AI Security Tools
AI security tools and solutions for protecting artificial intelligence systems, machine learning models, and AI-powered applications from cyber threats.
Browse 347 ai security tools
FEATURED
USE CASES
Privacy-preserving AI inference platform using Fully Homomorphic Encryption
End-to-end LLM security platform protecting GenAI interactions & applications
Continuous vulnerability scanning for GenAI systems and LLM applications
Secures AI coding assistants by controlling data access and monitoring prompts.
FHE-based encryption for AI models, vector databases, and RAG workflows
AI governance platform for managing AI system lifecycle and compliance
Detects deepfakes in audio, video, images, and documents using AI models.
Confidential AI platform for deploying AI agents on sensitive data securely
Confidential computing platform for secure RAG and AI agent workflows
Enterprise MCP gateway for managing, securing & controlling AI agent access to systems
Security layer for OpenClaw AI agents protecting against prompt injection attacks
Confidential computing platform for private, verifiable AI inference on sensitive data.
Deepfake detection platform for video, image, and audio content analysis
AI governance platform for risk assessment, compliance, and policy enforcement
AI model protection platform securing on-device models from reverse engineering
Governance platform for LLM-based apps with visibility and compliance monitoring
Defend AI delivers runtime security guardrails with >98.1% accuracy and subsecond latency.
AI security platform for data protection across AI/ML development lifecycle
Private AI model hosting platform for on-premises deployment in secure environments
Detects AI-generated audio/video in real-time across voice and video channels
Real-time guardrails for AI agents, models, and apps with multimodal protection
AI Security Specializations
347 tools across 10 specializations · 16 free, 331 commercial
Agentic AI Security
Security tools for protecting AI agents, MCP servers, multi-agent systems, and autonomous AI workflows.
AI Data Poisoning Protection
Data poisoning protection tools that detect and prevent malicious data injection attacks targeting AI training datasets and machine learning models.
AI Governance
AI governance platforms for managing AI risk, compliance, policy enforcement, and responsible AI adoption across the enterprise.
Articles About AI Security
Tool roundups, buying guides, and strategic analysis from the CybersecTools resource library.
Best Agentic AI Security Tools in 2026
The 7 best agentic AI security tools in 2026: runtime protection, governance, red teaming, and secure execution for AI agents.
Best AI SPM Tools in 2026
The 7 best AI SPM tools in 2026 reviewed: Prisma AIRS, Zscaler AI, Sysdig, Zenity, Noma, and more. Find the right fit for your AI security stack.
Best AI Security Tools in 2026
The 7 best AI security tools in 2026 reviewed: CrowdStrike Falcon AIDR, Prisma AIRS, FortiAI, SkopeAI, Lakera Red, Cyera AI Guardian, and Secure AI Factory.
AI Security Tools FAQ
Common questions about AI Security tools, selection guides, pricing, and comparisons.
AI security focuses on protecting AI systems, machine learning models, and AI-powered applications from adversarial attacks, data poisoning, model theft, and misuse. As organizations deploy LLMs, GenAI, and autonomous AI agents, securing these systems is critical to prevent prompt injection, data leakage, hallucination-based risks, and unauthorized access to sensitive training data.
The top threats include prompt injection (manipulating LLM inputs to bypass guardrails), data poisoning (corrupting training datasets), model extraction (stealing proprietary models through API queries), adversarial attacks (crafting inputs that cause misclassification), and shadow AI (unauthorized AI tool usage leaking corporate data). The OWASP Top 10 for LLM Applications provides a comprehensive framework for understanding these risks.
Traditional cybersecurity protects infrastructure, networks, and applications using well-defined perimeter controls. AI security deals with probabilistic systems where behavior is non-deterministic, making threats harder to detect and prevent. AI-specific challenges include securing model weights, preventing training data extraction, detecting adversarial inputs in real-time, and governing AI usage across the organization.
Existing security tools (WAFs, DLP, endpoint protection) do not address AI-specific threats like prompt injection, model poisoning, or adversarial ML attacks. Dedicated AI security tools provide runtime guardrails for LLMs, AI asset discovery, model vulnerability scanning, and AI-specific threat detection that traditional tools cannot replicate.
