AI Security Concerns & 5 Ways to Protect Data in AI Development

More than half of global businesses lack effective defense strategies against AI security concerns, even though they are increasingly concerned about the growing AI security risks. However, most are taking steps to address the issue.

Strengthening AI risk management has emerged as one of the leading AI security trends for 2025. We’ve outlined the leading approaches business leaders are adopting to develop AI systems that are secure, fair, and trustworthy — while minimizing privacy risks.

As generative AI tools like ChatGPT, DALL·E, and others become integral to enterprise operations, the urgency to manage associated security risks of generative AI has intensified. These models process vast amounts of data to generate human-like responses, images, or code — but this sophistication comes with substantial exposure to AI privacy and security vulnerabilities.

Let's break down the most pressing generative AI security risks and explore why robust safeguards are no longer optional, but essential.

#1. Data Exposure and Sensitive Information Leaks. Generative AI rapidly enhances cybercriminals' capabilities, driving a surge in sophisticated social engineering and phishing attacks.

In 2024, 42% of organizations reported experiencing cyberattacks. Furthermore, 72% of business leaders now report a rise in overall cyber risk, with ransomware continuing to top the list of concerns. More notably, 47% cite adversarial use of generative AI as a primary risk factor — pointing to AI's growing role in enabling highly targeted, scalable, and convincing attack vectors. Data breaches often stem from improperly secured training data, weak access controls, or misconfigured APIs. 

👀 For instance, in early 2023, Samsung banned the internal use of ChatGPT after an employee inadvertently entered sensitive source code into the platform. This highlighted how easily proprietary data can escape a company's control when using public AI systems, underscoring the critical need for AI security framework and prompt injection mitigation measures.

Without strict data governance, even anonymized datasets can leak insights that can be traced back to individuals or expose competitive information.

#2. Unintended Data Sharing (Proprietary or Personal Information). Many generative AI tools interface with external APIs or cloud services, raising the risk of unintended data sharing. Even if users don't explicitly consent, their data can be retained, reused, or shared across model updates. 

This creates a potential legal minefield under regulations like the GDPR, CCPA, or China's PIPL, highlighting the importance of AI vulnerability management and enterprise AI security controls.

#3. Training on Unsecured or Sensitive Datasets. When generative models are trained on unvetted datasets, including emails, health records, or internal documentation, they can inadvertently memorize and regurgitate sensitive snippets during everyday use. This issue becomes critical when the model is deployed publicly or integrated into customer-facing applications.

😱 A study by UIUC and Stanford University researchers found that large language models could reproduce specific social security numbers, names, and emails that were included in their training data — even if that data was supposedly anonymized.

Companies must ensure that all training data is thoroughly scrubbed, encrypted, and vetted for regulatory compliance to mitigate gen AI data security risks.

#4. Adversarial Threats: Data Extraction and Reconstruction. Generative models are increasingly vulnerable to adversarial attacks — techniques where attackers deliberately probe AI systems to extract or reconstruct sensitive data. These attacks exploit model behaviors to reveal confidential training inputs, sometimes with shocking precision.

😶‍🌫️ In a 2022 academic experiment, researchers successfully extracted sensitive medical details from a fine-tuned GPT model used by a healthcare company simply by crafting targeted prompts.

These attacks can compromise intellectual property, expose personal information, or even reveal strategic business operations. As AI capabilities grow, so does the sophistication of potential threats, making AI risk management and AI security assessment indispensable.

In this section, we explore the most effective techniques for protecting user data and minimizing exposure risks, including anonymization, encryption, differential privacy, and more.

AI systems increasingly rely on large volumes of personal and sensitive data and data security in AI through data anonymization and pseudonymization have become vital techniques for safeguarding privacy and ensuring compliance with regulations like GDPR, HIPAA, and CCPA. 

Although both anonymization and pseudonymization protect personally identifiable information (PII), they function differently — and are suitable for different use cases.

🕵🏻‍♂️ Anonymization is used when the goal is to eliminate any link to an individual. This is critical for datasets shared publicly or used for model training where no follow-up with the data subjects is needed.

🕵🏻 Pseudonymization, on the other hand, allows organizations to secure individual-level tracking. It is commonly used in healthcare, finance, or support operations where data may need to be re-linked under controlled conditions.

Virtually any organization using AI that touches personal or sensitive data will benefit. Key stakeholders include:

Best practices for implementation: to ensure effective and compliant use of anonymization and pseudonymization, organizations should

✅ Adopt a layered approach. Combine these techniques with encryption, strict access controls, and audit logging to build defense-in-depth.

✅ Secure the re-identification key (for pseudonymization). Store the mapping key in a separate, highly secured system, with access limited to authorized personnel only.

✅ Continuously test for re-identification risks. Run regular assessments to ensure anonymized data can't be de-anonymized with external datasets.

✅ Document and validate methods. Regulators and auditors may require evidence of your de-identification processes and their effectiveness.

While anonymization and pseudonymization remove or obscure identifiers within a single dataset, Secure Multi-Party Computation (SMPC) addresses a different challenge: how multiple parties can jointly compute insights over their combined data — without ever revealing their raw inputs. This makes SMPC an indispensable technique when collaboration and confidentiality must coexist.

How SMPC Works:

Secret Sharing of Inputs. Each participant splits their private data into cryptographic shares. Alone, a share reveals nothing about the original value; only by recombining a threshold number of shares can the accurate input be reconstructed.

Encrypted Computation. All computations (additions, multiplications) are performed on the shares themselves. At no point does any party see another's raw data, yet the group collectively executes the same function as if they had pooled their cleartext inputs.

Result Reconstruction. Once the calculations finish, each party holds a share of the result. By bringing together those result-shares, they recover the final output — again, without ever exposing individual inputs.

SMPC vs. Anonymization & Pseudonymization

Who Benefits Most from SMPC:

Best practices for implementation:

✅ Threat Model & Collusion Resistance. Define the maximum number of parties that could collude. Ensure your SMPC setup enforces security even if a subset of participants attempts to cheat — a critical aspect of AI model security.

✅ Integration with Other Controls. Combine SMPC with encryption-at-rest, strict access controls, and audit logging to build a defense-in-depth strategy.

✅ Regulatory Alignment. While SMPC doesn’t generate personal data leaks, document your protocol choices and risk assessments to satisfy auditors and regulators.

Differential Privacy is an innovative method for helping organizations share valuable insights about groups of people — without revealing anything about any one individual.

Think of differential privacy, like adding a bit of noise or fuzziness to the data. This noise makes it very hard for anyone to figure out if a specific person's data is included in the results. You get useful overall trends and patterns without risking anyone's private details.

How does it work:

How Is Differential Privacy Different from Other Privacy Methods:

Who Benefits from Differential Privacy:

The amount of noise added needs to be carefully managed to keep insights functional. It's also crucial to keep in mind that differential privacy is just one part of a bigger privacy strategy — often combined with other tools.

Data masking is a powerful security technique that replaces real sensitive information with scrambled or fake data that looks structurally similar to the original. This approach enables AI systems and analytics tools to work with realistic datasets without exposing confidential details, ensuring privacy while maintaining data usability.

How does it work:

— First, the system locates which data needs masking — names, social security numbers, credit card details, or email addresses.

— Depending on the data type, specific masking techniques are applied. For example:

Substitution: Replacing real data with fake but plausible alternatives (e.g., swapping real names for random names).

Shuffling: Randomly rearranging existing data within the dataset to break the link to the original.

Masking Out: Replacing parts of the data with symbols (e.g., showing only the last four digits of a credit card as **** **** **** 1234).

Encryption: In some cases, sensitive data is encrypted strings that only authorized systems can reverse.

Encryption, including homomorphic encryption, is a fundamental requirement for protecting sensitive information, especially under regulations like PCI DSS (Payment Card Industry Data Security Standard) and HIPAA (Health Insurance Portability and Accountability Act).

It is a cutting-edge cryptographic method that allows computations to be performed directly on encrypted data without ever needing to decrypt it first. When decrypted, the result exactly matches the result as if the operations were done on the original raw data. This means sensitive information can stay encrypted and protected even while being processed, drastically reducing the risk of exposure during data analysis or machine learning.

How does it work:

This advanced technique is especially valuable for organizations that must process highly sensitive data while preserving strict privacy and security, such as:

While homomorphic encryption requires significant computing power, ongoing research improves its efficiency. As these advancements continue, homomorphic encryption promises to become a cornerstone of privacy-first AI and data processing strategies in regulated and high-risk environments.

Beyond fundamental principles like data minimization and access control, there are several additional best practices that organizations must adopt to manage and mitigate privacy risks associated with AI systems effectively:

By limiting data collection to what is strictly needed, organizations can significantly reduce the risks associated with data breaches, unauthorized access, and misuse of sensitive information.

Clearly define the purpose for which data is collected before acquisition. Collect data only if it directly supports that purpose, avoiding gathering information “just in case” it might be helpful later. Limit the types and categories of data collected to those relevant to the task. For example, if an AI agent only needs anonymized demographic data for analysis, avoid collecting detailed personal identifiers like full names or contact information.

Establish clear retention policies that define how long data is stored and when it must be securely deleted. Retain data only as long as necessary to fulfill the intended purpose or comply with legal requirements, then securely delete or anonymize it. 

Periodically review the data collected to identify and remove any unnecessary or redundant information. This practice ensures ongoing adherence to minimization principles as business needs evolve.

Regular audits and penetration tests are essential to uphold data privacy and security in AI development.

✅ Vulnerability Scanning: Routinely check AI systems for known security weaknesses in code and infrastructure as part of AI vulnerability management.

✅ Penetration Testing: Simulate cyberattacks to uncover and address system vulnerabilities, including those in AI algorithms, data storage, and communication channels before malicious actors exploit them.

Data privacy and security require collective responsibility across the organization. Foster a culture of security awareness among AI developers, business leaders, and end-users.

✅ Training: Provide regular AI security training for all team members involved in AI development.

✅ User Awareness: Inform end-users how their data is used and how to protect their privacy, including adjusting settings or opting out of data collection when possible.

Partnering with a trusted and experienced software development company specializing in AI ensures that data privacy is prioritized and effectively managed throughout the AI lifecycle.