Generative Artificial Intelligence (GenAI) is rapidly changing how organizations develop software, analyze information, automate workflows, interact with customers, and make business decisions. Large Language Models (LLMs) are increasingly being integrated into enterprise applications, cloud platforms, customer service systems, development environments, and internal knowledge repositories.
But as organizations move from experimentation to production, an important question is emerging:
How do we secure AI systems that can interpret natural language, access enterprise data, interact with external systems, and increasingly take actions on behalf of users?
Traditional cybersecurity controls remain essential, but they are no longer sufficient by themselves. LLM-powered applications introduce a new attack surface involving prompts, training and retrieval data, model outputs, plugins, APIs, vector databases, AI agents, and the cloud infrastructure supporting them.
The OWASP GenAI Security Project’s 2025 Top 10 identifies major risks including prompt injection, sensitive information disclosure, supply-chain vulnerabilities, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation, and unbounded consumption.
Organizations therefore need to treat LLM security as part of their broader cloud security, identity, data protection, application security, governance, and operational resilience strategy.
1. Prompt Injection: When Instructions Become an Attack Vector
Prompt injection is one of the most significant security challenges facing LLM applications. Unlike traditional applications, LLMs interpret natural-language instructions. Attackers may intentionally construct prompts designed to manipulate the model into ignoring its intended instructions, revealing information, accessing functions improperly, or producing unauthorized outputs.
There are two major forms.
Direct prompt injection occurs when an attacker directly provides malicious instructions to the model.
Indirect prompt injection can occur when the model processes external information containing hidden or malicious instructions. That information could originate from a document, website, email, database, retrieved content, or another external source.
This becomes particularly dangerous when an LLM is connected to enterprise tools or has permission to perform actions.
How organizations can reduce the risk
Prompt injection cannot simply be solved with a stronger system prompt. Organizations should implement multiple layers of protection:
- Validate and filter user inputs.
- Separate trusted instructions from untrusted external content.
- Apply strict authorization controls outside the model.
- Restrict access to tools and APIs.
- Validate model outputs before downstream execution.
- Require human approval for sensitive actions.
- Continuously conduct adversarial testing and AI red-team exercises.
The fundamental principle is simple: never assume that an LLM will always follow the instructions you intended it to follow.
2. Sensitive Information Disclosure
Generative AI systems frequently interact with large volumes of enterprise information. That may include personally identifiable information (PII), financial information, healthcare records, intellectual property, source code, authentication credentials, legal documents, internal communications, and confidential business information.
If data controls are poorly designed, an LLM application could inadvertently expose sensitive information through generated responses or allow unauthorized users to retrieve information they should never have been able to access.
The problem can become more complex with Retrieval-Augmented Generation (RAG), where an LLM retrieves enterprise information from external knowledge repositories before generating an answer.
Protection requires data governance before AI governance
Organizations should:
- Classify sensitive information.
- Apply encryption in transit and at rest.
- Implement role-based or attribute-based access controls.
- Mask or tokenize sensitive data where appropriate.
- Prevent secrets and credentials from entering prompts.
- Establish retention policies for AI conversations.
- Monitor access to sensitive datasets.
- Apply Data Loss Prevention (DLP) controls.
- Ensure retrieval systems respect the permissions of the requesting user.
An AI application should never become an alternative path around an organization’s existing authorization model.
3. AI Supply-Chain Risk
Modern LLM applications are rarely built entirely by one organization.
They may depend on:
- Foundation models
- Open-source models
- Third-party APIs
- AI libraries and frameworks
- Plugins and extensions
- Vector databases
- Public datasets
- Pre-trained models
- Cloud infrastructure
- Containers and software packages
Every dependency creates another potential security boundary. A compromised model, malicious package, vulnerable plugin, poisoned dataset, or improperly secured API could affect the entire AI application.
Organizations should therefore extend existing software supply-chain security practices to AI.
This includes maintaining inventories of models and dependencies, verifying trusted sources, scanning software components, reviewing third-party security practices, monitoring vulnerabilities, controlling model repositories, and maintaining traceability between models, datasets, applications, and deployment environments. AI security starts long before the user enters the first prompt.
4. Data and Model Poisoning
Generative AI systems depend heavily on data. If an attacker can manipulate training, fine-tuning, or retrieval data, the behavior of an AI system can potentially be influenced without directly attacking the application itself.
A poisoned dataset could introduce misleading information, hidden behaviors, biases, or backdoors.
RAG architectures introduce another consideration: if attackers can modify documents contained within a trusted knowledge repository, they may influence what the LLM retrieves and ultimately communicates to users.
Organizations should establish strong provenance and integrity controls for AI data.
Important measures include:
- Tracking where datasets originate.
- Restricting who can modify AI knowledge repositories.
- Maintaining version histories.
- Validating new data sources.
- Monitoring unusual changes.
- Testing model behavior after updates.
- Separating trusted and untrusted data sources.
- Applying integrity checks to critical datasets.
For enterprise AI, data integrity is a security control.
5. Improper Output Handling
An LLM’s response should be treated as untrusted input, especially when another application will consume it.
This principle becomes critical when LLM output can generate code, construct database queries, invoke APIs, create web content, or trigger automated processes.
Without proper validation, malicious or unexpected model output could potentially contribute to traditional application vulnerabilities. The solution is to place deterministic security controls between the model and downstream systems.
Organizations should sanitize and validate generated content, enforce schemas, restrict executable outputs, apply conventional application-security controls, and never allow generated instructions to bypass established authorization boundaries.
The LLM should recommend or request an action. The security architecture should determine whether that action is permitted.
6. Excessive Agency and the Rise of AI Agents
One of the most important changes in generative AI is the transition from AI that answers questions to AI that performs actions.
AI agents may be able to:
- Send emails.
- Modify records.
- Access cloud resources.
- Execute workflows.
- Query databases.
- Create or modify files.
- Interact with business applications.
- Invoke APIs.
- Communicate with other agents.
This dramatically increases the potential impact of a compromised or incorrectly behaving AI system. Excessive agency generally occurs when an AI system has more functionality, permissions, or autonomy than it actually needs.
Organizations should apply the principle of least privilege to AI agents exactly as they would to users, applications, and service accounts.
Give agents only the tools they need. Give those tools only the permissions they require. Require approval for high-impact operations. Establish transaction limits. Maintain detailed audit logs. Provide emergency revocation mechanisms.
The goal is not to eliminate AI autonomy it is to make autonomy bounded, observable, and accountable.
7. System Prompt Leakage
System prompts often contain internal instructions that define how an LLM application should behave. Organizations sometimes place confidential business logic, security rules, internal architecture information, or other sensitive details inside these prompts.
That is risky. Developers should operate under the assumption that prompt content may eventually be exposed.
Passwords, API keys, credentials, and security secrets should never be treated as safe simply because they are placed in a system prompt. Authorization and security enforcement belong in deterministic application and infrastructure controls, not in hidden natural-language instructions.
8. Vector and Embedding Security
RAG has become one of the most important enterprise AI architectures because it allows an LLM to answer questions using organizational knowledge. However, vector databases and embeddings create their own security considerations.
Organizations must consider:
- Who can add documents?
- Who can modify them?
- Who can retrieve them?
- Are permissions preserved during retrieval?
- Can information from one customer or department appear in another user’s context?
- Can malicious documents manipulate the LLM?
- Are embeddings and vector stores properly isolated?
Access controls must follow the data through the entire RAG pipeline. Security should exist not only at the chatbot interface but also at the document, retrieval, embedding, model, and infrastructure layers.
9. Misinformation and Overreliance
LLMs can generate highly convincing responses that are nevertheless inaccurate. In low-risk situations, an incorrect response may simply inconvenience a user. In healthcare, finance, cybersecurity, government services, engineering, or legal workflows, however, incorrect AI-generated information could have significant consequences.
Organizations should establish clear boundaries around where AI-generated information can be trusted.
High-impact decisions should include appropriate human oversight, authoritative data sources, validation mechanisms, confidence indicators where appropriate, and traceability back to source material. The objective is not simply to make AI sound confident. It is to make AI outputs verifiable and appropriately governed.
10. Unbounded Consumption and AI Resource Abuse
LLM systems consume computational resources, and sophisticated models can be expensive to operate. Attackers or poorly designed applications may generate unusually large prompts, repeated requests, complex workflows, or uncontrolled agent loops that consume significant resources.
The result can include service degradation, denial of service, unexpected cloud spending, or exhaustion of API quotas.
Organizations should implement:
- Rate limiting.
- User and application quotas.
- Token limits.
- Request-size restrictions.
- API throttling.
- Cost monitoring.
- Budget alerts.
- Timeout mechanisms.
- Agent iteration limits.
- Anomaly detection.
Security and FinOps increasingly intersect in AI environments because uncontrolled resource consumption is simultaneously an availability risk and a financial risk.
Building Defense in Depth for Generative AI
There is no single security product capable of eliminating every LLM risk. The strongest strategy is defense in depth.
Organizations should think about AI security across multiple interconnected layers:
- Identity – Who is requesting access?
- Data – What information can they retrieve?
- Model – What can the model process and generate?
- Application – How are inputs and outputs validated?
- Agent – What actions can AI perform?
- Cloud – Where is the system running and how is it configured?
- Network – What systems can communicate?
- Monitoring – Can suspicious activity be detected?
- Governance – Who owns the risk and how is compliance demonstrated?
NIST’s Generative AI Profile reinforces this lifecycle-oriented approach by encouraging organizations to identify, evaluate, measure, and manage GenAI risks within their broader AI risk-management programs.
Kosmic Eye: Extending Visibility and Governance Around AI Infrastructure
Securing an LLM does not end at the model. Enterprise GenAI applications ultimately operate inside broader technology environments containing cloud infrastructure, identities, permissions, APIs, storage systems, networks, security controls, and operational processes.
This is where Kosmic Eye can complement an organization’s broader AI security strategy. Kosmic Eye provides organizations with visibility across cloud environments and helps security and technology teams identify risks associated with cloud posture, identities, permissions, configuration drift, and cross-cloud security relationships.
Its capabilities can support the infrastructure and governance layers surrounding GenAI workloads through areas such as:
- Multi-cloud security posture visibility: Organizations increasingly operate AI workloads across multiple cloud platforms. Centralized visibility can help teams understand the environments supporting AI applications rather than evaluating each cloud in isolation.
- Identity and permission visibility: As AI agents receive access to APIs, data stores, and cloud services, understanding excessive or risky permissions becomes increasingly important.
- Toxic permission detection: Individual permissions may appear acceptable independently but become dangerous when combined. Identifying risky permission relationships can help organizations reduce the blast radius surrounding AI-enabled services.
- Configuration and drift monitoring: AI infrastructure changes rapidly. Continuous monitoring can help identify when environments deviate from approved security configurations.
- Unified security relationships: Understanding relationships among identities, cloud resources, permissions, workloads, and security configurations provides valuable context when investigating risk.
- SIEM/SOAR and security operations integration: Connecting cloud security findings with security operations workflows can help organizations move from visibility to investigation and response.
For organizations adopting GenAI, this broader perspective is critical. An LLM may be secure at the application layer while the storage account containing its enterprise knowledge is misconfigured. An AI agent may have strong prompt protections but excessive cloud permissions. A RAG application may validate its outputs while its underlying data repository is exposed.
AI security therefore requires both AI-specific controls and strong security of the infrastructure surrounding the AI system.
Kosmic Eye can form part of that broader defense-in-depth architecture by helping organizations continuously understand and govern the cloud environments in which modern AI workloads operate.
A Practical Enterprise LLM Security Strategy
Organizations do not need to wait for AI security standards to stop evolving before taking action. A practical program can begin with several foundational steps.
- Inventory AI usage: Organizations cannot secure AI systems they do not know exist.
- Classify AI systems by risk: An internal writing assistant should not necessarily receive the same controls as an AI agent capable of modifying production systems.
- Apply least privilege everywhere: To users, service accounts, agents, plugins, APIs, data repositories, and cloud resources.
- Fourth, protect the data lifecycle. Understand what enters prompts, what is retrieved, where conversations are stored, and who can access AI-generated information.
- Validate inputs and outputs: Treat prompts, external documents, retrieved information, and generated responses as potentially untrusted.
- Monitor continuously: AI applications should produce meaningful security telemetry and integrate into existing security operations.
- Test adversarially: Red teaming, prompt-injection testing, permission testing, abuse-case analysis, and agent simulations should become part of the AI security lifecycle.
- Establish governance: Security, cloud, application, data, privacy, compliance, legal, and business teams should understand who owns AI risk and how decisions are documented.
Securing the Future of Enterprise AI
Generative AI is rapidly becoming part of enterprise technology architecture rather than a standalone experiment. That transition changes the security conversation.
Organizations must protect not only the model but the entire AI ecosystem: prompts, identities, datasets, embeddings, APIs, agents, applications, cloud infrastructure, third-party dependencies, and human decision-making processes.
Frameworks such as the OWASP Top 10 for LLM Applications, NIST AI Risk Management Framework, and MITRE ATLAS provide valuable foundations for understanding this evolving threat landscape.
But frameworks are most effective when translated into operational controls. That means implementing least privilege, securing data, continuously monitoring cloud posture, validating AI interactions, testing adversarial scenarios, governing autonomous actions, and maintaining visibility across increasingly complex technology environments.
Solutions such as Kosmic Eye can complement AI-specific security controls by strengthening visibility, posture management, permissions governance, configuration monitoring, and security operations across the cloud infrastructure supporting GenAI systems.
The organizations that succeed with generative AI will not simply be those that deploy the most powerful models. They will be the organizations that can deploy AI securely, responsibly, and at scale without losing visibility or control.
Frequently Asked Questions
1. What is LLM security?
LLM security protects generative AI systems, their data, applications, identities, APIs, agents, and supporting infrastructure from misuse, attacks, unauthorized access, and data exposure.
2. What are the biggest security risks for LLMs?
Key risks include prompt injection, sensitive information disclosure, data poisoning, excessive agency, insecure output handling, supply-chain vulnerabilities, and uncontrolled resource consumption.
3. Can prompt injection be completely prevented?
No single control can eliminate prompt injection. Organizations should use defense in depth, including input validation, least-privilege access, output validation, tool restrictions, monitoring, and human approval for sensitive actions.
4. Why is cloud security important for Generative AI?
AI applications depend on cloud infrastructure, identities, storage, APIs, and data. Even a well-secured model can create risk if the infrastructure around it is misconfigured or excessively permissive.
5. How can Kosmic Eye support Generative AI security?
Kosmic Eye can complement AI-specific security controls by providing visibility into multi-cloud security posture, identities and permissions, toxic permission combinations, configuration drift, and security relationships across the cloud environments supporting GenAI workloads.