TL;DR: Most AI security tools cover one phase of the problem: a runtime guardrail, a model scanner, or a red teaming suite. Production AI security requires three phases in sequence: discover what AI you're running, attack it before attackers do, then block threats at runtime using what you learned. This guide ranks six platforms by how much of that lifecycle they actually cover, and calls out which competitors have been acquired and what that means for buyers.

Why one-phase tools leave gaps

An AI security program that skips asset discovery is defending a perimeter it cannot see. A program that skips adversarial testing ships runtime guardrails calibrated against known attack patterns rather than the actual vulnerabilities in the deployed system. A program that skips runtime protection finds vulnerabilities and does nothing with them until the next pentest cycle.

The three-phase model addresses this in sequence:

Phase 1: Inventory. Discover every AI model, agent, and agentic workflow across the organization. Build an AI Bill of Materials. Map the threat graph showing attack paths and blast radius per asset. Without this, red teaming and runtime protection are applied to a partial picture.

Phase 2: Red teaming. Run adversarial testing against the live application stack, not a model endpoint in isolation. This means attacking RAG pipelines, tool integrations, browser-based agents, and MCP-connected systems with real attack patterns. Findings at this phase are the input to phase 3 — not a PDF that goes in a drawer.

Phase 3: Runtime protection. Deploy guardrails calibrated from actual red teaming results. Blocking policies built from first-party attack data are more accurate than policies built from generic threat feeds. The difference in false positive rates is material.

Most vendors cover one phase. A few cover two. The platforms below are ranked by coverage depth across all three.

The 6 best AI security platforms in 2026

1. Repello AI

Repello is the only platform in this comparison with purpose-built products for all three phases, designed to operate as a connected pipeline rather than separate point solutions.

Phase 1 — AI Inventory (repello.ai/inventory) discovers every AI asset across the organization automatically: models, agents, agentic workflows, shadow AI deployments. It builds an AI Bill of Materials, generates threat graphs showing attack paths and blast radius, and maintains a living inventory that updates as the AI surface evolves. For security teams that cannot currently answer "what AI is running in our environment," this is the starting point.

Phase 2 — ARTEMIS (repello.ai/product) is the red teaming engine. It runs 15M+ attack patterns across OWASP LLM Top 10, NIST AI RMF, and MITRE ATLAS against the full application stack: prompt interface, RAG pipeline, tool integrations, and browser-based agents via ARTEMIS Browser Mode. Findings include exploitation evidence, blast radius assessment, and compliance-mapped remediation. The attack library is informed by Repello's own research team, which publishes first-party vulnerability data — including a documented 4.8% breach rate against Claude versus 28.6% against GPT-4-class models in production red teaming engagements.

Phase 3 — ARGUS (repello.ai/argus) deploys runtime protection calibrated directly from ARTEMIS findings. Guardrails are not generic; they reflect the specific attack vectors confirmed against the specific deployment. ARGUS blocks malicious inputs in under 100ms with zero user-facing latency, applies context-aware policies for fraud, credit, and compliance use cases, and covers 100+ languages. The MCP Gateway (repello.ai/mcp-gateway) extends runtime protection to MCP-connected agentic systems with real-time monitoring, malicious server blocking, and full audit trails.

The offensive-first architecture is the structural differentiator. ARGUS's blocking policies start from what ARTEMIS found, not from a rulebook assembled independently of the actual attack surface. Runtime security built on red teaming data catches the attacks that generic guardrails miss.

What it covers: All three phases; RAG and agentic testing; browser-mode agent red teaming; MCP security; 100+ language coverage Strengths: Only full-lifecycle platform in this comparison; ARGUS calibrated from ARTEMIS data; MCP Gateway for agentic deployments; Gartner Emerging Tech recognition Limitations: Commercial; pricing on request Best for: Enterprise teams deploying AI at scale with agentic components, MCP integrations, or compliance requirements across multiple frameworks Pricing: Commercial, pricing on request URL: repello.ai/product | repello.ai/get-a-demo

2. HiddenLayer

HiddenLayer focuses on the model artifact layer: scanning model files for embedded malware, backdoors, and unsafe serialization (Pickle exploits, unsafe Hugging Face imports), and monitoring deployed models at runtime for inference anomalies, model theft, and behavioral drift.

The model scanning capability is production-grade. Guardian-style scanning detects malicious code embedded in model weights, which is a real supply chain risk given the volume of third-party models sourced from Hugging Face and similar repositories. Runtime monitoring flags anomalous inference patterns that may indicate active exploitation or model extraction attempts.

Application-layer attack surfaces are outside HiddenLayer's scope. Prompt injection, RAG poisoning, agentic tool-call hijacking, and indirect injection via external content are not what the platform is designed to test or block. Teams using HiddenLayer for model security still need separate tooling for application-layer red teaming and runtime guardrails.

What it covers: Phase 2 partial (model-layer scanning); Phase 3 partial (runtime model monitoring); supply chain Strengths: Deep model artifact scanning; inference anomaly detection; supply chain protection Limitations: No application-layer red teaming; no RAG, agentic, or MCP coverage; no AI asset discovery Best for: Teams securing fine-tuned model artifacts; MLOps pipelines sourcing third-party models; monitoring for production model theft Pricing: Commercial, pricing on request URL: hiddenlayer.com

3. Mindgard

Mindgard provides automated AI security testing across LLMs, computer vision models, and multimodal systems. Findings map to OWASP LLM Top 10 and MITRE ATLAS. The platform supports continuous testing via an API that integrates into MLOps pipelines and covers adversarial attacks across text, image, and audio modalities.

Multimodal coverage is the primary differentiator relative to other commercial platforms. For deployments that combine a text LLM with a vision or audio model, Mindgard reduces the number of separate testing tools required.

Phase 1 (inventory) and full Phase 3 (runtime blocking) are not part of the core product. Mindgard is primarily a Phase 2 platform, with monitoring features that provide observability but fall short of an active runtime protection layer. Agentic surface coverage and MCP testing are more limited than ARTEMIS.

What it covers: Phase 2 (LLM and multimodal red teaming); partial runtime monitoring Strengths: Multimodal coverage across text, image, and audio; OWASP and ATLAS-mapped output; continuous testing API Limitations: No AI inventory; limited agentic and MCP coverage; runtime monitoring rather than active blocking Best for: Deployments combining text and multimodal AI; organizations needing ATLAS-mapped findings for compliance teams Pricing: Commercial, pricing on request URL: mindgard.ai

4. Lakera (acquired by Check Point)

Lakera was acquired by Check Point Software Technologies in November 2025 for $300M and is now the foundation of Check Point's Global Center of Excellence for AI Security. Before the acquisition, Lakera built runtime guardrails for LLM applications through its Guard product: real-time input/output classification covering prompt injection attempts, sensitive data exposure, and policy violations, integrated as an API layer with no changes to the underlying LLM.

Post-acquisition, Lakera's capabilities are being folded into Check Point's broader enterprise security stack. For teams already running Check Point infrastructure, the integration adds AI guardrail coverage to an existing security perimeter. For teams outside that ecosystem, the acquisition means evaluating Lakera as a Check Point product rather than a standalone AI security tool, with pricing, support, and roadmap now under Check Point governance.

The core limitation of the Guard product remains the same post-acquisition: blocking policies reflect the training distribution, not the specific vulnerabilities in the deployed application. A Lakera-guarded application that has never been red teamed is protected against known attack patterns but not against application-specific logic exploitation.

What it covers: Phase 3 (runtime prompt injection detection, output filtering, policy enforcement) Strengths: Low-latency API guardrail; clean integration path; multilingual coverage; Check Point ecosystem integration Limitations: No AI inventory; no red teaming capability; guardrails not calibrated from application-specific testing; standalone roadmap now under Check Point Acquisition note: Acquired by Check Point Software Technologies in November 2025 for $300M; now part of Check Point's AI security portfolio Best for: Teams in the Check Point ecosystem; organizations needing a production guardrail with enterprise security stack integration Pricing: Under Check Point pricing; contact Check Point URL: lakera.ai

5. Protect AI (acquired by Palo Alto Networks)

Protect AI was acquired by Palo Alto Networks in 2025. Its capabilities are being integrated into the Palo Alto Networks AI security portfolio. Before the acquisition, the platform covered ML model scanning (Guardian), LLM application security (LLM Guard), and AI supply chain risk. Open source components including LLM Guard and the nbformat scanner remain available.

Post-acquisition, the commercial product roadmap is driven by Palo Alto Networks rather than an independent product team. Buyers evaluating Protect AI today are effectively evaluating a Palo Alto Networks product: pricing, support commitments, and feature trajectory are under Palo Alto Networks governance. Teams already running Palo Alto Networks infrastructure may find the integration story compelling. Teams outside that ecosystem should verify current support commitments directly before committing.

The original platform covered Phase 2 partially (model scanning, LLM application security testing) and had no Phase 1 or full Phase 3 capability. That scope is unchanged post-acquisition; the integration path into Palo Alto Networks tooling is the primary difference.

What it covers: Phase 2 partial (model scanning, LLM application security); supply chain Strengths: Strong ML supply chain coverage; LLM Guard open source components remain available; Palo Alto Networks ecosystem integration Limitations: Post-acquisition roadmap under Palo Alto Networks; standalone trajectory uncertain; no inventory; limited agentic and runtime protection Acquisition note: Acquired by Palo Alto Networks in 2025; now part of the Palo Alto Networks AI security portfolio Best for: Teams in the Palo Alto Networks ecosystem; organizations using LLM Guard as a defense-side integration Pricing: Open source components free; commercial pricing under Palo Alto Networks URL: protectai.com

6. Robust Intelligence (acquired by Cisco, now Cisco AI Defense)

Robust Intelligence was acquired by Cisco in 2024 and integrated into Cisco AI Defense. The platform originally covered AI model validation, red teaming, and runtime firewall capabilities for LLM deployments. Post-acquisition, the functionality is available as part of Cisco's AI Defense product within the Cisco Security portfolio.

The integration into Cisco's infrastructure stack is the acquisition's primary change for enterprise buyers. Organizations already running Cisco Security products gain AI security capabilities without adding a standalone vendor. The Phase 2 and Phase 3 coverage from the original Robust Intelligence product carries over, though feature velocity and independent roadmap commitments shifted to Cisco's release cycle.

Teams evaluating this platform outside the Cisco ecosystem face the standard acquisition tradeoff: the technology is mature, but the product is no longer independently competitive or independently roadmapped. Cisco AI Defense is the correct product name for current procurement conversations.

What it covers: Phase 2 (model validation, LLM red teaming); Phase 3 partial (AI firewall, runtime policy) Strengths: Mature platform with established enterprise deployments; Cisco ecosystem integration; combined red teaming and runtime capability Limitations: No AI inventory; post-acquisition velocity dependent on Cisco roadmap; MCP and agentic coverage limited Acquisition note: Acquired by Cisco in 2024; integrated into Cisco AI Defense Best for: Organizations in the Cisco Security ecosystem looking to add AI security without adding a standalone vendor Pricing: Under Cisco AI Defense pricing; contact Cisco URL: robustintelligence.com

Coverage comparison

How to choose

You need to know what AI is running before you can secure it.

Phase 1 is not optional for organizations with more than a handful of AI deployments. Shadow AI — models and agents deployed by individual teams without security review — is a documented enterprise risk. Repello AI Inventory is the only product in this comparison purpose-built for AI asset discovery at enterprise scale. Without visibility into the full AI surface, red teaming and runtime protection are applied to a partial picture.

You need guardrails that reflect your actual attack surface, not a generic one.

The structural gap in standalone Phase 3 tools (Lakera, Robust Intelligence's firewall) is that their blocking policies are built from general attack datasets, not from testing your specific deployment. Repello ARGUS is calibrated from ARTEMIS findings against your application — which is why the false positive rate is materially lower in production. If you are deploying a guardrail without having run adversarial testing first, you are protecting against the attacks you know about, not the ones that exploit your application's specific logic.

You are in an acquired vendor's ecosystem.

Three of the six platforms in this comparison have been acquired: Protect AI by Palo Alto Networks (2025), Robust Intelligence by Cisco (2024), and Lakera by Check Point (November 2025). If your organization already runs infrastructure from any of those vendors, the integration path is a genuine advantage. Outside those ecosystems, the acquisition means evaluating a feature within a larger platform rather than a standalone AI security product with its own roadmap.

Your deployment includes agentic workflows or MCP integrations.

HiddenLayer, Lakera, and the acquired platforms have limited to no coverage of agentic AI attack surfaces and MCP protocol vectors. Mindgard covers some agentic flows. For production deployments with tool access, browser-based agents, or MCP-connected systems, only ARTEMIS and ARGUS provide full-stack agentic and MCP coverage.

Frequently asked questions

What is the difference between AI security and LLM security?

LLM security focuses on the text-based application layer: prompt injection, jailbreaking, RAG poisoning, and output manipulation. AI security is broader and includes model supply chain risk (compromised model files, backdoored fine-tunes), agentic system attacks (tool-call hijacking, MCP exploitation), AI asset governance (shadow AI, AI inventory), and runtime protection across all modalities. The AI security solutions guide covers the full scope.

Why are Protect AI, Robust Intelligence, and Lakera listed if they've been acquired?

All three remain actively deployed under their new parent companies. Protect AI was acquired by Palo Alto Networks in 2025. Robust Intelligence was acquired by Cisco in 2024 and integrated into Cisco AI Defense. Lakera was acquired by Check Point in November 2025 for $300M. Buyers evaluating AI security vendors will encounter all three in competitive processes, and understanding the acquisition context matters for long-term procurement decisions around support, roadmap continuity, and pricing.

Does runtime protection replace red teaming?

No. Runtime guardrails block known attack patterns in production; they do not find unknown vulnerabilities in your application logic. AI red teaming is the process of discovering those application-specific vulnerabilities before attackers do. The two are complementary: red teaming informs what the runtime protection needs to block. Running only one without the other leaves a material gap.

What is offensive-first AI security?

Offensive-first means the defensive configuration is derived from active attack data, not from a general threat model. In Repello's architecture, ARGUS runtime guardrails are calibrated from ARTEMIS red teaming results against the specific deployment. The attack patterns that ARTEMIS confirmed as exploitable are the ones ARGUS prioritizes blocking. Guardrails built from generic datasets protect against published attack patterns; guardrails built from first-party red teaming protect against the actual vulnerabilities in your system.

What frameworks do AI security tools map findings to?

The primary frameworks are OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF. ARTEMIS maps findings to all three. Mindgard covers OWASP and ATLAS. Open source tools typically require manual mapping. For EU AI Act compliance, NIST AI RMF alignment, or internal governance requirements, check whether findings output maps to those specific frameworks before committing to a platform.

See the full platform in action

Repello covers all three phases in a connected pipeline: AI Inventory to map what you're running, ARTEMIS to find what's exploitable, and ARGUS to block it in production with guardrails calibrated from real attack data.

Book a demo to see the platform against your stack.