Securing Data in the GenAI Era: How to Protect Innovations and Proprietary Information
The rapid rise of Generative AI presents major challenges for traditional Data Loss Prevention (DLP) programs. Security teams have an entirely new attack surface to protect, as businesses share sensitive information with AI models without considering data security. Check Point’s 2025 AI Security Report found that 7.5% of all prompts contain sensitive or private information, and 1 in 80 prompts expose sensitive data to potential attackers.
While there are significant AI adoption risks, these tools facilitate innovation across a wide range of enterprise use cases, and the more business data you share with them, the better they perform. So how do you protect the value GenAI brings to your business while also protecting sensitive data and proprietary information? Securing data in the GenAI era requires a fundamentally new approach, one that addresses emerging AI DLP risks.
Schedule an NG Firewall Demo Download the AI security report
Innovating and Sharing Proprietary Information with Generative AI Tools
Enterprises are adopting AI and innovating their operations in many ways. In the GenAI era, securing data and protecting innovation opportunities requires considering how different use cases share sensitive information with AI models and grant various levels of access. Understanding these differences is critical to managing enterprise AI adoption risks and tailoring DLP solutions to proactively protect against potential data breaches.
Enterprise AI use can typically be divided into four main categories, each sharing data in different ways and offering higher levels of access, but also potentially unlocking greater innovations.
#1. General AI Model Use
Employees use general-purpose AI systems through chat interfaces or embedded SaaS features. General AI use among employees can produce significant productivity gains, enabling them to quickly create content, summarize material for faster learning, and brainstorm or experiment with different ideas.
The AI model is not trained on company data and is not connected to internal enterprise systems or data stores. Instead, users provide context manually through prompts in an ad hoc manner. Typically, there is also no persistent storage of this data.
However, it is easy for employees to include proprietary information in their prompts, copying confidential data without considering security best practices. It can also be difficult for traditional DLP solutions to track sensitive information and enforce security controls in unstructured prompts and model outputs. Additionally, the model typically transforms the information provided, potentially leading traditional pattern-matching techniques to miss outputs that contain sensitive data.
Finally, while organizations can promote approved AI models, ones with stronger data handling practices, employees often revert to their favorite tools. This leads to shadow IT and reduced visibility, as employees may expose sensitive data without security teams becoming aware.
#2. Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation (RAG) enhances AI outputs by dynamically retrieving relevant information from approved enterprise data sources at query time. This enables more accurate AI responses tailored to your organization and reduces the need to manually copy data into prompts to provide context.
Instead of training the model on company data, RAG injects retrieved content into the prompt as contextual grounding for each response. This means the model has access to selected documentation, leading to greater DLP risks. For example, giving the AI model overly broad access, misconfiguring permissions, or sending retrieved content to the model provider without proper security controls in place.
#3. Fine-Tuning Models on Company Data
To get even more out of AI models, many organizations customize foundational models by training or fine-tuning them on internal company data. This allows the model to better understand company-specific terminology, processes, products, and domain knowledge. Examples include tailored AI assistants and chatbots that accurately perform niche use cases specific to your company or industry.
However, to access the innovation offered by fine-tuned AI models, they need access to high-value proprietary information. This data is stored and reused across model iterations, fundamentally changing how it understands inputs and generates outputs as it becomes part of the training corpus.
This creates significant new risks, including exposing high-value business data, tracking which data was used to train which models, and ensuring regulatory compliance, such as data residency and customer data anonymity.
Model data retention could lead to accidental data leaks or deliberate exposure through prompt injection attacks and other unauthorized actions. Fine-tuning AI models on company data significantly raises the stakes for data protection.
#4. AI Agents with Access to Enterprise Data and Systems
A step further than training foundational models on your data is giving AI systems access to internal systems. AI agents combine models with access to enterprise data, APIs, and external tools, enabling them to act autonomously across multi-step workflows. At the forefront of enterprise AI use, agents have the ability provide end-to-end automation, make decisions without human intervention, and coordinate entirely AI-driven workflows.
While this offers exciting opportunities across the business world, it also requires real-time access to sensitive business data and the ability trigger actions. Agents represent AI becoming an active participant in your organization, not just an assistant. This makes governance and real-time controls essential.
With access to external systems and increased autonomy, the potential impact of agent attacks or hacked AI agents increases dramatically. Overprivileged access, prompt injection, unintended agent actions, and system misuse are critical concerns that traditional DLP solutions cannot address.
The DLP Capabilities Needed to Secure GenAI Tools
To maintain the integrity of sensitive business data and proprietary information while enabling GenAI innovation, organizations need DLP solutions that account for the unique enterprise risks of AI adoption. Typical DLP capabilities, including data classification, tracking, and the enforcement of security controls, are complicated by GenAI tools. Next-generation DLP controls for securing data in the GenAI era include:
Data Detection Through Semantic Understanding
Traditional detection techniques for identifying and monitoring proprietary information typically rely on static pattern-matching methodologies. DLP solutions could develop a structured database of sensitive business information, such as financial records, Personally Identifiable Information (PII), IP, or login credentials. Then monitor network traffic to identify information that matches the database and enforce security policies.
However, GenAI models transform the data they are given when generating their outputs. This means outputs could contain sensitive information, but in a new form such that it no longer triggers DLP detection techniques. For example, the original pattern changes because the model summarized the data provided, but the output still contains proprietary information you need to track and protect.
To solve this issue, next-generation DLP solutions need semantic understanding of sensitive data to track its movement, even if transformed by GenAI. Understanding sensitive data and proprietary information at the language level, rather than relying on pattern matching, enables you to detect and monitor them in the GenAI era.
Comprehensive Visibility of GenAI Tools
You can’t protect what you can’t see. Comprehensive visibility into all GenAI use across the organization is the first step in monitoring what data is being shared, how model vendors handle that data, and the protections applied as it leaves the organization. This requires monitoring network traffic to identify the tools in use, their associated risks, and analyzing prompts and model outputs in real time to detect sensitive information and block risky AI interactions.
Additionally, you should continuously audit GenAI tools and their security practices, and keep up to date with any updates to their services. Generative AI is an exciting field, with new tools and services being released regularly. Effective DLP programs track changes to how these tools handle your data or integrate with your systems, then develop policies accordingly to keep sensitive information safe.
Context-Aware, Risk-Based Policy Enforcement
Modern DLP must move beyond static “block or allow” decisions and instead apply controls based on context and risk. AI-driven interactions vary widely depending on the user, the data being shared, the tool in use, and the intended outcome. A developer pasting code into an approved internal copilot presents a different risk than an employee sharing financial forecasts with a public AI chatbot.
Context-aware DLP evaluates factors such as data sensitivity, user role, and AI tool trust level to determine the appropriate response. This enables smart security enforcement and a range of potential actions, including warnings, redaction, anonymization, or blocking. Context-aware, risk-based policy enforcement reduces accidental data loss while allowing safe AI-driven innovation.
Protection of AI-Specific Assets
In the GenAI era, prompts, embeddings, fine-tuning datasets, and model outputs can contain proprietary knowledge and sensitive business data. Modern DLP must treat these AI-specific assets accordingly, applying classification, monitoring, and enforcement.
This includes protecting prompts and retrieved context from exposure, securing fine-tuning datasets from misuse, and monitoring outputs for unintended data disclosure. Additionally, next-generation DLP must monitor how any models trained on sensitive business data are utilized. Checking for suspicious activity indicative of AI-native threats like prompt injection, model inversion, and data extraction attacks, to ensure that adversaries cannot exploit models to recover confidential enterprise information.
Governance and Least-Privilege Controls for AI Access
As enterprises move toward fine-tuned models and autonomous AI agents, access governance becomes an increasingly important DLP requirement. Modern DLP must integrate with Identity Access Management (IAM) to enforce least-privilege principles for both humans and AI systems.
This includes limiting which datasets can be used for training, constraining the scope of RAG pipelines, and tightly controlling the tools and systems AI agents can access. By continuously auditing permissions and detecting over-privileged access, organizations reduce DLP risks without slowing down innovation.
Keep your Proprietary Data Safe and Maintain GenAI Innovation with Check Point
Generative AI is redefining data loss prevention and how sensitive information can be exposed. To protect innovation and proprietary information, you must rethink data protection for AI-driven workflows. Thankfully, Check Point is developing a suite of next-generation technologies for the GenAI era. This includes:
-
- GenAI Protect: Comprehensive visibility into the generative AI tools with accompanying risk assessments and the ability to enforce granular data protection policies.
- GenAI Application Protection: Real-time GenAI security to identify suspicious activity, block unsafe model use, and maintain compliance.
- GenAI Application Risk Scanner: Red teaming for GenAI use to assess security risks and minimize/mitigate model vulnerabilities.
Check Point GenAI security solutions work alongside the company’s Data Loss Prevention platform. With over 700 predefined data types and real-time security controls to identify and protect sensitive information, Check Point is always tracking proprietary business data to ensure it doesn’t fall into the wrong hands.
See our solutions in action for yourself by scheduling a demonstration of Next Generation Firewall or GenAI Protect today.
