As organizations race to adopt large language models (LLMs) and generative AI (GenAI), the security risks are multiplying just as fast. From prompt injection and model inversion to data leakage and unauthorized fine-tuning, these systems introduce entirely new attack surfaces that traditional security tools weren’t designed to handle.

In this article, we’ve identified 10 leading AI security tools engineered to secure LLMs and GenAI applications. These platforms offer offensive and defensive capabilities to identify, test, and mitigate AI-specific threats before they become breaches.  

Mindgard

Mindgard is a leading AI security solution specializing in safeguarding AI Systems including LLMs and GenAI applications through automated red teaming and continuous security testing. Developed from over a decade of academic research at Lancaster University, Mindgard protects AI systems from vulnerabilities that traditional security tools often overlook. The platform enables organizations to detect and address threats specific to AI systems through proactive measures, delivering comprehensive protection throughout the AI lifecycle. 

Mindgard’s primary offering is its Offensive Security solution, which uses simulated adversarial attacks to discover weaknesses like prompt injections, model inversion, data poisoning, and evasion attacks. This dynamic approach uncovers runtime-specific risks, providing a level of security that static analysis tools can’t achieve. Mindgard integrates seamlessly into existing CI/CD pipelines to provide continuous monitoring and rapid remediation capabilities, reducing testing times from months to minutes. 

Mindgard’s platform is model-agnostic, supporting a wide range of AI systems including LLMs, NLP models, and multi-modal applications. Its extensive attack library, aligned with frameworks like MITRE ATLAS™ and OWASP, enables security teams to proactively combat new threats. Mindgard delivers compliance-ready reporting capabilities and securely connects with current SIEM systems, making AI security both actionable and auditable. 

Key Features: 

• Automated red teaming simulates adversarial attacks like prompt injection, model inversion, and data poisoning 
• CI/CD pipeline integration embeds AI security testing directly into your development workflow 
• Runtime threat detection identifies vulnerabilities during execution, not just through static analysis 
• Extensive attack library aligned with MITRE ATLAS™ and OWASP frameworks 
• Compliance-ready reporting for internal and external regulatory requirements 
• Continuous monitoring enables ongoing evaluation of AI models in production environments
• Covers development, deployment, and post-deployment stages to secure the AI development lifecycle 

Amazon Bedrock Guardrails

Amazon Bedrock Guardrails is a suite of configurable safety features designed to help organizations build secure, responsible, and trustworthy generative AI applications at scale. Integrated directly into the Amazon Bedrock platform, Guardrails provide a standardized approach to implementing safety and compliance rules across a variety of foundation models (FMs), including those supported in Amazon Bedrock, fine-tuned models, and even models hosted outside of Amazon Bedrock. 

Guardrails incorporates contextual grounding checks to assess the factual accuracy and relevance of model responses to user queries, eliminating the risk of hallucinations. The platform features Automated Reasoning checks which use logical and mathematical methods to ensure model outputs match against known documents and policies while preventing fabricated or inconsistent data. Customizable filters can identify and block harmful content types, including hate speech, insults, sexual content, violence, and misconduct across text and image media. 

Key Features: 

• Identifies and optionally masks personally identifiable information (PII) such as names, addresses, and phone numbers to protect user privacy
• Assesses whether a model’s response is grounded in the provided context or source material
• Uses formal methods to verify that generated responses align with pre-approved documents or facts, reducing hallucinations and misinformation
• Offers a point-and-click interface for setting up guardrails—no ML or coding expertise required
• Provides visibility into when and how guardrails are triggered, supporting transparency and continuous improvement

Holistic AI 

Holistic AI is an enterprise AI governance platform offering a variety of tools for AI risk management, regulatory compliance, and audit readiness. The platform includes automated discovery, risk scoring tools, and lifecycle assessments to help organizations track and control AI risks in both technical and governance areas. 

Holistic AI’s centralized dashboards and real-time monitoring capabilities produce actionable insights that support decision-making and maintain continuous AI governance. Its AI Safeguard solution helps enterprises leverage LLMs safely by preventing data exposure, bias, and ethical issues. 

Key Features: 

• Centralized, real-time dashboards for monitoring compliance, risk status, and operational metrics
• Supports adherence to global AI regulations like the EU AI Act and NIST AI Risk Management Framework
• Tools for detecting and mitigating algorithmic bias in AI models
• Automatically redacts or blocks sensitive information from LLM inputs and outputs
• Tests LLMs with static and dynamic prompts to uncover vulnerabilities and improve model robustness

Adversarial Robustness Toolbox

The Adversarial Robustness Toolbox (ART) is an open-source Python library developed to enhance the security and reliability of machine learning models against adversarial threats. Hosted by the Linux Foundation AI & Data Foundation, ART provides tools that enable developers and researchers to evaluate, defend, certify, and verify machine learning models and applications against adversarial threats, including evasion, poisoning, extraction, and inference attacks.

ART is designed for customization and extension, allowing researchers and developers to contribute new attacks, defenses, and evaluation methods. For more information and access to the toolbox, you can visit the Adversarial Robustness Toolbox GitHub repository.

Key Features: 

• Supports a wide range of adversarial attacks (evasion, poisoning, extraction, and inference) across different data modalities (image, text, audio, and tabular)
• Provides tools for measuring model robustness, helping developers quantify and understand vulnerabilities
• Compatible with TensorFlow, PyTorch, Keras, MXNet, scikit-learn, XGBoost, LightGBM, CatBoost, and others
• Includes methods such as adversarial training, input preprocessing, and model regularization to harden models against adversarial inputs
• Works across different machine learning tasks including classification, object detection, speech recognition, and generative modeling

NVIDIA Flare

NVIDIA Flare (Federated Learning Application Runtime Environment) is an open-source, domain-agnostic SDK that enables secure privacy protection during the federated learning (FL) process. This allows organizations to train and fine-tune LLMs and GenAI applications without centralizing sensitive data, which helps to address challenges related to data privacy and regulatory compliance.  

In traditional machine learning workflows, aggregating data from multiple sources can introduce privacy concerns, legal restrictions, and logistical complexities. FLARE provides a solution to these challenges by enabling clients to train models directly on their own datasets without sharing raw data. NVIDIA Flare works with PyTorch, TensorFlow, XGBoost, and other popular machine learning frameworks, as well as federated learning algorithms including FedAvg, FedProx, and FedOpt.

Key Features: 

• Enables collaborative model training across decentralized data sources without sharing raw data
• Supports supervised fine-tuning (SFT) and parameter-efficient fine-tuning (PEFT) for large language models
• Incorporates differential privacy and homomorphic encryption to safeguard sensitive information
• Provides flexible APIs and plugin support for custom workflows and components
• Designed to meet privacy and security requirements in regulated industries like healthcare and finance

Flower

An open-source federated learning framework, Flower enhances the privacy, scalability, and personalization aspects of LLMs and GenAI applications. It enables decentralized model training and inference, which allows organizations to build AI systems that maintain data sovereignty and ensure user confidentiality. 

Flower’s federated learning architecture enables multiple devices or organizations to train models collaboratively without exchanging raw data. Flower also advances privacy-perserving AI with Flower Intelligence, a hybrid platform that combines local device inference and confidential remote compute capabilities. 

Key Features: 

• Supports federated fine-tuning of large language models (e.g., LLaMA2) using private datasets to enhance model accuracy while maintaining confidentiality
• Combines on-device inference with secure, confidential remote compute for flexible and privacy-conscious GenAI deployments
• Prevents sensitive data from leaving local environments, aligning with privacy regulations and security best practices
• Supports a range of federated learning setups—from edge devices to cloud environments—tailored to specific GenAI use cases
• Offers modular components and APIs, making it easy for developers to integrate federated learning into existing ML pipelines

Netskope

Netskope addresses concerns associated with shadow AI through its SkopeAI suite, which delivers extensive visibility and management capabilities over AI activities within an organization. The system monitors AI application user interactions and differentiates between personal and corporate instances to enforce granular policies according to user behavior and data sensitivity.

To protect sensitive data from being inadvertently shared with or used to train public or private LLMs, Netskope employs advanced data loss prevention (DLP) and data security posture management (DSPM) tools. These tools automatically discover, classify, and label data across various storage environments, enabling organizations to set policies that prevent confidential information from being exposed through AI interactions.

Key Features: 

• Identifies and monitors usage of over 370 GenAI apps, including distinctions between corporate and personal use
• Detects unsanctioned or unauthorized AI tools being used across the organization
• Applies contextual, real-time policies based on user identity, device, app instance, and activity
• Provides risk ratings for over 82,000 apps, including GenAI tools, to guide access decisions
• Prevents sensitive data from being shared with or used to train LLMs, using advanced classification and content inspection

Nightfall AI

The Nightfall AI DLP platform provides robust protection for sensitive data across cloud applications, endpoints, and GenAI tools. The system acts as a trust boundary, protecting both corporate data and customer information during the development and deployment of AI models. 

One of Nightfall AI’s core capabilities is its ability to detect and prevent the exposure of sensitive data, such as personally identifiable information (PII), payment card information (PCI), and protected health information (PHI). It addresses risks associated with data exposure, privacy breaches, and prompt-based attacks that can result in accidental data leaks or AI behavior manipulation. 

Key Features: 

• API library that prevents sensitive data from being used in LLM training, annotation, or fine-tuning processes
• Browser plugin that detects and redacts sensitive data before it's entered into AI tools like ChatGPT
• Enables workflows that automatically respond to policy violations by redacting, quarantining, or alerting on sensitive data
• Provides comprehensive logging and insights into how sensitive data is accessed, shared, and used within GenAI workflows
• Connects with popular SaaS, messaging, and AI platforms

Cyberhaven

Cyberhaven provides organizations with tools enhanced visibility and control measures to manage and secure their data exchanges with AI applications. It can detect and manage shadow AI usage when employees operate AI tools without the IT department’s knowledge. 

Cyberhaven’s data detection and response (DDR) technology enables real-time monitoring of data exchanges with AI applications, capturing information without the need for pre-configured policies. This helps prevent sensitive data from being input into unapproved AI tools and tracks the origin of AI-generated content to mitigate the risks associated with malicious or inaccurate outputs. 

Key Features: 

• Safeguards source code, training data, and proprietary models from leaks or theft
• Tags AI-generated outputs to ensure transparency and accountability
• Differentiates between personal and corporate accounts to apply appropriate controls
• Provides guidance to employees on secure and compliant AI usage
• Enables flexible, role-based restrictions for AI tool usage

Symmetry

Symmetry analyzes the risks associated with GenAI deployments, particularly in environments like OneDrive, SharePoint, and Microsoft Teams. The platform ensures that sensitive data is identified and monitored before it can be accessed or embedded by AI models, which helps to prevent data leaks. 

The platform also focuses on preserving organizational compliance and ethical standards. By continuously monitoring user access to information through GenAI, Symmetry helps enforce regulatory requirements and uphold ethical standards. 

Key Features: 

• Maintain an inventory of various generative AI models in use, including LLaMA, OpenAI, ChatGPT, and others
• Integrates with existing security stacks, including SIEM solutions, SOAR platforms, and ticketing systems
• Ensures regulatory compliance and uphold ethical standards by monitoring AI interactions with sensitive data
• Tailor classification rules and train models on your own data
• Detect sensitive information within images and PDFs across unstructured data stores

How to Choose the Best AI Security Tools for LLM and GenAI Protection

Protecting LLMs and GenAI applications requires solutions beyond conventional security measures. These systems behave differently—they’re dynamic, probabilistic, and often unpredictable. Choosing the right security tools starts with mapping out the threat surface and identifying solutions purpose-built to address it. 

Start by identifying your system’s exposure points. Security measures should begin by pinpointing exposure areas, including model endpoints, training pipelines, inference APIs, and the data they consume. Choose security tools that perform active stress tests on each system layer, rather than just monitoring them. 

Red teaming capabilities specifically designed for LLMs are essential. Red teaming tools must be able to simulate prompt injection attacks, data leakage issues, and jailbreak scenarios in a controlled, repeatable way. 

Next, prioritize visibility. Real-time telemetry should map user behavior, model responses, and detect any atypical patterns. The goal is to observe attacks during their active progression, rather than retrospective logging. Security solutions must work with current SIEM, SOAR, and EDR systems without requiring significant customization. 

Look for AI security companies offering solutions that can detect subtle changes in model behavior. Adversarial inputs, poisoned training data, and malformed prompts can degrade performance or extract sensitive information. Defense tools need protective measures like input sanitization, response filtering, and output watermarking that can adapt to evolving threats. 

Finally, make sure the solution supports both offensive and defensive measures. Red teams find the cracks, and blue teams fix them. The best tools enable you to handle offensive and defensive tasks quickly. 

Final Thoughts

The AI threat landscape is evolving fast. Traditional tools weren’t built to handle model-level exploits, generative misuse, or runtime vulnerabilities in LLMs. Securing your GenAI systems and LLMs requires you to move beyond surface-level defenses, leveraging specialized tools that understand these systems’ operations and weaknesses. 

Unlike static scanners or generic monitoring platforms, Mindgard’s Offensive Security solution is purpose-built for AI adversarial testing. It goes beyond compliance checklists to include emulating real-world attacks, stress-testing LLMs, and detecting runtime vulnerabilities that only appear during execution. With CI/CD integration and a model-agnostic engine, Mindgard embeds security directly into your AI pipeline, from development to deployment. Request a demo today to discover how Mindgard can help you secure your GenAI and LLM applications.   

Frequently Asked Questions 

What are the biggest security risks for LLMs and GenAI?

LLMs and GenAI systems face unique risks including prompt injection, data leakage, model inversion, training data poisoning, and adversarial inputs designed to manipulate outputs or extract confidential information.

How does automated red teaming improve AI security?

Automated red teaming simulates real-world attacks on AI models—like prompt injection, evasion, and data extraction—to uncover vulnerabilities before adversaries exploit them. It enables continuous, scalable testing without relying solely on manual audits.

What’s the role of federated learning (e.g., NVIDIA Flare, Flower) in AI security?

Federated learning enhances security and privacy by keeping raw training data decentralized. Instead of centralizing sensitive data, it shares model updates, reducing exposure risks and supporting compliance with data protection regulations.

How do I integrate AI security into my existing DevOps pipeline?

AI security tools can be integrated into CI/CD workflows by embedding automated testing, red teaming, and monitoring steps. This allows real-time scanning for vulnerabilities and continuous model evaluation alongside regular software builds and updates. Solutions like Mindgard and ART offer CI/CD integration, enabling automated security testing during development, deployment, and production stages.