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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Runtime security platform for protecting AI-powered apps and agentic AI.
Secures Salesforce Agentforce AI workflows via visibility, monitoring & governance.
Privacy-preserving AI agent platform for running LLMs on sensitive data.
AI/ML model security tool for internal vulnerability analysis in defense apps.
Secures MCP sessions in AI dev environments via proxy, discovery, and policy enforcement.
Secures AI-assisted dev environments from prompt injection, DLP, & shadow AI.
Privacy layer enabling confidential AI & data analytics for AIaaS providers.
Governs autonomous AI agents with context-aware authz, policy control & audit.
Security audit service for agentic AI systems via threat modeling & red teaming.
Real-time inventory tool for discovering and monitoring all AI usage across an org.
Centralized AI governance platform for monitoring and enforcing AI usage policies.
Aggregates & analyzes LLM logs from multiple AI providers for security & governance.
AI security platform for discovering, monitoring, and protecting AI integrations.
Automated LLM security testing platform detecting prompt injection & data leaks.
Centralized audit trail logging for AI model usage to support compliance.
PETs-powered encrypted ML training, inference, and validation across data silos.
Privacy-preserving AI research assistant for secure analysis of sensitive data.
Secure multiparty data collaboration platform using TEEs for AI/ML workloads.
Platform for privacy-protected AI/ML model training on sensitive data.
AI red teaming platform for adversarial testing of deployed AI systems.
Runtime security governance for AI agents operating over MCP environments.
Dual-layer AI security platform for RAG chatbots covering model and retrieval.
AI agent governance platform securing MCP traffic, prompts, and data access.
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.
