Enterprise LLM Security: Risks, Frameworks, & Best Practices

Enterprise LLM security is a growing concern as organizations integrate language models into business-critical workflows. Unlike traditional software, LLMs take natural language as input and produce unpredictable, sometimes unsafe output. This creates new risks like prompt injection and hallucinations that most existing security tools aren’t built to catch.

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In this article, we’ll cover: 

• How LLMs introduce new risk classes
• Tackling prompt injection, data leakage, and shadow AI
• How to build governance for secure, observable LLM deployments

We’ll start with the risks LLMs introduce.

The unique risks of enterprise LLM deployments

LLMs create several categories of security risks that traditional application security doesn't address.

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Below are some of these risks:

• Data leakage through chatbots or summarization tools: LLMs can memorize and regurgitate sensitive information from training data, leak context between documents, or include confidential details in summaries.
• Prompt injection and chaining vulnerabilities: Attackers can manipulate LLM inputs to override system instructions or extract sensitive information. They use crafted prompts to override system instructions. In some cases, they chain multiple LLM calls to escalate privileges or bypass controls.
• Hallucinated outputs creating legal or reputational risks: LLMs often generate false information that appears accurate. The case of lawyers sanctioned for using ChatGPT-generated fake cases demonstrates this risk in practice.‍
• Over-permissive API integrations or system actions: Orgs often give LLMs excessive permissions to maximize functionality, which increases attack surfaces. A compromised model with database or API access can leak data or trigger unintended system actions.‍
• Shadow LLM usage by teams without IT governance: Business users may deploy unvetted LLM tools or services that bypass security reviews and expose organizational data to external providers. This leads to data sprawl across systems with varying security standards.‍
• Missing audit trails in experimental rollouts: Early LLM deployments often skip logging. Without visibility, teams can’t detect misuse, track data access, or prove compliance to regulators.

Where LLM security fails in enterprise environments

LLM security fails when enterprises deploy models without proper controls, grant excessive permissions, or lack governance frameworks

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The examples below highlight where security tends to slip:

Embed LLMs in business-critical workflows

A single compromised model can cause cascading failures when teams embed it in business-critical systems. If an LLM with database access is hijacked, it could corrupt or leak data across multiple dependent applications.

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Unlike traditional systems with defined rollback paths, compromised LLMs may be difficult to restore to a known good state, especially if you have modified their training data or model.

Grant AI agents too much operational access

It’s common for organizations to over-permission LLMs to maximize functionality. What starts as a narrow use case often expands, accumulating permissions without formal review.

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This gradual permission creep turns LLMs into over-privileged assets. In a worst-case scenario, attackers can pivot through these systems to reach broader infrastructure.

No approval process for prompt logic or model access

Many enterprise LLM deployments lack basic governance around how models are selected and how prompt logic evolves. Teams select models without reviewing security, and write prompts without testing or version control.

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As teams iterate, configuration drifts. These gaps introduce avoidable risks that compound over time.

Limited RBAC or perimeter control over LLM inputs

Traditional access controls struggle to map to LLM behavior. Problems include:

• Input validation gaps: Natural language inputs are hard to sanitize or constrain.
• Context-dependent access: Most RBAC systems lack the granularity to adjust permissions based on a prompt's context.
• Dynamic access patterns: LLM usage patterns may change based on the content being processed, making it difficult to apply consistent access controls.

External or open-source models with unclear security maturity

Teams that deploy third-party or open-source models without fully assessing their risk may face:

• Supply chain risks: Public models may contain hidden vulnerabilities or even malicious code.
• Vendor practices: External providers may not align with your security standards.
• Update challenges: Patches may lag or break your pipelines entirely.

6 best practices for securing LLMs

Securing LLMs requires layered defenses that combine tooling, policy, and user behavior. Below are six best practices to reduce risk:

1. Treat LLMs like APIs: Use Git-based workflows to maintain strict version control for models, prompts, and configurations. Create well-defined APIs with clear interfaces that validate inputs and limit request rates.
2. Sanitize or mask sensitive data in prompts: Build systems that automatically detect and redact sensitive information before prompts reach the model.
3. Use retrieval-augmented generation with vetted data sources: Connect LLMs to knowledge bases that you've pre-approved and regularly update to reduce hallucinations. Configure retrieval layers to access only data that aligns with company policies and validate all sources before you integrate them.
4. Monitor output for banned terms, hallucinations, or anomalies: Implement content filtering that catches sensitive terms, toxic language, or policy violations in real-time. Regularly audit LLM responses to detect unusual response patterns or content drift.
5. Apply RBAC to LLM usage and deployment tooling: Define clear roles and permissions for LLM users. Restrict who can build, deploy, or modify workflows, and require approvals for high-risk changes.
6. Train internal users on prompt safety and expected behavior: Conduct security awareness programs that cover prompt injection, misuse scenarios, and safe prompting practices.

Security frameworks and community standards to consider

Enterprise LLM deployments benefit from aligning with established AI-specific security frameworks.

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Consider using the following widely adopted frameworks:

OWASP top 10 for LLM applications

OWASP is a nonprofit that publishes widely used security guidelines, including the top 10 risks for LLM applications. The list helps developers and security teams understand where LLM behavior breaks traditional security assumptions.

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It includes threats like:

1. Prompt injection: Manipulating LLMs through crafted inputs
2. Insecure output handling: Failing to validate LLM outputs
3. Training data poisoning: Corrupting training data to influence model behavior
4. Model denial of service: Overwhelming LLMs with resource-intensive requests
5. Supply chain vulnerabilities: Risks from third-party components and models
6. Sensitive information disclosure: Unintended exposure of confidential data
7. Insecure plugin design: Vulnerabilities in LLM extensions and integrations
8. Excessive agency: Granting LLMs too much autonomy or permissions
9. Overreliance: Depending too heavily on LLM outputs without proper validation
10. Model theft: Unauthorized access to proprietary models

MITRE ATLAS

MITRE ATLAS maps AI threats into tactics, techniques, and procedures that help organizations understand attack vectors. You can use ATLAS to conduct AI red teaming exercises, develop simulation-based testing, and implement monitoring strategies for AI-specific threats.

Google Secure AI Framework (SAIF)

Google’s Secure AI Framework (SAIF) offers a set of best practices for securing AI systems. These best practices focus on threat modelling, input validation, and strong governance.

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It outlines six core practices:

1. Expand existing security foundations to include AI-specific systems.
2. Extend detection and response capabilities to cover AI behaviors and outputs.
3. Automate defenses to keep up with dynamic AI use cases.
4. Harmonize controls across infrastructure, model pipelines, and deployment layers.
5. Adapt security measures to model lifecycle stages and operational context.
6. Contextualize AI risk within broader enterprise risk management strategies.

NIST AI Risk Management Framework (AI RMF)

NIST’s AI RMF helps organizations assess and manage the risks of deploying AI systems at scale. It defines four core functions called Map, Measure, Manage, and Govern. These functions guide teams to identify risks, evaluate impact, apply controls, and ensure accountability. 

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Unlike OWASP and SAIF, which focus on technical threats and defenses, the AI RMF supports broader governance policies and responsible AI deployment.

How to build a governance layer around enterprise LLMs

To build a governance layer around enterprise LLMs, start with visibility. You need to know who is using the model, what prompts they’re sending, and the outputs the models are producing. 

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The practices below help establish that foundation:

Require peer review or manual approval for high-impact LLM outputs

Define clear criteria for when LLM outputs require human review, based on factors like financial impact, regulatory requirements, and potential for harm. Then, establish clear escalation paths for when reviewers identify issues or need additional expertise.

Audit all prompts, models, and application-level decisions

Log every prompt submitted to an LLM along with the user, timestamp, and application context, to enable full traceability. Track which model version was used for each request and record any downstream actions taken in response to LLM output.

Maintain a production vs. experimental separation

Separate infrastructure, access controls, and monitoring for production and experimental systems. Set clear promotion criteria that include security review, testing, and sign-off. Include rollback procedures to quickly revert production systems if needed.

Create a model and a prompt registry for enterprise use

Build centralized registries to manage LLM assets and reduce duplication. Maintain a catalog of approved models, including their intended use cases, security posture, and operational limits. Build a prompt library with pre-approved, tested prompts to encourage reuse and reduce risk.

Favor structured interfaces over raw prompts

Structured inputs allow better control and safer user interaction. Build form-based interfaces to enforce input validation and sanitization, limit injection risk, and create more consistent user experiences.

Enterprise use cases with real security tradeoffs

LLM security risks manifest in the real world in different scenarios.

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Here are a few:

Customer service AI exposes internal pricing data

Scenario: A retail company deploys an AI chatbot to handle customer inquiries. During peak shopping season, the bot begins revealing internal cost structures and competitor pricing strategies when customers ask about product availability.

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Root causes: The AI was trained on internal documents, including pricing spreadsheets. The system lacked output filtering for sensitive business information, and customer queries weren't properly scoped to public information only.

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Mitigation strategies: 

• Implement data classification before training
• Add real-time output filtering for financial data
• Create separate knowledge bases for customer-facing vs. internal use
• Establish query validation to prevent fishing expeditions.

CI/CD automation triggered by a hallucinated prompt

Scenario: A development team uses an LLM to generate CI/CD pipeline configurations based on natural language descriptions. The LLM hallucinates a configuration that includes malicious code, which is automatically deployed to production, compromising the system.

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Root causes: The pipeline lacked validation checks for LLM-generated configurations, and there were no monitoring systems in place to detect unusual or malicious deployment activity.

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Mitigation strategies:

• Implement automated validation and testing for all LLM-generated configurations
• Require human review for all production deployments
• Add monitoring and alerting for unusual deployment patterns
• Implement rollback procedures for quickly reversing problematic deployments

Legal/HR teams using unauthorized LLM tools

Scenario: Legal and HR teams begin using public LLM services to analyze contracts and employee data, bypassing IT security controls. This creates significant data exposure risks and potential compliance violations.

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Root causes: Teams adopted unsanctioned tools due to a lack of approved internal options, the organization lacked controls to block access to public AI services, and staff weren’t trained on the security risks of external LLM use.

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Mitigation strategies:

• Deploy network-level controls to block access to unauthorized LLM services
• Provide approved LLM tools that meet your org’s requirements
• Conduct security awareness training for all teams using AI tools
• Establish clear policies for AI tool approval and usage

Secure LLM workflows at scale with Superblocks

As LLMs move from isolated pilots to production workflows, enterprise teams need more than just model access. They need an AI infrastructure that aligns with their security, compliance, and governance policies.

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Superblocks gives teams full control over how they build, prompt, and deploy LLM-powered apps and automations.

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Our approach aligns with these best practices for LLM security, including:

• Context-aware AI app generation: Every app built with Clark abides by organizational standards for data security, permissions, and compliance, addressing the major LLM risks of ungoverned shadow AI app generation.
• Zero data retention by LLMs: Customer data is never used to train third-party LLMs, and LLM providers used by Superblocks enforce zero retention policies. 
• Ephemeral AI suggestions: All AI-generated suggestions are not stored on devices or servers, reducing the attack surface for data exposure.
• Encrypted data transfers: All data sent to and from LLMs is encrypted in transit using TLS 1.2 or greater.
• Role-based access control: Users only interact with data they’re authorized to access. LM workflows inherit organizational permissions.
• Vendor vetting and monitoring: All subprocessors and LLM vendors undergo privacy, security, and confidentiality evaluations before integration.
• Audit trails and retention: Superblocks maintains a limited 14-day history of user interactions (prompt, response, whether suggestions were accepted/rejected, etc.) solely for operational security and support. This is subject to our data retention policies.
• Compliance: Superblocks, including its AI features, is covered in the scope of Superblocks' SOC 2 Type 2 report.
• Configurable AI usage: You can configure which features use LLMs and what data is sent, or disable AI capabilities altogether.

Ready to build secure LLM-powered apps? Take a look at our Quickstart guide.

Frequently Asked Questions

How do you prevent LLM data leakage?

To reduce LLM data leakage, limit what data LLMs can access, sanitize prompts to remove sensitive information, and use providers that enforce zero-retention policies.

What is prompt injection, and how can it be mitigated?

Attackers use prompt injection to make an LLM execute unintended instructions. You can mitigate it by using structured inputs (like forms), validating prompt content, stripping user-controlled context before prompting, and filtering outputs for risky behavior. This governance approach is essential as organizations undergo IT transformation and increase AI adoption.

How do you govern shadow AI or unauthorized LLM usage?

You can govern shadow AI by setting network-level controls to block unsanctioned tools. Then, provide approved alternatives with the right permissions and logging. Pair this with clear AI usage policies and security training across business units.

Are LLMs compliant with SOC 2 or GDPR?

LLMs themselves aren't inherently compliant with SOC 2 and GDPR, but your implementation can be. Choose vendors that support your required compliance frameworks.

What is OWASP’s guidance on LLM security?

OWASP recommends treating LLMs like APIs, validating inputs/outputs, and enforcing access boundaries. It outlines critical risks like prompt injection, output handling flaws, and excessive system permissions.

How do I apply RBAC to AI workflows?

Map user roles to AI access at the app and prompt level. Use RBAC to control who can deploy models, modify prompts, or trigger high-impact actions.

What’s the difference between LLM observability and monitoring?

Monitoring tracks performance and uptime, while observability gives insight into what the LLM does. Observability tools record each prompt the model receives, track its responses, and log user interactions with the LLM.

Can Superblocks secure LLM workflows inside internal apps?

Yes. Superblocks secures LLM workflows by giving teams full control over how their internal apps use models. You can manage access, version prompts, log every request, and trigger human review before Clark takes action. The built-in audit logs and RBAC make it easier to align LLM usage with your org’s security policies.