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
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AI-native security platform for agentic frameworks and LLM applications
AI Security Posture Management platform for discovering and securing AI agents
AI Detection and Response platform for securing AI agents and applications
Red teaming platform for testing AI agents against adversarial attacks
Security platform for AI agents with real-time behavior monitoring & control
GenAI security platform protecting against data leaks and prompt attacks
AI security platform for lifecycle protection, governance, and runtime defense
Observability platform for monitoring and analyzing AI agent interactions
Security platform for Agentic AI with discovery, policy control & detection
Real-time AI guardrails platform for detecting misuse, hallucinations & attacks
AI agent security platform for Web3 with audits and breach prevention
Protects AI models from theft, misuse & reverse engineering via licensing
AI observability platform for shadow AI discovery and inventory management
AI Security Posture Management platform for AI/ML infrastructure security
Autonomous AI agent security platform for testing, detecting, and defending AI workforces.
Real-time intent analysis platform for detecting and preventing AI agent threats.
Automated AI red teaming tool for testing AI model vulnerabilities
Library of AI threat detection signals for securing generative AI models
Provides real-time monitoring and oversight for agentic AI systems
AI risk assessment tool that scores AI apps and MCP servers for security
GenAI security platform for shadow AI discovery, prompt injection defense & DLP
Security skill suite for OpenClaw AI agents with hardening capabilities
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.
