In an ideal world, we could expect AI solutions to provide flawless data security and complete personal privacy. Unfortunately, achieving zero risk in AI privacy is impossible due to objective factors like the complexity of data systems, the fast pace of the development of the technology itself, and the vast amounts of data that AI technologies require.
Since eliminating all risks isn’t realistic, let’s focus on how these risks can be effectively mitigated.
This article is written with two goals in mind:
- To define the key data privacy and security risk areas in generative AI. We’ll start by considering real-world examples and then generalize risk areas to provide you with a helicopter view of the topic.
- To explore how these risks can be minimized. Here, we’ll provide an overview of existing regulations and delve into Privacy by Design, an approach to safeguard data privacy in AI systems.
By understanding where AI-related threats originate and how they can be managed, business leaders can make smarter decisions and avoid compromising data privacy in the most effective way.
Exposing Hidden AI Risks Through Real-World Cases
Why not start with authentic examples in which data privacy was under threat in AI systems rather than spinning the theoretical yarn?
We’ll base our research on three aspects—an issue, consequences, and lessons—to get the most out of our research on the relationships between privacy issues and AI technologies.
Microsoft’s Accidental Data Exposure: Poor Access Control Risks
Microsoft, a company that positions itself as a trusted leader in the AI industry, was left embarrassed after accidentally leaking 38TB of confidential data on its GitHub page.
An issue
A Microsoft employee accidentally posted a GitHub link with a security token, exposing internal storage data, including backups from two former employees' workstations. It was a part of the process of contributing data for the open-source AI model training.
Consequences
While no customer information was compromised, the exposure raised concerns about Microsoft’s data security practices.
Lessons learned
Microsoft recognized the need for stricter controls on how access tokens are created and managed. They improved their security systems to better detect overly permissive access tokens and updated their internal practices to prevent similar incidents in the future.
Amazon’s ChatGPT Misuse: A Data Privacy Wake-Up Call
Amazon got burned by using ChatGPT and revised its policy on the use of AI-based third-party tools in areas involving confidential information.
An issue
Amazon employees used ChatGPT for everyday tasks like coding and customer service—a promising practice if managed properly.
However, in this case, Amazon specialists were astonished to notice that ChatGPT’s responses suspiciously resembled the content of the company’s internal documentation. (This happens when sensitive information becomes a part of the data set for training an AI model.)
Consequences
Amazon faced internal confusion and operational disruption as legal teams warned employees against using ChatGPT for work, fearing data privacy breaches.
Lessons learned
Amazon reinforced internal policies to restrict the sharing of confidential data with third-party AI tools and highlighted the need for stronger data governance. Additionally, the company accelerated the development of its own secure AI system to reduce dependency on external technologies-—a decision driven, in part, by the financial losses in this case, which Walter Haydock's research estimates at $1,401,573.
Slack AI Security Breach: How Prompt Injection Exposed Private Data
Luckily for Slack, an AI-related vulnerability was detected by a security company. There was no data leak, but an unpleasant aftertaste remained.
An issue
Slack AI demonstrated vulnerability to prompt injection attacks, allowing attackers to manipulate the system into leaking sensitive data (API keys) from private channels. While Slack representatives argued that the observed behavior was "intended functionality," the software design inadvertently introduced a security risk.
Consequences
The public disclosure of the vulnerability raised serious concerns about the platform’s security.
Lessons learned
We can’t say for sure what lessons Slack may have learned, but we can emphasize that this case highlights the importance of implementing stricter access controls and limiting AI's ability to interact with sensitive data.
Clearview AI: Unchecked Data Collection and Its Global Fallout
Clearview AI paid a high price—literally—for collecting and distributing personal data without user consent.
An issue
Clearview AI illegally collected and stored over 30 billion facial images scraped from social media and the internet without user consent, creating a vast biometric database. This data was sold to law enforcement and intelligence agencies, violating global data privacy laws, including the EU's General Data Protection Regulation (GDPR).
Consequences
The Dutch data protection authority fined Clearview €30.5 million. However, fines alone do not fully capture the impact of such cases—legal scrutiny and public criticism can pose significant reputational risks.
Lessons learned
This case highlights the critical importance of obtaining explicit consent when collecting biometric data and the need for stricter global enforcement of privacy laws. It also underscores how aggressive data collection without transparency can lead to severe legal and reputational consequences for tech companies.

While these cases effectively illustrate certain risks in the domain of AI and privacy of data, they don't comprehensively cover all potential areas.
To achieve our goal of addressing the full spectrum of possibilities, we now present a complete and structured list.
AI-Related Security Risks and How to Mitigate Them
AI-related risks in the data privacy domain should be closely linked to strategies for mitigating those risks. This will be our approach in the next set of insights.
The Big Data-Driven Nature of AI
Let’s imagine AI as a schoolboy—a curious learner who gleans information from everywhere. He’s incredibly inquisitive, absorbing vast amounts of knowledge from countless sources.
However, he doesn’t fully understand why he’s learning or what his ultimate goals are. Without proper guidance from responsible adults, this boy might unintentionally share fascinating facts, pictures, or sensitive details about someone.
In a worst-case scenario, he can even be misled by people with malicious intentions and manipulated into revealing secrets he should never share.
In other words, when AI systems lack oversight and control, the risks of breaches of privacy are high.
Examples of risk in action
- An AI assistant shares private user details due to conflicting or overlapping information in its training data.
- A user exploits the AI through jailbreaking techniques, manipulating it to disclose sensitive company information.
Mitigation strategies
- Data scrubbing. Just as the schoolboy needs responsible adults to guide his learning, AI requires well-scrubbed datasets to ensure no sensitive or private information sneaks into its training.
- System prompts. Like teaching the boy clear rules about what he can and cannot share, AI needs restrictive system prompts to establish boundaries in its behavior.
- Monitoring and testing. Just as a teacher observes a student’s progress, AI systems must undergo continuous monitoring to detect and address unsafe or unintended outputs.
- Anti-jailbreaking measures. Implementing safeguards to recognize and block malicious attempts prevents AI from sharing unauthorized information.
Misguided AI Profiling
Imagine our curious schoolboy again, eagerly observing everything around him.
He doesn’t just limit himself to the classroom chatter—he collects information from everywhere: notes left on desks, conversations in the hallway, drawings on the blackboard, and even diaries he manages to peek into.
In the AI world, this is equivalent to gathering data from diverse sources like social media activity, geospatial data, purchase histories, and even health metrics from fitness apps.
Having all these piles of information at hand, the schoolboy starts forming profiles of his classmates, piecing together who likes what, who’s good at sports, who might suffer from diabetes, and who has a strained relationship with their parents.
Notably, the boy’s observations are not always accurate. Interestingly, that doesn’t stop him from drawing conclusions. For instance, he might tell the entire class that Timothy’s parents are going through a divorce or that Stephanie’s mom is frequently visiting a doctor for stress management.
Examples of risk in action
- A recommendation engine suggests pregnancy-related healthcare products to a woman based on data from her fitness app. The woman never consented to the app detecting her pregnancy status, and the recommendation mechanism manipulates her product choices by exploiting inferred private details.
- An AI-based business intelligence (BI) tool analyzes a user's purchase history and incorrectly estimates their financial well-being. Based on this flawed assessment, a bank denies the user a loan, causing financial and emotional distress.
Mitigation Strategies
- Limit data collection. Avoid gathering unnecessary personal information from sources like social media or fitness apps unless it is directly relevant to the task of AI algorithms.
- Consent and transparency. Clearly inform users about what data is being collected, why it’s being used, and how it will be protected. Obtain explicit consent for profiling activities.
- Restrict inferences. Implement safeguards to prevent the AI from inferring overly sensitive or irrelevant personal details.
Weak Data Management and Security Practices
The following two risk areas in AI and privacy of data focus on technical and organizational aspects rather than the nature of AI itself. Thus, we can confidently let our schoolboy return to his lessons while we continue our journey.
So, data management. With the current level of technological development, it might seem that data protection is no longer a concern. But that's not the case!
In 2024, data breach incidents increased by 10% compared to 2023, with the average cost reaching an all-time high of $4.88 million. The number one reason for potential data breaches is poor data management and weak security measures in data processing.
Examples of risk in action
- A company stores personal information without proper encryption.
- An organization fails to regularly update security systems.
Mitigation strategies
- Strict data access controls. Limit data access to only those employees and systems that need it.
- Encryption for data protection. Encrypt sensitive data both at rest and in transit to protect it from unauthorized access or theft.
- Regular security audits and updates. Conduct regular security assessments and implement software updates to patch vulnerabilities.
- Data minimization. Collect only the data necessary for business operations.
- Opt-in and opt-out mechanisms. Implement clear and user-friendly opt-in and opt-out options to ensure users have control over their data. Opt-in allows users to actively consent to data collection, while opt-out ensures they can withdraw consent or decline participation at any point. These mechanisms not only align with regulatory requirements but also build user trust and transparency.
Third-Party and Supply Chain Dependencies
If you’re building or running your AI-based solution on your own, you can skip this part of the article. Greetings! However, it should be noted that companies independent of partners are in the absolute minority.
Most are connected with a number of vendors across various domains, including cloud services, data management services, APIs, and SaaS providers.
A third-party vendor can compromise an organization's security in the artificial intelligence and privacy domain for several reasons, including inconsistent security protocols, varying compliance standards, and inadequate protection measures for personal information.
Examples of risk in action
- A cloud storage provider fails to encrypt stored data.
- A third-party AI tool processes sensitive customer data without proper consent.
- A vendor's system is compromised by cybercriminals, creating a backdoor into the organization’s internal network.
Mitigation strategies
- Thorough vendor risk assessments. Evaluate third-party vendors for security practices and data handling procedures.
- Clear data handling agreements. Implement comprehensive contracts with vendors that define data usage, storage, and protection responsibilities.
- Audit of the third-party security practices. Perform routine audits and assessments to ensure vendors maintain robust security measures.
- Limited data sharing. Share only the minimum amount of sensitive data necessary for the vendor to perform their services.
- Vendor access controls. Restrict vendor access to critical systems and personal information through role-based permissions and secure data transfer protocols.

Summary of Key Considerations for Mitigating Data Privacy Risks
Is it sufficient to have a generalized vision of the risk areas to avoid these risks? The answer is no. The most challenging part of AI-related risk management is that the real cases are rarely based on one particular type of risk.
As we know from the real-world examples mentioned above, risks amplify each other’s consequences and create complex challenges.
In particular, in the case of ChatGPT misuse in Amazon, the dependency on third-party tools and data management flows played a crucial role.
Similarly, in the case of Slack, the flawed AI design created fertile ground for a jailbreak attack and played right into the attackers' hands, leaving little chance for reliable data protection.
There could be only one solution — a multilevel policy.

Formal regulations set the framework for strategic decision-making, but within individual companies, tech and business leaders must take responsibility for ensuring the protection of personal data. This dual approach bridges the gap between broad legal guidelines and the specific actions needed to safeguard data privacy at the organizational level.
The good news is that by establishing the two-level privacy protection policy, you cover most of the ethical AI principles, such as transparency, explainability, privacy and data protection, and human-centricity.
By implementing this comprehensive policy framework, organizations not only mitigate privacy risks but also build trust with their stakeholders, ensuring sustainable growth in the rapidly evolving AI landscape.
Building a Safer AI Future: Regulations and Privacy by Design
The part of our journey related to data privacy legislation in AI will be breathtaking, as a few topics can compete with artificial intelligence in complexity and the number of aspects—strategical, technical, and ethical—involved.
The Global Framework - Regulations for AI and Data Privacy
There are about 3,000 global regulations in the food and beverage industry. Over 25,000 laws protect the rights of consumers of all kinds of goods worldwide.
The AI industry is regulated by a few legislative norms, and it would be far from the truth to claim that these legal provisions cover all instances where data privacy is at risk.

The positive trend, however, is that the legislative foundation for protecting data privacy has been established and continues to evolve in line with changes in both the technology itself and the conditions under which it is used.
The regulations are mandatory within their territorial jurisdiction, and violations are penalized with fines. Therefore, developers of AI-based solutions would do well to know these regulations inside and out, even though they are often written in complex bureaucratic language and tend to be extensive (for example, the General Data Protection Regulation (GDPR) consists of 11 chapters).
For our purposes, a brief overview will suffice, providing an understanding of which regulations exist, the countries and regions they apply to, and how they generally protect data privacy in the artificial intelligence domain.
Overview of Data Protection Laws and AI-Specific Regulations
Despite differences in terminology and nuances in how certain terms are treated (which becomes apparent when delving into these documents), the key acts governing data privacy regulation share a great deal in common.
The AnyforSoft team of ML engineers and software developers refers to these shared principles as the five pillars of data privacy in AI.

The similarity of principles and approaches in various regulatory documents highlights the absence of a unified global standard. Many acts overlap in their concerns and solutions, yet a global source of directives still doesn’t exist.
Hopefully—taking into account the rapid pace of AI development—a comprehensive set of legislative directives will emerge to provide concise, unambiguous, and efficient data privacy protection worldwide.
Beyond Compliance—Privacy by Design Approach
While regulations lay the foundation for protecting data privacy, they don’t cover all the cases where issues may arise. The number one reason for that is the neck-wrecking speed of AI development: tomorrow, you can face a brand-new challenge that is unimaginable today.
The only way to build truly secure and responsible AI systems is to go beyond compliance and embrace proactive strategies—like Privacy by Design.
What is the essence of this approach, and why can it be considered a versatile methodology? Its strength lies in embedding data protection considerations into the core of AI systems from the outset, and that’s why it remains actionable at each point of AI technology advancement.
Is a Privacy by Design approach a philosophy, methodology, or a set of technical measures? It's all three, and it makes the question, “How can I implement this approach in my organization?” complex indeed.
That said, every journey begins with a single step! The foundational steps of the Privacy by Design methodology are universal, making them applicable to a wide range of solutions, whether you’re exploring AI solutions for education or tools for chatbot consulting. As a bearer of this methodology, the AnyforSoft team is glad to share these steps to help get you started.

Final Thoughts: Building Trust Through Data Privacy in AI Solutions
As AI technology evolves, safeguarding data privacy is no longer optional—it’s a cornerstone of building trust and ensuring long-term success.
For business leaders, achieving this goal requires focusing on two essential strategies:
- Leverage regulations. Use frameworks like GDPR and the European AI Act as a foundation to guide your data privacy practices and maintain compliance. Regulations set the baseline for protecting sensitive data and addressing privacy challenges in the use of AI-based solutions while also ensuring robust privacy policies.
- Embrace Privacy by Design. Go beyond compliance by embedding protecting privacy considerations into every stage of your AI solution’s development. This proactive approach fosters user trust, minimizes privacy risks, and creates adaptable systems ready for future challenges.
As AI data scientist and ethics pioneer Fei-Fei Li emphasizes, while artificial intelligence is a powerful tool created by humans to assist humanity, it is ultimately humans who must exercise ownership and agency over it.
By aligning your practices with regulations and Privacy by Design principles, you’ll position your organization for innovation while protecting the integrity of the data you rely on.