What is AI Data Security?

The Rapid Spread of AI Technology

AI has seen a dramatic rise in both capability and adoption in recent years. While AI was used for analytics and spotting previously unnoticed patterns in large datasets, the release of ChatGPT in late 2022 and the introduction of Large Language Models (LLMs) brought generative AI to the forefront of mainstream business and consumer use.

However, AI use introduces new and unique security threats, many of which target sensitive data across different stages of LLM development and use. From misusing generative AI tools and unsafe agents to protecting AI infrastructure and maintaining visibility into AI traffic, data security in the age of AI technology presents many challenges.

As AI’s role in the enterprise grows, securing the data these systems use must be a priority. Organizations that fail to implement strong AI data security early will face significant risks down the road as they expand their use of this transformative technology.

Understanding AI Data

AI data refers to the huge amounts of information that artificial intelligence systems use at various stages to learn, make predictions, and improve their performance. The capabilities of an AI model are defined by the scale and variety of data it has access to across different stages of its development and use. This includes:

• Training: The first phase of AI development is training, where AI models “learn” from vast datasets.
• Testing: After training, AI models are tested on separate datasets. This step validates how well the model performs when applied to new, unseen data.
• Improvement: AI systems typically continue to improve over time as they are exposed to new data. Continuous learning involves providing the AI model with new data to refine the algorithms and improve their accuracy or efficiency.
• Run Time: Once deployed, AI systems operate in real time, analyzing data provided by the user or data retrieved by the model.
• Fine-Tuning: Another type of AI data that often contains sensitive business data is fine-tuning training data. A common practice is to take large-scale, proprietary AI models developed by the big players in the industry, then retrain or fine-tune them on internal business datasets to improve their performance for specific use cases.
• Agent Datasets: AI agents combine AI systems with external tools and datasets to complete more complex tasks without human intervention.

The Importance of AI Data Security

Given the importance and sensitivity of the data used throughout AI pipelines, it’s essential to implement robust data security practices at every stage. AI systems create a new attack surface for cybercriminals to exploit, enabling them to gain access to critical business data or compromise training data to impact LLM performance. Each stage presents distinct risks, and securing data across these phases ensures you can confidently use AI tools to innovate your operations while maintaining data security and compliance.

The data security challenges posed by using AI systems include:

• Level of Access: AI systems typically have access to vast amounts of sensitive information to learn, make predictions, and provide insights. This may include personal data, financial records, healthcare data, or even proprietary business information. The level of access AI systems have increases your exposure, particularly if security measures are not properly implemented. A small breach at any stage of the data pipeline can have cascading effects, leading to widespread data vulnerabilities.
• Oversharing of Data with GenAI Tools: The rise of generative AI tools improves employee productivity by automating various tasks. However, users may inadvertently expose proprietary or personal data when interacting with these tools. Data gathered using Check Point’s GenAI Protect platform, and published in the 2025 AI Security Report, show that 1 in 80 generative AI prompts have a high risk of exposing sensitive data to attackers, and 7.5% of prompts include sensitive or private details. Employees oversharing data with generative AI tools is a major data security and compliance concern.
• Shadow AI Usage: Another threat is shadow AI, the use of AI tools or models that are not officially sanctioned or monitored by an organization’s IT or security teams. A lot of AI use is informal, with employees adopting external AI tools for convenience or efficiency, without considering the potential security risks. Shadow AI can introduce significant vulnerabilities by bypassing traditional security measures and creating visibility gaps. Without IT teams monitoring and enforcing security controls, shadow AI usage can quickly lead to unauthorized access or data leakage.
• Visibility Challenges: Securing AI systems requires deep visibility into all AI and LLM traffic, such as user prompts, back-end data feeds, API calls, etc. Without full-stack monitoring and runtime analysis, you may miss data exfiltration attempts, oversharing of sensitive information, or instances of shadow AI. Comprehensive visibility allows security teams to detect suspicious activity, enforce policies, and trace sensitive data usage across the AI lifecycle, ensuring compliance and minimizing risk.
• Unsecured AI Agents: These autonomous AI systems are capable of performing complex tasks on behalf of users or applications. However, with greater autonomy and access to external tools and systems, AI agents present a critical security risk. Vulnerabilities in agent design or implementation may allow attackers to manipulate outputs or access confidential data. A notable example of the risks posed by unsecured AI Agents is the OpenClaw incident. This unsecured AI agent highlighted the gap between the implementation of autonomous systems and the implementation of security controls to keep them contained and safe.
• Protecting AI Infrastructure: Enterprise AI infrastructure, including high-performance GPU clusters running private LLMs, is a prime target for attackers due to its strategic value and access to sensitive data. Protecting these systems requires robust firewall protections, including access controls, network segmentation, and continuous monitoring to prevent unauthorized use. Securing the “AI factory” infrastructure ensures that both the models and the underlying data remain safe from internal and external threats.

Security professionals and business executives are aware of the new challenges and risks associated with LLMs as well as the importance of AI data security. IBM data shows that 96% of executives say adopting generative AI increases the likelihood of a security breach affecting their organization within the next 3 years. Yet only 24% of executives stated their generative AI initiatives had a cybersecurity component.

There is a disconnect between the very real risks posed by AI technology and the urgency to implement proper data security measures. This is evident in IBM’s data, which shows that 70% of executives say innovation takes precedence over security.

But AI data security is a vital part of modern cybersecurity strategy, enabling businesses to:

• Secure Their Sensitive Data: AI systems process valuable proprietary data, trade secrets, and personally identifiable information (PII) that must be kept confidential. A breach of this data not only harms individuals but also damages the organization’s reputation and legal standing.
• Minimize Business Disruption: Cyberattacks targeting AI data can disrupt business operations and cause significant downtime. If an organization’s AI systems are compromised, the impact on operations can be substantial. Strong AI data security practices ensure that, in the event of a breach, the business can recover and return to normal operations as quickly as possible. This helps maintain business continuity, minimize disruption, and protect the organization from long-term financial repercussions.
• Adhere to Data Privacy Regulations: Organizations are subject to an increasing number of data privacy regulations and laws, including GDPR (General Data Protection Regulation), HIPAA (Health Insurance Portability and Accountability Act), and various AI-specific compliance frameworks. These laws impose strict requirements on how data must be handled, stored, and protected. Failing to comply with these regulations not only exposes organizations to fines and penalties but can also damage their reputation with regulators and customers. AI systems must be designed with these compliance requirements in mind to avoid costly violations.

Build Customer Trust: A data breach, especially one involving customer information leading to a compliance violation, can severely damage an organization’s reputation and erode customer trust. Once trust is lost, it can be not easy to regain. Customers are less likely to work with companies that can’t protect their data. As organizations deploy AI in customer-facing applications, data security is essential to maintain long-term relationships and revenue streams.

Attacks Targeting AI Data

To understand and implement data security best practices, you first need to understand the potential cyberattacks targeting AI data. Below is a list of the most common attacks targeting AI data. These attacks aim to exploit vulnerabilities in AI data pipelines, from training to deployment, putting sensitive information and model integrity at risk. Some of the attacks described below are included in the Open Web Application Security Project (OWASP) top 10 risks for LLMs.

Data Poisoning

Data poisoning is an AI security threat where attackers deliberately manipulate AI training data to cause LLMs to behave incorrectly. For example, producing flawed outputs, predictions, or decisions. The performance of an AI model depends on the data it was trained on. By injecting inaccurate or harmful data into the training corpus, attackers can compromise the resulting AI model. This could include reduced performance, biased outputs, or creating backdoor vulnerabilities.

Backdoor data poisoning attacks manipulate the training data to insert a hidden trigger into the AI model. This trigger changes the model, causing it to behave in a certain way, such as producing unsafe or unethical outputs after activation. Backdoor attacks are especially challenging because they only reveal themselves when the trigger is activated.

Research by AI company Anthropic, alongside the UK AI Security Institute and the Alan Turing Institute, showed that poisoning just a small percentage of AI training data can create a backdoor vulnerability. The study showed that adding just 250 malicious documents to the total AI training dataset was sufficient to create a backdoor vulnerability. This result held regardless of the size of the training dataset or the model.

Prompt Injection

While data poisoning manipulates training data to affect model performance, prompt injection utilizes malicious inputs in an attempt to override the model’s guardrails. This could include jailbreaking its security protocols and forcing the model to reveal sensitive information.

Prompt injection is a runtime attack in which the attacker crafts specific prompts to break the model’s internal safety mechanisms. It exploits the model’s goal of satisfying the user’s query or instructions, finding ways to prioritize this over internal safety constraints.

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Data leakage occurs when sensitive information unintentionally leaks from the AI model or its outputs. In some cases, the model might expose details about the data it was trained on, even if that data is not directly accessible. For example, if an AI model is used for customer profiling, it might reveal private details about individuals through its predictions or inferences.

Model Inversion

In a model inversion attack, attackers reverse-engineer the AI model to reveal sensitive information about the data it was trained on. This is particularly dangerous in scenarios where the model has been trained on private or personal data, such as healthcare or financial records. The attacker could retrieve sensitive details about individuals, even if that data wasn’t directly accessible during the attack. Model inversion undermines the confidentiality of the model’s data and, depending on what is revealed, may result in compliance violations.

Adversarial Attacks

Adversarial attacks involve subtly altering input data to cause AI models to misclassify or make erroneous predictions, even when the changes are imperceptible to humans. For example, in image recognition systems, a small number of pixel changes can make an object unrecognizable to the AI, while humans still see it clearly. These attacks can have severe consequences, especially in safety-critical applications such as self-driving cars and security systems, where misclassifications could lead to accidents or breaches.

Model Extraction

Model extraction attacks occur when an adversary tries to extract or replicate the functionality of a machine learning model. The goal is to gain access to the intellectual property embedded in the model, without direct access to the underlying data or code. This could lead to the unauthorized use of proprietary AI models or the creation of a copycat model that competes with or undermines the original model’s value.

Best Practices for AI Data Security

To mitigate the risks associated with AI data security, organizations must adopt extensive, proactive measures across the entire AI lifecycle. Implementing AI data security best practices can help safeguard sensitive data, protect AI models from attacks, and ensure compliance with privacy and security regulations.

Build a Robust and Comprehensive AI Data Security Framework

A well-defined security framework is essential for managing AI data risks. While AI data security presents new dangers for enterprise and new opportunities for cybercriminals, many traditional data security practices still offer value in protecting AI systems. Therefore, review existing data security frameworks aligned with industry standards and regulations, and incorporate and adapt data protection, risk management, and incident response best practices for your AI ecosystem.

Establish a holistic security framework that embeds security into every aspect of AI development, from data collection and model training to deployment and maintenance. Define policies related to different security controls, including access controls, encryption, data storage and transfers, authentication mechanisms, threat detection, and recovery plans. Your framework should also include clear roles and responsibilities for different members of the security team, and be regularly reviewed to respond to changes in business operations or emerging AI data threats.

Strengthen Access Controls and Authentication

Implementing strong access controls is critical to ensuring that only authorized personnel or systems have access to sensitive AI data and AI models. Role-based access control (RBAC) and least privilege principles should be enforced to minimize access to AI data to only the necessary users. Additionally, use enhanced authentication processes, such as Multi-Factor Authentication (MFA), for system access to provide an additional layer of protection against unauthorized users. Regular audits of access logs can also help identify unusual access patterns or potential insider threats.

Encrypt and Anonymize Data to Maximize Protection

Data encryption is one of the most effective ways to protect sensitive information. With encryption protocols protecting data both in transit and at rest, even if unauthorized access occurs, the data remains unreadable.

Anonymization and pseudonymization techniques can further safeguard personal and sensitive data by removing or obscuring identifiers, such that information cannot be traced back to specific individuals. These techniques are especially important when working with large datasets or when using third-party data sources, where privacy concerns are heightened.

Validate Inputs to Prevent Malicious Activity

Input validation ensures that the data fed into AI systems is clean, accurate, and within expected parameters. This practice can prevent malicious actors from exploiting AI models through attacks that leverage incorrect or manipulated data to deceive them. Implementing robust data validation mechanisms, such as cross-checking data for consistency and using anomaly detection tools, helps ensure that the system can identify and reject potentially harmful inputs.

Monitor and Detect Threats in Real-Time

AI model activity should be continuously monitored for potential security threats, both during training and after deployment. Implementing threat detection systems allows organizations to identify suspicious activity or anomalies, indicative of an attack. These systems must run in real-time to quickly spot unusual input patterns, changes in model behavior, or significant deviations from typical AI usage. Additionally, regular audits help track any changes to AI data and models.

Conduct Adversarial Training to Enhance Model Resilience

Adversarial training intentionally exposes AI models to controlled adversarial examples during training to understand how they affect performance and assess the model’s resilience to different attack vectors. By simulating potential threats, you can better defend models against attacks like adversarial inputs or data poisoning. This proactive approach helps reduce the model’s vulnerability to external manipulation, ensuring its reliability even when faced with malicious inputs.

Stay Ahead of Regulatory Compliance and Ethical Standards

Organizations must stay informed about regulatory requirements and ethical considerations related to AI data security. Adhering to global data protection laws is mandatory for organizations that handle personal data. Additionally, ethical AI practices, such as ensuring fairness, transparency, and accountability in AI models, help mitigate risks related to bias and discrimination. Compliance with regulations protects the organization from legal penalties while also demonstrating your trustworthiness to current and future customers.

Train Employees to Safeguard AI Data Security

Since human error is often a key factor in data security breaches, employee training is a critical component of an AI data security strategy. Employees should be educated on best practices for data protection, how to identify potential threats, and the importance of safeguarding sensitive information. Regular security awareness training can help employees understand the risks associated with AI and empower them to take the necessary precautions, whether they are handling data, working with AI tools, or responding to potential incidents.

Build Robust AI Data Defenses with Check Point’s Suite of Generative AI Security Services

Check Point’s AI Security platform offers an extensive range of Generative AI security services designed to protect enterprise AI use without limiting the technology’s potential. AI data security features include:

•  AI Access Security that enforces data security policies with comprehensive visibility and the elimination of shadow AI.
• Runtime security protection from Check Point AI Agent Security that provides complete visibility, protection, and control of generative AI agents and applications at your organization, including real-time threat detection and guardrails to block inappropriate outputs and data leakage.
• Simulating real-world attacks targeting sensitive AI data using AI Red Teaming to test the resilience of your models while gathering data to improve security policies and incident response capabilities.

Schedule a demo today and learn more about Check Point AI Security and how to implement robust AI data security practices without impacting the user experience or limiting innovation.

Beyond these network security services that protect enterprise AI use, Check Point also secures AI infrastructure with theAI Factory Firewall platform, in partnership with NVIDIA. The platform provides a secure environment for developing and deploying AI models while protecting against the latest threats, including data poisoning, model theft, and exfiltration. Learn more by downloading Check Point’s Security Blueprint for AI Factories.