AI systems have changed how data is processed and handled. In simple terms, data is AI’s “food,” as large language models learn from huge datasets to create new intelligence. The catch is that they remember the information that you paste into them, which means it can be shared with other users.
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AI also merges text, images, and documents together. This makes it difficult to track an individual piece of data. At the same time, tech companies and threat actors are quietly scraping billions of unmonitored data points from all over the web, raising privacy concerns, creating security vulnerabilities, and posing compliance challenges.
In this blog, we will explore data privacy and data security in AI and practical steps on how to stay secure.
AI data privacy vs. AI data security: what’s the difference?
Rather than viewing them as interchangeable, it’s more useful to compare the role each plays:
AI data privacy controls who owns the data, the type of consent granted, and how the data is collected, used, stored, and shared.
AI data security focuses on protecting training data and prompts from exploitation, poisoning, or unauthorized exfiltration.
Here are the key differences between AI data privacy and AI data security:
| Aspect | AI data privacy | AI data security |
|---|---|---|
| Purpose | Responsible use of personal data that complies with data privacy regulations (e.g., GDPR, CCPA). | Protection of AI datasets, prompts, and outputs from unauthorized access, theft, or misuse. |
| What it protects from | Prevents AI from ingesting, memorizing, and exposing personal data. | Prevents malicious actors from poisoning inputs or hijacking outputs. |
| Main risks | Data scraping by AI systems and the lack of the right to be forgotten. | Prompt injection, data poisoning, model inversion attacks, and data leaks. |
| The key question it asks | Can we use a person’s data, and do we have their consent to use it? | Is our AI data protected from unauthorized access, theft, or misuse? |
| How it is achieved | Data minimization, anonymization, and granting users consent options. | Strict access controls to AI tools, encryption, input, and output filtering. |
Data privacy and security concerns
Let’s start by explaining how AI is different from “traditional” technology. Computing usually follows fixed rules. A programmer writes step-by-step instructions, and the system follows them. AI works differently. It uses machine learning to learn from data. A human feeds an AI model with data, and the model figures out the patterns itself, using statistical probability. Now, does this create problems for data privacy and security?
Data privacy. An AI system may use data about location, browsing history, purchases, or work behavior in ways that reveal sensitive details about a person.
Data security. The more data that enters an AI system and appears in prompts, logs, outputs, or third-party platforms, the more it is exposed to risk.
The black-box nature of AI
An AI system works like a black box. It discovers a pattern, but a human can’t see how it reached its result. The whole decision-making process is hidden, creating risks of bias. For example, if a healthcare AI tool is trained on data from a time when certain patient groups received less care, it may underestimate their current medical needs.
But why does the black box problem matter for AI data privacy and security? Here are a few reasons:
Data exfiltration. Threat actors can trick AI models into revealing the private data they have memorized. As a result, a company may not even know a data leak is happening.
Bug exploitation. AI models also have bugs. Because of the black-box nature of AI, security teams can find it hard to identify and patch these weak spots before they are exploited.
Data poisoning. Cybercriminals can manipulate training data. If an AI model learns from fake or malicious information, it will produce incorrect or dangerous results.
Data leaks. If employees feed personal or sensitive company data into an AI model, it may memorize it, depending on how the system is configured.
No surveillance and bias. If there is a security breach or an AI-made bad decision, it would be impossible to work out if the issue was an accident, a cyberattack, or just a software glitch.
Compliance issues. The black box problem also makes audits harder, as proving compliance with an AI model is a challenge.
The most common AI data privacy attacks
AI can be your best friend or your worst enemy. It boosts productivity, but it also helps cybercriminals extract sensitive data and carry out sophisticated attacks. Here are a few of the most common AI data privacy attacks.
Membership inference attacks (MIAs)
When an AI model encounters a piece of data it has processed before, it reacts differently than it would to brand-new information. This behavior helps bad actors identify memorized responses, which are exploited in two types of MIAs:
Confidence-based attacks. Cybercriminals feed a specific input to an AI model to determine if that particular data was used to train the model. If the model responds with confidence, the attackers conclude that the model was trained using that particular data.
Shadow-based attacks. Threat actors build their own lookalike shadow AI models that look like the target model using datasets they control. By observing how these clone models react to known training data, the attackers learn exactly what a “memorized” response looks like, then they apply that template to reverse-engineer the target AI model.
These attacks show that bad actors don’t need to steal an entire database to violate data privacy. An MIA can quietly reveal sensitive information.
Model inversion and reconstruction attacks
Threat actors recover sensitive information about the training data from an AI model. There are two types of these attacks:
Model inversion attacks. Attackers repeatedly query the model to find inputs that can be used to reconstruct sensitive training assets or private intellectual property.
Training data extraction. Hackers use targeted prompts to make the model reveal data it may have memorized during training. In some cases, this can result in the surface of near‑verbatim training data.
Model inversion and reconstruction attacks can leak sensitive data, such as PHI, PII, trade secrets, or confidential documents embedded in training data. This may lead to violations of privacy regulations and contractual obligations, even if the model was never explicitly designed to reveal such information.
Attribute inference and linkage attacks
Both of them disclose private information about a user because of AI’s ability to connect unseen dots. However, while attribute inference attacks use available data to guess hidden personal details, linkage attacks match records across datasets to identify a person. Here is how they work:
Attribute inference. A cybercriminal uses a machine learning model or dataset to guess hidden or sensitive information about a user, such as their health condition or income.
The technique. Threat actors use the information they already know about a person, such as their age, to send queries to the model and guess more personal details.
Linkage attacks. Adversaries use an anonymized dataset and match it with another public dataset in order to re-identify a person.
The technique. In this attack, bad actors look for data points that overlap (quasi-identifiers, such as a birth date or an address) and can be found in both the anonymized datasets and public directories.
Attribute inference and linkage attacks can expose sensitive data and threaten personal anonymity.
Security measures for protecting data in AI
If you want to keep your data safe in an AI model, you must secure the whole AI workflow, including training datasets, model architecture, user prompts, and outputs.
Differential Privacy (DP)
DP is a mathematical framework used for analyzing trends across large datasets. What’s great about DP is that it hides individual details. It intentionally adds a tiny bit of random “noise” into the data pool. The system only makes small, safe changes to search results or AI models. This blurs individual records and makes them impossible to isolate.
Federated Learning (FL)
AI models need large datasets, usually centralized in a single location, to train on a single central server. This exposes the sensitive information stored there to risk. FL works differently. It trains models across separate devices or servers while keeping the raw data in its original location. However, FL may not ensure data privacy because model updates may still reveal sensitive information. Organizations often combine FL with encryption, secure aggregation, access controls, and DP.
AI governance
It is a framework that guides businesses on how to use AI responsibly and ethically. It defines best practices for secure AI management. From AI inventory and risk assessment to access controls, monitoring, and auditing, AI governance helps businesses meet regulatory compliance.
One of the best practices of AI governance is data minimization and masking. Before you feed any data into an AI model, strip out or replace personal details with synthetic ones or placeholders. This way, you can keep your sensitive data safe.
Access controls
If you want to secure your data in AI, deploy a comprehensive set of security measures, such as:
Zero-trust architecture. The system must continuously verify every single user and device that attempts to access your AI assets before granting access.
Encryption. Encrypt your data both when it is stored and when it moves across networks.
Role-based access control (RBAC). It restricts user access to AI tools based on their role and job function.
Secure access to AI. Using a centralized credential manager helps prevent unauthorized access to your AI accounts. It also enables employees to securely access company-approved AI tools without seeing, knowing, or sharing the passwords.
A review of privacy protection laws
The General Data Protection Regulation (GDPR)
GDPR controls how organizations in the EU handle personal data. It forces companies to specify exactly why they need data and to collect only the minimum that is needed for a specific purpose. Users have the right to access, edit, or delete their data. The GDPR also states that personal data shouldn’t be stored longer than necessary, which means it must be permanently deleted the moment it’s no longer needed.
Penalties for violations range from €10 million or 2% of global revenue for administrative issues to €20 million or 4% of global revenue for infringements, such as unauthorized data transfers or invalid consent.
The EU Artificial Intelligence (AI) Act
This is the world’s first AI regulation. Instead of just regulating data, the AI Act regulates the technology itself. It bans, for example, invasive practices, such as the indiscriminate collection of facial images from CCTV networks and real-time public biometric tracking. High-risk AI applications face strict audits to ensure their training data is clean and unbiased.
Fines for prohibited AI practices can reach €35 million or 7% of a company’s global annual turnover. For SMEs and startups, the lower amount applies.
US privacy regulations
A few states have introduced their data privacy laws, such as the California Consumer Privacy Act (CCPA) and the Texas Data Privacy and Security Act. In 2024, Utah became one of the first states to regulate the use of generative AI with the passage of the Artificial Intelligence Policy Act.
However, the US has no federal law that covers AI and data privacy. In 2022, the White House Office of Science and Technology Policy released the nonbinding “Blueprint for an AI Bill of Rights.” Its 5 principles include data privacy, consent, and protection from inappropriate data use.
China’s Interim Measures for Generative AI Services
China was one of the first countries to introduce AI regulations. In 2023, Beijing rolled out its Interim Measures for Generative AI Services, which placed legal boundaries around content-producing models. Under this regulation, AI platforms are explicitly prohibited from infringing on citizens’ privacy, reputation, or personal information during model training and deployment.
How NordPass mitigates AI data privacy risks
NordPass Business is a credential management platform that helps organizations reduce AI data privacy risks by:
Securing credential sharing. It helps protect access to your business AI accounts. The Shared Folders feature allows you to securely organize and share logins. This means employees log in to centralized, company-approved AI models.
Preventing AI system hijacking. Data Breach Scanner monitors for leaked employee credentials or emails. This helps stop threat actors from stealing credentials and accessing your AI tools.
Eliminating credential-stuffing attacks. Passkeys provide a phishing-resistant and passwordless way to access your system.
To secure your AI systems and ensure data privacy, deploy a credential manager like NordPass Business.