AI agents are moving beyond simple question-and-answer workflows. They can access company data, call APIs, use external tools, connect to MCP servers, and take actions on behalf of users. That flexibility creates security risks that traditional application controls may not fully address.
AI agent security platforms help organizations discover, assess, monitor, and protect these systems. However, they do not all solve the same problem. Some focus on prompt injection and runtime guardrails, while others provide AI discovery, red teaming, model security, posture management, or controls over agent actions. This guide compares 10 AI agent security platforms in 2026, with a focus on their documented capabilities, enterprise use cases, and the limitations buyers should evaluate before choosing a product.
What Are AI Agent Security Platforms?
AI agent security platforms are security tools designed to protect AI applications and agents across their lifecycle. Depending on the product, they may help organizations discover AI assets, assess vulnerabilities, scan models, test applications, enforce runtime policies, monitor agent behavior, or control access to tools and data.
The difference between traditional LLM security and agent security is important. A basic chatbot mainly processes prompts and generates responses. An agent can also retrieve documents, call tools, access databases, send messages, or perform business actions. A security platform for agents therefore needs to consider not only what the model says, but also what the system is allowed to do.
For example, a prompt injection may cause an agent to ignore its intended instructions. If that agent has access to an email account, CRM, or internal database, the consequences can extend beyond an unsafe response. Security teams may need controls for the prompt, the retrieved content, the tool call, the data being accessed, and the final action.
For a broader comparison of enterprise LLM protection, see our guide to LLM security tools for enterprise AI deployments. This article focuses more specifically on platforms that address agent discovery, agent behavior, runtime controls, and the wider AI security lifecycle.
What Should an AI Agent Security Platform Protect?
The category includes several overlapping security layers. Understanding them makes it easier to compare products without assuming that every platform provides the same coverage.
| Security layer | What it addresses | Typical capabilities |
|---|---|---|
| Discovery and inventory | Unknown AI applications, agents, models, and tools | AI asset discovery, AI-BOM, shadow AI visibility |
| Posture and governance | Permissions, ownership, exposure, and policy gaps | Risk assessment, policy controls, access governance |
| Security testing | Weaknesses before deployment or during assessment | Red teaming, attack simulation, vulnerability testing |
| Runtime protection | Threats while an AI system is operating | Prompt defense, data protection, detection, blocking |
| Model and supply-chain security | Risks in models, dependencies, and AI artifacts | Model scanning, integrity checks, supply-chain assessment |
| Agent and tool security | Tool misuse, excessive autonomy, and unsafe actions | Tool-call monitoring, MCP visibility, runtime enforcement |
How We Selected These AI Security Platforms
We selected platforms based on their relevance to AI agent security, documented capabilities, enterprise use cases, security testing, runtime protection, discovery, model security, integration options, and product scope. The list includes broad AI security platforms as well as tools with a more focused role in testing or evaluation.
This is an editorial comparison, not an independent performance benchmark. Vendor capabilities, product names, pricing, and availability can change. Buyers should verify the current product scope, supported integrations, deployment requirements, and commercial terms during evaluation.
Quick Comparison of AI Agent Security Platforms
| Platform | Best for | Main capabilities | Pricing approach |
|---|---|---|---|
| Lakera | Prompt and runtime AI security | Agent discovery, risk assessment, prompt defense, guardrails | Contact sales |
| HiddenLayer | Broad AI security | Discovery, model security, attack simulation, runtime protection | Contact sales |
| Noma Security | AI estate security | AI-SPM, access control, red teaming, runtime detection | Contact sales |
| Prompt Security | Enterprise AI usage and application security | AI usage controls, application security, runtime protection | Contact sales |
| Lasso Security | AI discovery and runtime enforcement | AI-BOM, posture management, agent security, runtime protection | Contact sales |
| Mindgard | AI red teaming and security testing | AI discovery, attack simulation, assessment, runtime protection | Contact sales |
| Prisma AIRS | Broad enterprise AI security | Agent, model, posture, assessment, and runtime security | Contact sales |
| Cisco AI Defense | Enterprise AI visibility and protection | Discovery, validation, runtime protection, access controls | Contact sales |
| Patronus AI | AI evaluation and quality assurance | Grounding, relevance, policy, and output evaluation | Contact sales |
| General Analysis | AI application and agent assessment | Security assessment, agent evaluation, runtime security | Contact sales |
The 10 Best AI Agent Security Platforms in 2026
1. Lakera
Lakera is an AI security platform focused on protecting generative AI applications, agents, and enterprise AI usage. Its documented capabilities include prompt attack prevention, data leakage protection, AI red teaming, and agent security. Its current documentation also describes agent discovery, risk assessment, and runtime guardrails.
Lakera's agent security approach separates the structural state of an agent from its live behavior. This includes understanding connected tools, MCP servers, authentication, autonomy, prompts, tool calls, and tool responses. That distinction is useful for organizations that need to assess not only whether an agent is exposed, but also what it does during execution.
Best for: Organizations looking for prompt defense, runtime AI protection, and agent-focused security controls.
Pricing: Contact sales for current enterprise pricing and product availability.
Limitation: Buyers should verify which discovery, guardrail, and runtime capabilities are included in the selected product tier, especially where agent and MCP support is important.
2. HiddenLayer
HiddenLayer provides a broad AI security platform covering AI discovery, model supply-chain security, attack simulation, and runtime protection. Its platform is designed to address risks across models, AI applications, and agentic workflows rather than focusing on a single prompt-level control.
Its runtime security offering includes visibility into agent interactions, threat detection, and inline protection. HiddenLayer also describes capabilities for detecting prompt injection, sensitive data exposure, malicious tool use, and unsafe agent actions. This makes it relevant to teams that need security operations visibility alongside AI-specific controls.
Best for: Enterprise security teams that want to evaluate model, application, agent, and runtime risks within a broader AI security platform.
Pricing: Contact sales for current pricing, modules, and deployment options.
Limitation: Its broad scope means buyers should clarify which modules are included and whether the proposed deployment covers their model formats, AI endpoints, and agent workflows.
3. Noma Security
Noma Security is an AI security and governance platform built around discovering, assessing, governing, and protecting AI systems. Its documented capabilities include AI security posture management, access control, AI red teaming, and runtime detection.
Noma's platform focuses on the wider AI estate, including agents, models, MCP servers, and tools operating across cloud, SaaS, and developer environments. Its access-control approach is designed to help organizations define which agents are approved, what data they can access, and which actions they can perform.
This makes Noma relevant to enterprises where the challenge is not only securing one application, but understanding the growing number of AI systems being introduced across departments and platforms.
Best for: Security teams that need AI discovery, posture management, access controls, and protection across an expanding agent environment.
Pricing: Contact sales for current pricing and product scope.
Limitation: Organizations should verify which cloud, SaaS, endpoint, and agent integrations are supported in their environment before assuming complete AI estate visibility.
4. Prompt Security
Prompt Security focuses on enterprise AI security, including workforce AI usage, AI application security, and runtime protection. Its positioning is relevant to organizations that need to govern how employees use AI tools while also securing AI applications developed internally.
The platform's scope goes beyond prompt filtering. Enterprise teams may need to understand which AI tools employees use, what information is shared, and how AI applications behave in production. Prompt Security is worth evaluating when workforce AI governance and application protection are part of the same security program.
Its role should be distinguished from a dedicated model-scanning or AI evaluation tool. The most useful comparison depends on whether the immediate requirement is employee usage control, application security, runtime protection, or a combination of these.
Best for: Enterprises that need to manage AI usage alongside security controls for their own AI applications.
Pricing: Contact sales for current pricing and deployment details.
Limitation: Buyers should confirm the coverage of employee devices, application traffic, agent workflows, and runtime integrations separately.
5. Lasso Security
Lasso Security provides an AI security platform organized around discovery, assessment, and protection. Its documented capabilities include AI asset discovery, AI-BOM, posture management, agent security, and runtime enforcement.
Lasso's discovery layer maps agents, models, system prompts, tools, guardrails, and connected MCP servers. Its assessment capabilities focus on understanding exposure, permissions, and exploitable risks, while its protection layer is designed to block or terminate AI threats across the execution path.
This lifecycle approach is relevant to organizations that want to connect inventory and risk assessment with controls that operate during AI execution. It also gives buyers a useful framework for evaluating whether a platform supports only visibility or can enforce policies in production.
Best for: Organizations seeking AI discovery, posture management, and runtime enforcement across agents and applications.
Pricing: Contact sales for current pricing and plan details.
Limitation: Verify which traffic paths and integrations are covered by runtime enforcement, including custom applications and third-party agent platforms.
6. Mindgard
Mindgard is an AI security platform with a strong focus on automated red teaming, AI security testing, attack-surface discovery, and runtime protection. It is designed to help teams identify and address exploitable weaknesses across AI models, applications, agents, and workflows.
Mindgard's platform includes AI reconnaissance, AI red teaming, agent security testing, model scanning, risk assessment, and runtime protection. It also supports developer-oriented workflows through APIs, CI/CD integrations, and other enterprise security integrations.
This makes it particularly relevant when security testing is the immediate priority. Teams can evaluate whether the platform fits their pre-deployment testing process, continuous assessment program, or broader AI security operations.
Best for: Security and engineering teams that need AI red teaming, automated assessment, and vulnerability discovery.
Pricing: Contact sales for current pricing and product availability.
Limitation: Buyers should distinguish between the platform's testing capabilities and its runtime protection features, then confirm which integrations and remediation workflows are included.
7. Prisma AIRS
Prisma AIRS is Palo Alto Networks' AI security offering, associated with the Protect AI product path. It addresses multiple parts of the AI security lifecycle, including model security, AI red teaming, posture management, agent security, and runtime protection.
Its broad platform scope makes it relevant to enterprises that want to evaluate AI security alongside an established security infrastructure. Depending on the product modules and deployment, organizations may assess models and applications, monitor AI activity, and apply controls to AI systems operating in production.
Because product packaging and branding have changed, buyers should evaluate the current Prisma AIRS offering rather than relying on older Protect AI comparisons. Confirm which model-security, assessment, agent, and runtime features are available in the proposed purchase.
Best for: Enterprises evaluating a broad AI security platform across model, application, agent, and runtime layers.
Pricing: Contact sales for current product packaging and pricing.
Limitation: The platform covers several security functions, so buyers should clarify module boundaries, availability, integrations, and which capabilities are included in the selected offering.
8. Cisco AI Defense
Cisco AI Defense is designed to help enterprises discover, validate, and protect AI applications and models. Its platform addresses AI visibility, security validation, runtime protection, and access controls, making it relevant to organizations evaluating AI security as part of their existing enterprise security architecture.
For agent deployments, the important evaluation point is how the platform handles AI application traffic, model interactions, and runtime behavior. A platform may identify AI systems and assess their risks, but buyers should also establish where security decisions are enforced and whether those controls reach the tools and actions used by agents.
Cisco's enterprise security ecosystem may also matter to organizations that want to connect AI security events with existing infrastructure and operational workflows.
Best for: Enterprises looking to connect AI visibility, validation, and runtime protection with their security environment.
Pricing: Contact Cisco for current pricing and licensing details.
Limitation: Verify the exact enforcement architecture, supported AI environments, and integration coverage for the applications and agents in scope.
9. Patronus AI
Patronus AI focuses on evaluating AI systems rather than acting as a conventional runtime security gateway. Its platform provides evaluators for areas such as grounding, relevance, policy compliance, and other output-quality criteria.
This makes Patronus relevant to teams that need to understand whether an AI application produces reliable, relevant, and policy-compliant responses. For agent workflows, evaluation can help identify failures in outputs, tool-use results, and task completion, although evaluation should not be confused with access control or runtime enforcement.
Patronus is therefore best considered as part of an AI quality and safety program. It may complement security platforms that focus on discovery, red teaming, or blocking threats during execution.
Best for: Teams that need systematic AI evaluation, quality monitoring, and testing of model or application outputs.
Pricing: Contact sales for current pricing and available plans.
Limitation: Patronus is evaluation-focused. Buyers needing agent discovery, tool authorization, or inline runtime blocking should assess those requirements separately.
10. General Analysis
General Analysis focuses on AI security research, application and agent assessment, and runtime security. Its approach is particularly relevant to teams that want to understand how an AI application behaves under attack and what controls are needed before or during deployment.
The platform's broader security work includes AI application assessment and agent-focused security guidance. Its own AI security comparison also separates discovery, assessment, runtime protection, and model supply-chain security, which reflects the different jobs buyers may need a platform to perform.
General Analysis is worth considering when application security evaluation is a central requirement. As with other platforms in this list, the buyer should establish which capabilities are included in the current product, what integrations are supported, and what evidence the platform produces.
Best for: Teams prioritizing AI application and agent security assessment, research-driven testing, and runtime security evaluation.
Pricing: Contact sales for current pricing and product details.
Limitation: Buyers should verify the distinction between assessment services, platform capabilities, and runtime offerings when defining the scope of a purchase.
How to Choose the Right AI Agent Security Platform
Start with the security problem you can clearly describe. If your organization does not know which agents, models, or MCP servers are in use, discovery and inventory should be a priority. If you are preparing a customer-facing agent for production, application assessment, red teaming, and runtime controls may matter more.
Next, examine the agent's actual execution path. Identify the model provider, data sources, tools, APIs, MCP servers, authentication method, and actions the agent can perform. Ask whether the platform can observe and control those paths. A product that protects prompts but cannot see the agent's tool calls may not address the full risk you are evaluating.
Also compare the difference between detection and enforcement. Some platforms identify threats and generate alerts. Others can block prompts, redact sensitive data, restrict actions, or terminate unsafe execution. These are different capabilities, and the distinction should be confirmed during a technical demonstration.
Finally, evaluate deployment, integrations, reporting, and cost. Ask whether the platform supports your cloud environments, AI frameworks, model providers, and development workflow. Check what evidence it exports, how policies are managed, and which modules are included. For organizations beginning with visibility, our guide to shadow AI discovery and governance provides additional context on identifying AI tools, agents, and data flows across an enterprise.
FAQs
What are AI agent security platforms?
AI agent security platforms help organizations discover, assess, monitor, and protect AI agents and applications. Depending on the product, they may provide runtime guardrails, red teaming, AI discovery, model security, posture management, or controls for tools and agent actions.
How is AI agent security different from LLM security?
LLM security often focuses on model inputs, outputs, prompt attacks, data leakage, and model-related risks. AI agent security also considers tools, permissions, connected systems, MCP servers, execution paths, and actions taken by the agent.
Do AI security platforms include red teaming?
Some do. Platforms such as Mindgard, Lakera, Noma, and Prisma AIRS describe AI security testing or red-teaming capabilities. Other products focus more heavily on runtime protection, discovery, or evaluation. Buyers should verify the testing methods and modules included.
Can AI security platforms protect MCP-based agents?
Some platforms describe MCP discovery, monitoring, or protection capabilities, but support varies. Ask vendors whether they can inventory MCP servers, inspect tool calls and responses, enforce policies, and cover the MCP integrations used in your environment.
Do AI agent security platforms replace application access controls?
No. AI security tools can add monitoring, policy enforcement, and threat detection, but applications still need identity management, authorization, least-privilege permissions, secrets management, and controls over sensitive resources.
Final Verdict
AI agent security is a broad category, and the platforms in this list approach it from different directions. Lakera and HiddenLayer are relevant to runtime and broader AI protection. Noma and Lasso focus on AI estate visibility, posture, and controls. Prompt Security addresses enterprise AI usage and application security, while Mindgard is particularly relevant to red teaming and assessment.
Prisma AIRS and Cisco AI Defense offer broad enterprise AI security approaches. Patronus AI is more focused on evaluation and output quality, while General Analysis is relevant to AI application and agent assessment.
The right platform depends on where your security gap exists. Build your shortlist around a specific requirement, test the proposed integrations against a real agent workflow, and verify what the product can actually discover, assess, monitor, or block before making a purchase.
