TL;DR
Large language models, commonly referred to as LLMs, have rapidly become foundational components of enterprise technology stacks. From chat assistants and copilots to embedded AI features in SaaS platforms, LLMs are now involved in how employees search, analyze, generate, and act on information.
As adoption accelerates, so does the need for LLM security.
LLM security focuses on protecting data, identities, and workflows when organizations use large language models in production environments. Unlike traditional applications, LLMs interact with data dynamically, generate new outputs, and often integrate across multiple systems. This creates unique security, governance, and compliance challenges that traditional controls were not designed to address.
This guide provides a practical and vendor neutral overview of LLM security, including how LLMs are used, where risks emerge, and what security teams should focus on to govern enterprise AI safely.
What is LLM Security?
LLM security refers to the technical controls, policies, and governance practices used to manage risk when deploying and operating large language models.This includes securing:
- Data submitted to LLM prompts
- Model outputs that may expose sensitive information
- Integrations between LLMs and enterprise systems
- User and service account access to AI capabilities
- APIs, tokens, and non human identities used by LLM powered applications
- Compliance and regulatory obligations tied to AI usage
LLM security is not limited to a single tool or vendor. It applies across cloud-hosted LLM services, embedded AI features in SaaS platforms, and custom applications built on LLM APIs.
How Enterprises Use Large Language Models
Enterprises use LLMs in a wide range of scenarios, including:
- Internal productivity assistants
- Document summarization and analysis
- Code generation and review
- Customer support automation
- Knowledge base search and synthesis
- AI driven workflows embedded into SaaS tools
In many cases, LLMs are connected directly to internal data sources, SaaS applications, or business systems. This makes LLMs powerful, but also increases the potential impact of misconfiguration or misuse.
Why LLM Security is Different from Traditional Application Security
LLMs introduce security challenges that differ from traditional software for several reasons.
Dynamic Data Interaction: LLMs process unstructured input in real time, often using sensitive data provided by users or pulled from connected systems.
Amplification of Existing Access: LLMs do not typically create new permissions. Instead, they amplify whatever access already exists by summarizing, correlating, and surfacing data more efficiently.
New Attack and Misuse Patterns: LLMs can be abused through prompt manipulation, data extraction attempts, or social engineering scenarios that bypass traditional controls.
Rapid and Decentralized Adoption: LLMs are often adopted directly by business teams, leading to shadow AI usage outside of formal IT or security oversight.
Common LLM Security Risks
Sensitive Data Exposure
Users may submit confidential or regulated data into LLM prompts. Outputs may inadvertently reveal sensitive information through summaries or generated responses.
Shadow AI and Unapproved Tools
Employees frequently adopt AI tools without approval, creating blind spots where LLM usage is invisible to security teams.
Overly Broad Access
LLM access may not be tied to role based controls, allowing users or service accounts to retain AI capabilities longer than necessary.
API Key and Token Sprawl
LLM powered applications often rely on API keys that can be long lived, over scoped, and poorly tracked.
Integration and Plugin Risk
LLMs integrated with SaaS platforms, databases, or internal tools can introduce indirect exposure paths if connections are not governed.
Compliance and Regulatory Risk
Improper handling of personal, financial, or healthcare data through LLMs can violate regulatory requirements such as GDPR, SOC 2, ISO 27001, HIPAA, or industry specific rules.
Key Components of an LLM Security Program
Data Governance: Organizations must define what data can and cannot be used with LLMs and enforce classification, handling, and retention policies.
Identity and Access Control: LLM access should be limited to approved users and service accounts, with strong authentication and lifecycle management.
Integration Governance: Connections between LLMs and SaaS applications or data sources should be inventoried, reviewed, and governed continuously.
Monitoring and Visibility: Security teams need visibility into where LLMs are used, how they interact with data, and where risk accumulates over time.
AI Usage Policies: Clear policies help align employees on acceptable AI use and reduce accidental data exposure.
Built-In Google Controls That Support Gemini Security
Google provides native capabilities that support Gemini governance, including:
- Identity and access management through Google Workspace
- Data classification and DLP controls
- Audit logs for Workspace activity
- Admin controls for Gemini availability and scope
- Context-aware access policies
These controls are necessary, but they do not automatically resolve oversharing, excessive access, or unmanaged integrations.
LLM Security Best Practices
1. Treat LLMs as Part of Your SaaS Ecosystem
LLMs should be governed with the same rigor as other SaaS applications, not treated as standalone tools.
2. Reduce Oversharing Before Expanding AI Use
Clean up permissions and data exposure in connected systems before enabling AI driven access.
3. Control Access to LLM Capabilities
Limit who can use LLMs and under what conditions. Remove access when roles change or users leave.
4. Govern API Usage and Non Human Identities
Track API keys, rotate credentials, and remove unused integrations regularly.
5. Address Shadow AI Proactively
Discover unapproved AI tools and bring them under centralized governance rather than blocking innovation outright.
6. Align LLM Use With Compliance Requirements
Ensure AI usage aligns with legal, regulatory, and contractual obligations and is documented for audits.
The Future of LLM Security
As LLMs become embedded across SaaS platforms and business workflows, LLM security will increasingly overlap with SaaS security, identity security, and AI governance.
Security teams will need to shift from point-in-time reviews to continuous oversight, focusing on how access, data, and integrations evolve over time. Organizations that treat LLM security as a foundational capability rather than a one-off project will be best positioned to adopt AI safely at scale.
Final Thoughts
Large language models are changing how organizations work, but they also drastically expand the enterprise risk landscape. LLM security is not about blocking AI. It is about understanding how AI interacts with data, identities, and systems, and governing those interactions intentionally.
If you are evaluating how to manage LLM security across your SaaS and AI environments, Valence can help. Valence provides security teams with visibility into SaaS and AI access, helps identify data exposure and integration risk, and supports a variety of remediation workflows across the enterprise. Book a demo to see how Valence helps you find and fix SaaS and AI risks.
Frequently Asked Questions
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What is LLM security in an enterprise environment?
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Why do large language models introduce new security risks?
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How is LLM security different from traditional application security?
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Can LLMs expose sensitive or regulated data?
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What role do APIs and non-human identities play in LLM security?
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How can organizations secure LLMs without slowing adoption?
