as education, healthcare, or finance. That is reflected by the rate of AI adoption by organizations being 72% (2024). Even if you just look around you – many people use tools like ChatGPT for daily life or work, AI helps with email management or studying. 

What do these advancements in AI bring to DevOps? Well, as with everything, there are positives and negatives. AI is great for automation, productivity, quick analysis, and workflow simplification.

Importance of AI compliance 

Despite its positives, AI introduces some threats to DevOps. Therefore, understanding and addressing AI and compliance is crucial for cybersecurity and overall business continuity. AI compliance is necessary for a number of reasons:

• protection of privacy and security of individuals
• making sure that organizations use AI legally and ethically
• prevent organizations from getting into financial and legal risks

Threats concerning AI in DevOps

Let’s address the risks AI systems bring into DevOps and appropriate risk management strategies. Threats can vary in different scenarios and different usage of AI, and take the form of legal and financial risks, however, here we present the common vulnerabilities around AI usage in DevOps. A huge threat is compromised training data that is used to train AI models. Now, since AI is being trained on public data. This data could be outdated, have flaws in it and introduce other security issues. Imagine the training data included maliciously altered data and created vulnerabilities to exploit by attackers. This poisoned data could make AI systems approve unsafe code, leaving you exposed to risk. Therefore, code reviews are important, in Techstrong’s research 86% of respondents who use AI state that some or significant human verification of outputs is still required.

Another threat leading to security and compliance issues is the exposure of sensitive information. Never include any passwords, API tokens, or SSH keys in your code. It is crucial to understand that AI-powered systems could expose your credentials during code suggestions or logs. Even if your credentials are not directly exposed by AI, your sensitive data could still be mishandled and lead to security issues. Then, there is package hallucination. It means that sometimes AI may provide you with outputs that are entirely false or not real. This could lead to a lack of attribution along with unclear licensing and puts the integrity of your project at risk. As a result, you need appropriate risk management strategies because this could be bad for compliance efforts. 

The main risks AI brings into DevOps include: 

• Data poisoning
• Prompt attacks
• Adversarial attacks
• Training data
• Backdoor attacks
• Exfiltration
• AI-supported password cracking 
• AI impersonation
• AI-generated phishing emails
• Deepfakes

Examples of AI in DevOps

More and more tools and platforms rely on AI-powered systems in one way or another in DevOps. Whether they are the processes “behind the scenes” that you cannot see or tools like GitHub Copilot, DevOps is adapting more and more to artificial intelligence. See that is the thing, the prediction is that DevOps will be adapting to AI systems, rather than AI being trained to accommodate current pipelines. That is why compliance processes are more important than ever. Types of AI systems commonly used in DevOps include:

• Machine learning
• Natural language processing
• Chatbots and virtual assistants
• Computer vision

It is also a good idea to address the tools from three DevOps giants: GitHub Copilot, Atlassian Intelligence, and GitLab Duo. While these tools are widely used and definitely support automation, efficiency, and productivity – there are still security risks to watch out for as well as relevant regulations and standards. For example, GitHub Copilot is also trained on public data meaning it is prone to all the aforementioned risks (package hallucination, compromised training data, sensitive data exposure).

Two new ways of exploiting GitHub Copilot have emerged. The first one is a vulnerability linked to AI’s nature of giving helpful feedback to the user. However, threat actors can embed chat interactions inside of Copilot code, in order to produce malicious outputs. The second way of exploitation involves rerouting Copilot using a proxy server so that OpenAI models Copilot integrates with can be communicated directly. Thus, it’s important to make sure to:

• pay attention to regulatory requirements,
• look out for emerging DevOps threats,
• and always have a risk management strategy.

AI regulations in Europe

In 2021, the first comprehensive regulatory framework for artificial intelligence was introduced by the European Commission. The EU AI Act puts different AI systems into categories based on the level of risk they pose – the higher the risk the more strict the regulations will be. The purpose of the initiative is to promote compliance management, keep technological progress secure and ethical while setting a global standard for taking responsibility for AI use. 

In terms of AI, 2024 brought some changes. The European AI Act was adopted by the European Union Parliament in March 2024, then it was officially approved and put into effect in May 2024 by the Council. This is a crucial step towards ensuring global safety for AI use and stands as a great achievement. While the full implementation is scheduled for 2026, regulatory adherence for some of the frameworks will be required sooner. 

Penalties for non-compliance

Violating the EU AI Act and other applicable laws and regulations will result in fines. These will either be a set price or a percentage of the annual turnover – whichever is higher. Make sure to develop a comprehensive compliance and risk management strategy.

• 7.5 million euros or 1% of global annual turnover for providing inaccurate, insufficient, or misleading information to competent authorities.
• 15 million euros or 3% of global annual turnover for failing to meet requirements set for operators, authorized representatives, importers, distributors, deployers, or notified bodies (Articles 16, 22-24, 26, 31, 33, 34, and 50).
• 35 million euros or 7% of annual worldwide turnover for the use of forbidden AI systems under Article 5 of the Act.

The regulations of AI in the U.S.

In the U.S.there are many processes in place targeted at regulating AI and ensuring firms comply with relevant regulations. That is to ensure full AI compliance as well as safe use, fairness, and accountability. These frameworks are only in the development stage however they set out ground rules for the proper use of AI. 

By adhering to the following legislative measures and principles, organizations can better ensure that their AI systems remain safe, fair, and in accordance with regulatory compliance requirements set out by U.S. AI and data protection frameworks. Here’s an overview of the main pertinent laws.

National Artificial Intelligence Initiative Act of 2020

This AI governance framework revolves around the research, development, and evaluation of artificial intelligence across federal science agencies. It lays the groundwork for compliance regulations of the American AI Initiative and aims to increase AI-related activities in different federal institutions.

AI in Government Act & Advancing American AI Act

The purpose of this act is to accelerate the adoption and integration of artificial intelligence within federal agencies. Policies for compliance management and effective use of AI technology will also be put into effect and the rate of AI use will increase within government operations.

Learn more about AI in Government Act and Advancing American AI Act

Blueprint for an AI Bill of Rights Principles

This is a set of guidelines and best practices to ethically and responsibly use AI-powered systems – it’s not a legal requirement. Key recommendations focus on safety, rigorous testing and monitoring, algorithmic fairness, data protection, transparency, and user control. Moreover, it provides options to opt out of AI systems and ask for human intervention.

International standards regulating AI

What is ISO?

ISO (International Organization for Standardization) was established in 1946 and sets these global best practices for different processes, to improve efficiency, security, and international trade. Being compliant with ISO boosts the reputation of an organization and also aligns with industry regulations (voluntary – not a legal requirement). Even though it is not mandatory, achieving ISO compliance boosts businesses to improve different areas in their processes. Companies struggling to pay ISO/IEC standards compliance fees may simply not be ready for advanced practices like AI technologies implementation.

Takeaway

We can all agree that AI’s evolution has transformed DevOps. The automation that is now possible is great for productivity but introduces risks like sensitive data exposure or data poisoning. That is why responsible AI development as well as AI compliance with relevant regulations, such as the EU AI Act and U.S. laws, (depending on where your business is being operated) is rather important to manage these risks. We have international standards in place, such as ISO/IEC. They offer useful guidance to help businesses stay following global regulations and improve their AI practices. Make sure to use AI technologies carefully. This means implementing proper compliance and risk management strategies to avoid potential legal and financial consequences and potentially save your company money. Keep your data backed up, and implement proper access controls along with monitoring capabilities.

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