There are several reasons why the Best LLM Security Platforms are relevant and important. Organizations are rapidly implementing large language models (LLMs), and with that, the risks associated with LLM internals such as prompt injection attacks and LLM jailbreaks and data leakage, have also rapidly increased.
LLM security platforms provide customers with the means to manage risks related to protection, compliance and integrity of AI. These platforms provide customers with a wide range of controls to manage LLM risks at run-time.
In addition, these platforms provide customers with integration, Red teaming and other controls related to LLMs. This document provides an overview of the available platforms to integrate secure LLMs.
What Is an LLM Security Platform?
LLM Security Platforms help protect enterprise LLM deployments. They provide prompt injection, jailbreak, data leakage, and model inversion attack protection, and implement runtime policies by scanning LLM inputs/outputs.
These platforms integrate with popular LLMs (e.g., OpenAI, Anthropic, Gemini, LLaMA), and offer various deployment options (e.g., API, SDK, cloud, gateways). Other features include, but are not limited to, red teaming, monitoring, and alerting; and integration with RBAC.
The platforms are designed to protect enterprise AI applications. Some example use cases include integrating AI in RAG (red, amber, green) workflows, and utilizing AI in SOC (security operations centers) and other similar control centers. The platforms help enterprises deploy AI in a secured and trusted manner.
How to Choose an LLM Security Platform?
Threat Coverage: The platform should defend against prompts injections, jailbreaks and other threats in the production system and should prevent data leaks and adversarial misuse.
RAG Security: The platform should prevent malicious prompts in retrieval-based generation and should ensure safe integration of external knowledge for retrieval.
Agent Security: The platform should provide security for AI agents and tools and should define and enforce safe usage policies.
Integrations: The platform should integrate with commercial LLMs.
Deployment: The platform should provide various deployment options e.g., cloud, self-hosted, API and SDKs.
Enterprise Controls: The platform should provide enterprise controls and features like RBAC and SSO, and should be audited and compliant to ISO/IEC 27001 and/or SOC/C2/L3, and FedRAMP.
Best Fit Use Case: The platform should be used in cases where other platforms are not appropriate, e.g., security of RAG generation and SOC teams
Key Points
| Platform | Key Point |
|---|---|
| Booz Allen Hamilton | Best for regulated enterprises needing scoped LLM risk assessment, red teaming, and control roadmaps. |
| NCC Group | Strong in executed LLM security assurance with evidence-based remediation for real integrations. |
| Deloitte | Enterprise risk and control mapping, turning red team findings into governance-ready artifacts. |
| PromptGuardrails | Comprehensive platform with real-time prompt injection detection, automated red team testing, and compliance support. |
| Lakera Guard | Fast prompt injection detection with low-latency scanning and integration with major LLM providers. |
| Protect AI Guardian | End-to-end model security testing, monitoring, analytics, and compliance reporting. |
| Robust Intelligence | Automated adversarial red team testing with custom attack generation frameworks. |
| Nightvision | Real-time monitoring and anomaly detection for production LLMs with performance analytics. |
| Noma Security | AI asset mapping, risk identification, and governance support for enterprise AI environments. |
| Astrix Security | Manages non-human identities and access relationships used by AI agents and applications. |
1. Booz Allen Hamilton
LLM-based security frameworks are sector-agnostic and can be implemented by entities across defense, finance and healthcare. The solutions provide risk assessment frameworks and governance controls. Other solutions include red teaming and adversarial risk assessment.
The company works with RAG security to assess the safety of LLMs in retrieval-based pipelines. The company helps in ensuring the safety of various LLM-based agents and tools. Other solutions provided by the company include API gateways, RBAC and SSO.
The solutions integrate with auditing and security information event management (SIEM) solutions. The company helps its clients comply with various regulatory frameworks. The company works with various clients in integrated and regulated industries to build governance frameworks for the integration of large language models.
Key Features:
- Assess risk for compliant markets
- Red Team modeling
- Manage governance frameworks
- Rule based policy controls
- SOC integration
Pros
- Office of the President and Defense markets
- Extensive consulting experience
Cons
- Oppaque pricing
- Less developer centered approach
2. NCC Group
NCC group focuses on LLM security assurance to validate remediation controls. The company provides various security frameworks and assesses the safety and security of various LLM integrations.
The company provides solutions in the area of advanced risk assessment and threat modeling. The company implements various security frameworks to assess the safety of LLMs in the enterprise and pipeline.
NCC group helps enterprise integrate various LLMs and other large language models by implementing adversarial controls. The frameworks assist enterprises in governing the safety of LLMs.
NCC Group offers SOCs with RBAC, SSO, and other enterprise-level policies, as well as audit logs, alerts, and red teaming services. Their AI products help satisfy clients’ needs for regulatory compliance.
Security professionals and organizations interested in protecting their production AI have NCC Group’s products endorsed by the company. Engagement-based pricing applies to testing and remediation services.
Expertise:
- Assurance with remediation
- LLM App Pen Tests
- Anomaly detection
- RAG validation
- Audit logs
Pros:
- Adversarial testing
- SOC 2 and ISO 27001
- Established in security
Cons
- Expensive
- Less developer tools
3. Deloitte
Deloitte has products and services for organizations interested in integrating red teaming and AI/ML operations. In addition, Deloitte specializes in mapping enterprise risks and controls and transforming findings from red teams’ activities to governance and compliance.
Products and services that support this capability include, but are not limited to, risk assessment and governance, and compliance and assurance.
Other products and services offered by Deloitte include agent and tool integration, and enforcement of compliance and governance. AI/ML products integrated through the cloud allow Deloitte to enforce compliance in a governance framework during runtime.
Like Deloitte, NCC Group has AI/ML and red teaming products, as well as audit logging and alerting capabilities. Other standard offerings for NCC Group and Deloitte include SSO, RBAC, and enterprise policies. Some of their products help regulated organizations embed governance in their use of AI/ML. Consulting services offered by Deloitte are pricey.
Expertise:
- Risk and compliance frameworks
- Policy and regulation mapping
- Consulting
- Rule based enforcement
- Audit and monitoring
Pros:
- Governance Artifacts
- Extensive consulting and market coverage
Cons
- Privacy consulting and slower pace for agile
4. PromptGuardrails
PromptGuardrails is an LLM security suites focused on prompt injection, jailbreaks, and tool-assisted data leak. It provides a range of capabilities such as in-line defense, automated red team actions, and regulatory compliance. It also offers runtime scanning and supporting RAG pipelines. It also provides an API, SDK and Cloud Gateway, and support for tool calls and AI agents.
It comes with Defensive threat Modeling, audit logs, and alerting. It has policy-based access and enterprise Integrations. It has compliance certifications with SOC 2 and ISO 27001. Best suited for Enterprises for RAG integrations and AI agents. It also supports other Enterprises applications. It has a tiered pricing model with a development and enterprise suit.
Expertise:
- Compliance and enforcement
- Real time prompt injection
- Policy and regulation
- Runtimes Scanning
- Auditing
Pros
- SaaS
- Developer friendly
- Subscription
Cons
- Less established
- Consulting support
5. Lakera Guard
Low latency scanning is the basis for Lakera Guard’s ability to quickly identify prompts. It protects against jailbreaks and intentional malicious user actions and data extraction. It uses in-memory scanning and runtime policy controls. The RAG pipelines are protected by retrieval validation. Lakera Guard also supports the protection of RLMs.
Integrations to OpenAI, Anthropic, and Llama are available via API and SDK. Lakera Guard employs a runtime protection model to scan I/O with low latency.
Lakera Guard provides real-time scanning and alerts. Administrator controls are provided via RBAC and SSO. Audit logs are available. Lakera Guard has SOC 2 and ISO 27001 compliance. The best market fit is developers and companies needing rapid low latency prompt scanning. Lakera Guard charges a subscription and offers discounted pricing for developers and companies.
Key Features
- Low latency prompt injection scanning
- Anomaly Detection
- RAG Validation
- Policy Enforcement
- Audit Logs
Pros
- Low Latency
- Simple Integration
- Software Development Kit and Application Programming Interface
- High Software Company Internship/Full-Time Employee Count
Cons
- Lack of Independent Software/Hardware Validation
- Lack of Competitor Analysis
6. Protect AI Guardian
Protect AI Guardian is a solution that offers a range of features and functions to provide security for LLMs. It offers model security testing and scanning, and provides security control and compliance and logging features.
It provides secure pipelines with retrieval validation and protects the use of AI tools and agents. It provides integration with different LLMs, such as OpenAI and Anthropic, through a cloud API and SDK.
It provides security and compliance by scanning and protecting input and output data. It provides integrations through a cloud API. It also provides a range of features for red teaming and monitoring and reporting.
It also supports auditing. It can generate alerts. It supports RBAC and SSO. It provides compliance certifications. It supports different frameworks. It provides security and compliance reporting. It supports different use cases across different industries. It supports various enterprise AI use cases and reporting. Pricing is through a subscription model.
Key Features
- Comprehensive Threat Testing
- Policy enforcement and scanning
- Automated Compliance Reporting and Dashboards
- Red Teaming and Validation Services
Pros
- Enterprise Solution
- Threat Coverage
- SaaS Offering
- Automation
Cons
- Price
- Complexity
- Limited Documentation
7. Robust Intelligence
The primary service of Robust Intelligence is automated adversarial red teaming, including prompt injection, jailbreaking, adversarial attacks, and data leakage. Other services include advanced threat simulation and attack generation.
The company offers a pipeline called RAG for attack generation and scanning. AI agents and tools also have security and integrations with large language models. Other services include anomaly detection and enforcement of compliance.
The company sells alerting and audit logging services. Additional security features include adversarial detection. Best practiced upper suite use cases include security and I.T. teams for the automatic and advanced testing of systems and services. Other teams benefit from the automation of testing and integrations offered.
Key Features
- Automated adversarial testing
- Customized assaults
- Identification of deviations and irregularities at the runtime environment
- Automated RAG validation
- Audit logs
Pros
- Automated tools for red teaming
- Contributions in the areas of development and integration
Cons
- Main focus is assessment and review
- Costs may be based on usage
8. NIGHTVISION
NIGHTVISION provides real-time protection against vulnerabilities for Large Language Models (LLMs). Capabilities include protection against prompt injection, jail breaks, and runtime adversarial behaviors and malicious compliance.
Additional capabilities include scanning and analyzing runtime behavior. NIGHTVISION’s RAG pipelines utilize retrieval validation. NIGHTVISION also provides security for AI tool/agent call in a production setting.
NIGHTVISION is compatible with OpenAI and other LLMs. Protection is provided at the runtime and I/O stage. NIGHTVISION also provides a dashboard to configure RBAC, and integrates with SSO. Audit logs are provided.
NIGHTVISION is ISO 27001 and SOC 2 certified. According to the vendor, best fit uses cases are organizations requiring real time monitoring and anomaly detection. Pricing is subscription based and charges are evaluated on an organizational basis.
Key Features
- Real-Time Monitoring for Large Language Models (LLMs)
- Anomaly Detection
- Runtime Scanning
- Performance Metrics
- Audit Logs
Pros
- Real-time visibility during runtime
- Integration with Security Operations Centers (SOC)
- Software as a Service (SaaS) model
Cons
- More monitoring than testing
- No compliance attestations
9. Noma Security
Noma Security specializes in managing risks and governance associated with AI assets, including prompt injection, jailbreaking, data leakage, and compliance. It offers AI-based discovery and risk assessment, and provides real time compliance.
The access control lists are integrated with RAG pipelines, and the control flow is secured. Support is provided for RAG pipelines, AI agent/tools. Integrations with OpenAI and Anthropic, along with Gemini and Llama, are provided through a cloud gateway and API. Real time input and output scanning is performed, and access is enforced according to company policy.
The solution offers monitoring and logging of access and compliance. Additional features include alerting, audit logging, and support for enterprise polices (e.g. SSO and RBAC). The solution has compliance certifications of SOC 2 and ISO 27001. The solution is designed for organizations to manage AI assets governance and risks.
Key Features
- AI Asset Mapping
- Risk Identification
- Runtime Scanning
- Compliance Scanning
- Audit Logs
Pros
- Focused on governance and asset identification
- Customer controls and integrations
- SO Committee of Service Organizations (COSO) 2 Type 2 certification
Cons
- Less focus on Runtime Adversarial Scanning
- Astrix Security
10. Astrix Security
Astrix Security offers non-human IAM for AI applications and agents, to prevent prompt injection, data leakage and other risks. It includes a real time compliance and risk assessments. Access control lists are integrated with RAG pipelines.
Support is provided for RAG pipelines and AI tools/agents. Integrations with OpenAI and Anthropic are provided along with real time compliance and risk assessment. Real time input and output scoping is enforced.
Monitoring, alerting and logging are provided. Access and compliance logs are audit logs. Additional features include alerting and auditing. The solution has compliance certifications of SOC 2 and ISO 27001. The solution is designed for organizations to manage risks associated with IAM for AI applications and agents.
Key Features
- Managing access to and from Artificial Intelligence (AI) Agents
- Runtime Scanning and Policy Enforcement
- Audit Logs
Pros
- First solution to market focused on the Identity of Artificial Intelligence (AI) Agents
- Integration with customer controls and separations of duty for Enterprise
- SO Committee of Service Organizations (COSO) 2 Type 2 certification
Cons
- Narrow solution compared to other LLM security offerings
- Pricing enterprise‑tier only
LLM Security vs AI Governance vs AI Observability
| Aspect | LLM Security | AI Governance | AI Observability |
|---|---|---|---|
| Primary Focus | Protects LLMs from adversarial attacks, unsafe tool use, and data leakage | Ensures responsible, ethical, and compliant AI deployment | Provides visibility into AI system performance, reliability, and behavior |
| Major Threats Covered | Prompt injection, jailbreaks, data leakage, model inversion | Bias, fairness, regulatory non-compliance, accountability gaps | Drift, anomalies, hallucinations, performance degradation |
| Key Security Capabilities | Runtime input/output scanning, red teaming, policy enforcement, RBAC | Policy frameworks, audit trails, compliance mapping, ethical guidelines | Monitoring pipelines, anomaly detection, logging, performance metrics |
| RAG Security | Validates retrieval pipelines against malicious queries | Ensures retrieved knowledge aligns with governance standards | Observes retrieval quality, latency, and accuracy |
| AI Agent / Tool-Call Security | Controls agent actions and tool-call permissions | Defines governance rules for agent behavior | Observes agent execution, latency, and error rates |
| Integrations | OpenAI, Anthropic, Gemini, Llama, enterprise APIs | Enterprise compliance systems, legal frameworks, regulators | Monitoring tools, dashboards, observability stacks |
| Deployment Method | API, SDK, gateway, cloud, self-hosted | Policy frameworks, enterprise governance platforms | Cloud dashboards, monitoring agents, APIs |
| Runtime Protection | Input/output scanning, anomaly detection, policy enforcement | Governance rules applied at runtime | Continuous monitoring, alerts, and telemetry |
| Security Testing / Red Teaming | Adversarial testing, penetration simulation | Governance audits, compliance reviews | Stress testing, performance benchmarking |
| Monitoring & Alerts | SOC integration, audit logs, anomaly alerts | Compliance dashboards, audit logs | Real-time monitoring, anomaly alerts, performance logs |
| Enterprise Controls | RBAC, SSO, enterprise policies | Governance policies, ethical guidelines, accountability frameworks | Observability dashboards, role-based access to logs |
| Compliance / Certifications | ISO 27001, SOC 2, FedRAMP (platform dependent) | GDPR, HIPAA, AI Act, industry-specific frameworks | SOC 2, ISO 27001 (observability vendors) |
| Best-Fit Use Case | Enterprise apps, RAG, AI agents, SOC teams | Regulated industries, compliance-driven enterprises | Developers, MLOps teams, reliability engineers |
Conclusion
LLM Security Platforms provide enterprise customers the ability to secure their AI deployments. These platforms provide protection from prompt injection and other jailbreaks, and provide loss and leakage of confidential data.
In addition, these platforms provide protection against the use of unapproved tools. As part of the offered protection, these platforms provide for scanning of the deployed models. In comparison, AI Governance platform protect ethical and legal use of deployed AI models.
On the other hand, AI Observability platforms help in detecting issues with deployed AI models. Hence, LLM Security platforms are the only AI platform providing protection against attacks on deployed models. The LLM security platforms integrate with the large language models from OpenAI, Anthropic, and others, and provide enterprise controls.
FAQ
What is an LLM Security Platform?
An LLM Security Platform safeguards large language models against adversarial misuse, covering threats like prompt injection, jailbreaks, and data leakage, while enforcing runtime policies and compliance controls.
Why do enterprises need LLM Security?
Enterprises need LLM Security to protect sensitive data, ensure compliance, prevent unsafe tool use, and maintain trust in AI deployments across regulated industries and mission-critical applications.
What threats are covered?
Platforms defend against prompt injection, jailbreaks, model inversion, data leakage, unsafe tool calls, and adversarial misuse, ensuring resilience in enterprise workflows.
How does LLM Security differ from AI Governance?
LLM Security focuses on technical defense against attacks, while AI Governance ensures ethical, compliant, and responsible AI use through policies, audits, and regulatory alignment.
Do these platforms support RAG workflows?
Yes, most platforms validate retrieval pipelines, prevent malicious query injection, and enforce safe knowledge integration for retrieval-augmented generation (RAG) systems.
