Large language models (LLMs) are the foundation of many generative AI systems, powering chatbots, coding copilots, autonomous agents, and other applications that interact directly with users and enterprise data. Unlike traditional software systems, LLMs do not operate on fixed logic paths or static inputs. They generate outputs probabilistically, drawing context from prompts, retrieved data, and tool calls—and often interact with external systems in real time. That flexibility is what makes them valuable, but it also introduces new security risks.
As organizations increasingly adopt LLMs, protecting these systems is becoming a core enterprise concern rather than a niche AI issue. Traditional application security focuses on protecting code and infrastructure with well-defined interfaces. LLM security must account for additional threat surfaces, including prompt injection, training data exposure, insecure model inputs and outputs, and unintended propagation of sensitive information.
But LLM behavior is shaped by data, context, and runtime interactions, so securing generative AI systems requires controls that extend beyond conventional software security practices. How do enterprise security teams activate the potential of LLMs without exposing the busines to new risks?
Why LLM Security Matters in Today’s Enterprise Environments
LLMs are increasingly embedded in enterprise applications that handle sensitive data, including internal documents, customer interactions, source code, and operational context. To make these systems useful, organizations often connect them directly to enterprise data stores and workflows. That design choice creates unique risks: LLMs do not clearly separate instructions, data, and outputs, and they generate responses dynamically rather than following fixed execution paths. As a result, sensitive information can be exposed or misused in ways traditional application security controls are not designed to catch.
Recent incidents illustrate how these risks translate into real-world impact. More capable models introduce heightened cybersecurity risk as they gain autonomy and access to tools and systems, increasing the potential for misuse and unintended data exposure. Practical vulnerabilities in deployed enterprise copilots show how these risks can materialize: a zero-click issue in Microsoft 365 Copilot (“EchoLeak,” CVE-2025-32711) demonstrated how a crafted email could potentially enable sensitive data exposure without user interaction.
Academic work has also shown that indirect prompt injection, where hidden instructions are embedded in content the model later retrieves or processes, can manipulate model behavior and create paths to unintended actions or disclosure—particularly for tool-using or web-browsing agents.
Key LLM security risks you need to know
LLMs introduce security risks that go beyond traditional application threats, driven by how models interpret input, retain context, and interact with data and tools. Table 1 outlines the most common LLM security risks that enterprises should understand when deploying generative AI systems.
Table 1. Common LLM security risks
Risk type | Description | Example scenario |
|---|---|---|
Prompt injection | Manipulating user input to change model or AI agent behavior | An attacker crafts a prompt that bypasses safety controls, causing the model to ignore content filters or system instructions. |
Indirect prompting | Embedding hidden instructions in content the model later processes | A document or web page includes concealed instructions that trick an LLM into outputting sensitive enterprise data when summarized. |
Data poisoning | Corrupting training data or fine-tuning data to bias outcomes | Malicious actors seed harmful or misleading content into datasets used to train or adapt an enterprise model. |
Memory poisoning | Injecting false data into an AI agent’s memory to influence future behavior | A bad actor manipulates an agent over time so it “remembers” altered bank routing information and repeats it later. |
Model theft | Reverse engineering or extracting model weights or behavior | An attacker probes an API repeatedly to reconstruct a proprietary fine-tuned model. |
Resource exhaustion | Overwhelming model or agent resources to cause denial-of-service | Automated requests flood an AI-powered customer support system, making it unavailable to real users. |
Unauthorized access | Gaining access to the model, underlying data, or agent privileges | An attacker exploits agent permissions to retrieve internal customer records or system data. |
Code execution | Using model output to trigger malicious code | Generated scripts or commands are executed automatically, leading to unsafe code execution in downstream systems. |
Security Best Practices for LLM Applications
Securing LLM applications requires a multilayered approach that addresses how models are accessed, how data flows into and out of them, and how their behavior is monitored over time. Rather than treating LLMs like conventional software components, organizations need security controls that reflect the dynamic, data-driven nature of generative AI systems. That includes:
Access controls: Apply role-based access, strong authentication, and user governance to limit who can interact with models and underlying data, and to strongly control the permissions AI agents themselves have, reducing the risk of unauthorized access.
Input filtering and sanitization: Inspect and sanitize prompts, retrieved content, and tool inputs to reduce exposure to prompt injection and indirect prompting attacks.
Output moderation: Scan model outputs for sensitive information, policy violations, or malicious content before results are returned to users or downstream systems.
Secure model hosting: Run models and supporting infrastructure in trusted, hardened environments with network segmentation and least privilege configurations.
Audit and monitoring: Maintain detailed logs of model interactions, tool calls, and data access to detect anomalous behavior and support investigations.
Model fine-tuning governance: Monitor fine-tuning datasets and processes to validate data quality, provenance, and security, limiting the impact of poisoned or inappropriate training data.
Risk monitoring: Continuously identify and quantify risky model or AI agent activity, flagging policy violations or abnormal patterns before they lead to data exposure or operational impact.
Mitigating Risks Through Training Data Management
Training data integrity directly impacts model safety. Compromised, sensitive, or poorly governed data can lead to biased outcomes, privacy violations, or backdoors that trigger harmful behavior. Without strong controls over training data and fine-tuning sources, organizations risk amplifying vulnerabilities already present in LLMs.
Here are some key ways to manage your training data to mitigate risk:
Keep sensitive or proprietary data out of training sets: Exposing internal or regulated information during training increases the chance that models will inadvertently memorize and later leak that data. For example, many AI companies have inadvertently exposed API keys, model access tokens, and internal training data when credentials appeared in public repositories, highlighting risks from insecure data management early in the development process.
Prevent data poisoning: Intentional or accidental corruption of training datasets can embed harmful behavior or backdoors into models. Adversaries only need to slip a small number of malicious documents into training data to significantly alter model behavior.
Classify and govern data sources: Use structured data discovery and classification to inventory sensitive information and prevent it from entering training pipelines. Classification helps teams understand what data exists, where it resides, and how it should be treated under security policies.
Anonymize and sanitize data before training: Apply anonymization techniques and remove direct identifiers from datasets to reduce privacy risks while maintaining useful patterns for model learning.
Monitor data quality and lineage: Track the provenance and transformation history of training data to catch issues such as duplicated, mislabeled, or unauthorized content before it’s used in fine-tuning or model adaptation.
Building Secure Foundations for Enterprise Generative AI
As enterprises expand their use of generative AI, LLM security becomes a prerequisite for scale. Models that interact with enterprise systems and sensitive data introduce new risk paths—from data leakage and misuse to agent-driven privilege abuse—that organizations need to address head-on.
Rubrik’s broader data security and posture management capabilities help organizations reduce this exposure by bringing visibility and control to the data that feeds LLM applications. Organizations must gain context to detect threats, enforce policies, and limit the blast radius when LLMs are integrated into production environments. This can be achieved by identifying sensitive data, tracking where it lives, and monitoring how it’s accessed.
Rubrik Agent Cloud extends this approach to AI agents themselves, providing centralized oversight into agent behavior, tool usage, and data access. This visibility helps teams identify risky agent activity, spot abnormal patterns, and govern how agents interact with enterprise systems, addressing one of the fastest-growing risk areas in LLM adoption.
As generative AI continues to evolve, organizations will need security strategies that evolve with it. Rubrik’s AI capabilities can help teams build LLM-powered systems that deliver value while maintaining strong data security, governance, and operational resilience.