A Comprehensive Guide to Protect Data, Models, and Users in the GenAI Era

Editor's Note: The following is an article written for and published in DZone's 2025 Trend Report, Generative AI: The Democratization of Intelligent Systems.

Generative AI (GenAI) is transforming how organizations operate, enabling automation, content generation, and intelligent decision making at an unprecedented scale. From AI-powered chatbots to advanced code generation and creative design, GenAI is revolutionizing industries by increasing efficiency and innovation. However, alongside these advancements come significant security risks that organizations must address.

The challenge is that as AI systems become more intelligent and sophisticated, they also face evolving threats and risks. Ensuring AI security throughout development and deployment is crucial.

This article provides practical checklists to help enterprises securely adopt GenAI. By understanding key security risks, implementing essential technologies, and following best practices, organizations can harness the power of GenAI while ensuring their data, models, and users remain protected.

The checklists are separated into two categories:

• Key security risks of GenAI
• Essential security technologies for GenAI

Key Security Risks of Generative AI

GenAI introduces new security risks that organizations must address. Threats include data leaks, model manipulation, and unauthorized access. These risks can lead to serious privacy and security breaches without proper safeguards.

1. Data Privacy and Compliance Risks

Generative AI can expose sensitive data, leading to legal violations under regulations like GDPR and HIPAA. Organizations face legal, financial, and reputational risks if AI models process confidential information without safeguards. Ensuring compliance requires strict data handling, access controls, and regular audits.

For example, in 2023, Samsung employees accidentally leaked confidential company data by entering it into ChatGPT, raising serious concerns about corporate data privacy and AI misuse. Learn more about the accidental data leak here.

Here are steps to address data privacy and compliance risks:

☐ Restrict AI access to sensitive data using role-based controls

☐ Implement data anonymization and encryption before AI processing

☐ Audit AI interactions for compliance with GDPR, HIPAA, etc.

☐ Use AI governance tools to enforce data protection policies

2. Misinformation and Bias

AI models can generate false or misleading information, commonly called hallucinations. AI may reinforce stereotypes and produce unfair outcomes if trained on biased data. Organizations must ensure that AI-generated content is accurate, ethical, and free from bias. An incident of this nature occurred in 2023 when an AI-powered news website published misleading and fake articles, causing public misinformation and damaging its credibility. To avoid misinformation and bias:

☐ Test AI models regularly for bias and accuracy

☐ Use diverse, high-quality training data

☐ Implement human review for critical AI outputs

☐ Establish AI ethics guidelines to ensure responsible usage

3. Unauthorized Access and Misuse

Unauthorized users can access AI models without proper security measures, leading to data theft or manipulation. Both insiders and external hackers pose a risk, especially if API security is weak or misconfigured. In one case, a misconfigured AI chatbot publicly exposed user conversations due to API vulnerabilities, compromising privacy. Here is a checklist to prevent unauthorized access and misuse issues from happening to you:

☐ Enforce multi-factor authentication (MFA) for AI access

☐ Implement role-based access controls

☐ Monitor AI activity logs for suspicious behavior

☐ Conduct regular security audits and penetration tests

4. Data Poisoning

Attackers can manipulate AI training data by injecting malicious inputs and corrupting model outputs. This can lead to biased decisions, misinformation, or exploitable vulnerabilities. In one experiment, researchers demonstrated how poisoning AI datasets could manipulate facial recognition systems, causing them to misidentify people. Here is a checklist to prevent data poisoning:

☐ Validate and clean training data before AI processing

☐ Use differential privacy to prevent data manipulation

☐ Deploy anomaly detection tools to identify poisoned data

☐ Retrain models with verified and diverse datasets

5. Fake "ChatGPT" and Impersonation Attacks

Fraudsters create fake AI tools mimicking ChatGPT or other AI services to trick users into sharing sensitive data or installing malware. These fake versions often appear as mobile apps, browser extensions, or phishing websites that look nearly identical to real AI platforms. Some have even been found in official app stores, making them seem more trustworthy to unsuspecting users. Once installed, they can steal login credentials and financial information or even spread harmful software across devices.

Here is a checklist to prevent fake "ChatGPT" and impersonation attacks:

☐ Use only official AI tools from verified sources

☐ Educate employees on fake AI and phishing scams

☐ Deploy security tools to detect fraudulent AI services

☐ Report fake AI platforms to authorities

6. Model Stealing

Attackers can extract proprietary AI models by exploiting APIs and analyzing responses, leading to intellectual property theft and competitive disadvantage. As found in North Carolina State University's research, "Researchers have demonstrated the ability to steal an artificial intelligence (AI) model without hacking into the device where the model was running. The technique is novel in that it works even when the thief has no prior knowledge of the software or architecture that supports the AI."

Figure 1. Model-stealing process

The diagram illustrates the model-stealing process, where an attacker sends multiple queries to a target machine learning model and collects the corresponding responses. Using these inputs and outputs, the attacker then trains a stolen model that mimics the behavior of the original, potentially leading to intellectual property theft and unauthorized use.

To prevent model stealing:

☐ Limit API access and enforce request rate limits

☐ Encrypt AI models during deployment

☐ Use watermarking to track unauthorized usage

☐ Monitor API activity for suspicious extraction patterns

7. Model Inversion Attacks

Hackers can reverse-engineer AI models to recover sensitive training data, potentially exposing confidential or personal information. In one instance, researchers reconstructed faces from a facial recognition AI model, revealing private user data used in training. Andre Zhou gathered a list of resources and research related to model inversion attacks in his GitHub Repository.

A model inversion attack is similar to a model stealing attack. A model inversion attack extracts sensitive training data by analyzing model outputs, infers private input data, posing a privacy risk, and grants attackers access to confidential or personal data. Meanwhile, a model stealing attack replicates a target model’s functionality using queries and responses, enables intellectual property theft by recreating the model, and allows attackers to obtain a functional copy of the model’s behavior.

Here are steps you can take to prevent model inversion attacks:

☐ Use differential privacy to protect training data

☐ Restrict model exposure by limiting API responses

☐ Apply adversarial defenses to prevent inversion attacks

☐ Assess AI models for vulnerabilities regularly

8. AI-Enhanced Social Engineering

AI can generate highly realistic phishing emails, deepfake videos, and voice impersonations, making social engineering attacks more effective. For example, cybercriminals used AI-generated voices to impersonate company executives at a European company, successfully authorizing fraudulent financial transactions amounting to €220,000.

The following are measures that can be taken to prevent AI-enhanced social engineering:

☐ Train employees to recognize AI-generated scams, using open-source tools like Google's SynthId         (or commercial tools)

☐ Deploy AI-powered security tools to detect deepfakes

☐ Use multi-factor authentication for financial transactions

☐ Monitor communications for unusual patterns

Essential Security Technologies for GenAI

Securing generative AI means using encryption, access controls, and safe APIs. Monitoring tools catch unusual activity, and defenses protect against attacks. Following privacy rules helps keep AI use safe and fair. We also need to consider the following topics to improve the security level when utilizing AI.

1. Data Loss Prevention

Data loss prevention (DLP) solutions monitor and control data flow to prevent sensitive information from being leaked or misused. Here are some ways to incorporate DLP solutions:

☐ Use AI-driven DLP tools to detect and block unauthorized data sharing

☐ Apply strict data classification and access policies

☐ Monitor AI-generated outputs to prevent unintentional data leaks

☐ Regularly audit logs for suspicious activity

2. Zero-Trust Architecture

Zero-trust architecture (ZTA) enforces strict access controls, verifying every request based on identity, context, and least privilege principles. Here is a checklist to implement zero-trust architecture:

☐ Implement MFA for AI access

☐ Use identity and access management tools to enforce the least privilege

☐ Continuously monitor and verify user and AI interactions

☐ Segment networks to limit AI system exposure

You can find a detailed guide about zero-trust architecture here.

Figure 2. Zero-trust architecture

3. Encryption and Confidential Computing

Encryption secures AI data at rest and in transit, while confidential computing protects sensitive AI operations in secure environments. Here is a checklist to implement encryption and confidential computing:

☐ Encrypt data using AES-256 for storage and TLS 1.2+ for transmission

☐ Use hardware-based secure enclaves for AI processing

☐ Implement homomorphic encryption for privacy-preserving AI computations

☐ Regularly update cryptographic protocols to prevent vulnerabilities

Conclusion

Securing generative AI means taking the proper steps to protect data, models, and users; therefore, organizations must continuously improve their security strategies and proactively address key security risks. This can be done in part by incorporating strong access controls, data protection policies, and regular security tests, and doing the proper research to ensure organizations are meeting their own needs as well as regulatory requirements. By following the checklists presented in this article, organizations can safely and innovatively use generative AI.

References:

• "Fake ChatGPT apps spread Windows and Android malware" by Graham Cluley
• "DeepSeek Data Leak Exposes 1 Million Sensitive Records" by Lars Daniel
• "Samsung Bans ChatGPT Among Employees After Sensitive Code Leak" by Siladitya Ray
• "Face Reconstruction from Face Embeddings using Adapter to a Face Foundation Model" by Hatef Otroshi Shahreza, et al.
• "Researchers Demonstrate New Technique for Stealing AI Models" by Matt Shipman
• "How Cybercriminals Used AI To Mimic CEO's Voice To Steal £220,000" by Think Cloud
• "The rise of AI fake news is creating a 'misinformation superspreader'" by Pranshu Verma
• "A Comprehensive Guide to Access and Secrets Management: From Zero Trust to AI Integration — Innovations in Safeguarding Sensitive Information" by Boris Zaikin

This is an excerpt from DZone's 2025 Trend Report, Generative AI: The Democratization of Intelligent Systems

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