AI Security Tools
AI security tools and solutions for protecting artificial intelligence systems, machine learning models, and AI-powered applications from cyber threats.
Browse 353 ai security tools
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Academic research lab focused on privacy-preserving and secure AI/ML.
AI governance & testing platform for ML models and LLMs in FinServ.
AI agent discovery & security posture mgmt for enterprise agentic ecosystems.
Ascend AI delivers continuous adversarial testing and exploit discovery for agentic AI.
MCP governance platform for securing and controlling enterprise AI agents.
Open-source framework for real-time LLM safety, policy & compliance enforcement.
LLM pipeline observability: tracing, monitoring, and alerting for GenAI systems.
AI agent testing platform for security, reliability, and behavior validation.
ML testing platform for validating models pre/post-deployment via CI/CD.
API gateway for managing, securing, and observing outbound LLM traffic.
Gateway for securing, governing, and auditing AI agent access to MCP servers.
GitHub Action scanner for LLM-specific app vulnerabilities like prompt injection.
Open-source LLM vulnerability scanner for AI red teaming and security testing.
Proxy layer for controlling and monitoring MCP server access in AI apps.
Adaptive LLM guardrails that self-improve via red team feedback loops.
Agentic platform enforcing real-time AI prompt governance & Shadow AI control.
AI control plane for enterprise AI agent security, governance, and observability.
AI-powered document fraud detection for PDFs and images in under 20s.
Security & governance platform for evaluating and securing enterprise AI systems.
AI security platform offering both Security for AI and AI for Security.
Agentic AI security platform for inventory, posture mgmt, and threat detection.
AI Security Specializations
353 tools across 10 specializations · 16 free, 337 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.
Yes. Out of 24 ai security tools listed on CybersecTools, 5 are free and 19 are commercial. Free tools work well for small teams, testing, and budget-conscious organizations. Commercial tools typically add enterprise features, dedicated support, and SLA guarantees.
