AI agents are becoming active participants in enterprise operations. They write and review code, retrieve information, analyze documents, call APIs, query databases, update records, trigger workflows, and communicate with other agents.
Unlike traditional software, an agent can interpret an objective, create a plan, select tools, adjust its behavior, and continue acting without a person approving every step.
This list focuses on platforms that offer meaningful protection for AI agents, agentic applications, AI coding tools, MCP-connected environments, or autonomous enterprise workflows.
The evaluation considered the following areas:
No single platform is ideal for every architecture. The following selections represent different approaches to building a secure agentic AI program.
Dash Security is the security and control plane for the agentic enterprise. It is designed to help organizations discover, govern, assess, monitor, and enforce controls across AI agents and the supporting ecosystem that influences their behavior.
Dash takes a wide approach. It does not stop at individual agents or model interactions. Its discovery capabilities identify agent platforms, MCP servers, skills, plugins, models, tools, identities, applications, connected systems, and data.
This provides security teams with an Agentic FootPrint of both approved and shadow AI activity across workstations, cloud environments, and enterprise networks.
The platform brings together AI Discovery, AI Governance, AI Security Posture Management, AI Detection and Response, AI DLP, and AI Spend.
This allows teams to move from inventory to policy, risk reduction, runtime protection, and operational measurement without treating each function as a separate project.
Dash also creates profiles that connect an agent with its purpose, capabilities, users, permissions, behavior history, and session trajectory.
Its intent-aware detection evaluates the user’s intent, the agent’s attempted actions, and whether the session remained aligned with its original objective. This helps distinguish malicious or unintended behavior from technically similar legitimate actions.
Dash reports discovery coverage for more than 60 unique platforms and runtime protection for more than 20 coding agents.
Its coverage includes coding tools in IDEs and command-line environments, desktop and browser-based assistants, enterprise AI platforms, and autonomous agents operating in cloud and network environments.
Zenity provides security and governance for AI agents across SaaS platforms, custom cloud environments, and end-user devices. Its platform is built around the idea that agents should be secured at the point where context, access, user intent, and action come together.
This coverage is important because enterprise agents are no longer created only by centralized development teams. Employees and business units can build agents through low-code tools, automation platforms, SaaS applications, and enterprise copilots.
These agents may access customer information, internal documents, business workflows, and operational systems even when the people creating them are not professional developers.
Palo Alto Networks Prisma AIRS is a comprehensive AI security platform covering agents, applications, models, data, development environments, and production interactions.
It is designed for organizations that want AI security connected to a broader cloud, network, application, and security operations strategy.
Prisma AIRS includes AI Agent Security, AI Runtime Security, AI Model Security, AI Red Teaming, and AI Security Posture Management. This creates lifecycle coverage that begins before deployment and continues while agents operate in production.
The agent security capabilities assess agent risks, verify agent identities, evaluate permissions, and enforce controls during execution.
Organizations can examine the agent supply chain and source code, test agent behavior, monitor deployed agents, and maintain audit trails of actions and interactions.
Check Point AI Security provides discovery, governance, data protection, runtime defense, and security testing across enterprise AI usage.
Its AI Defense Plane is designed to cover three closely related areas: employees using generative AI, organizations building AI applications, and autonomous agents interacting with enterprise systems.
This unified approach addresses a common challenge. Workforce AI, customer-facing applications, and agents are often managed through different teams and controls, but they can expose the same sensitive information and connect to the same business systems.
A single data policy may need to follow an employee from a browser-based AI tool into a custom application or agentic workflow.
Lasso Security provides an AI security platform that connects discovery, AI risk management, automated red teaming, and runtime protection in a continuous lifecycle.
Its goal is to ensure that AI applications and agents behave within their intended scope before and after deployment.
The platform begins with AI discovery and inventory. This helps organizations identify agents, models, applications, cloud services, coding assistants, and other AI resources across their environments.
Discovery feeds into AI Security Posture Management, where teams can evaluate configurations, integrations, permissions, and exposures.
HiddenLayer provides an AI security platform covering models, AI applications, coding agents, agentic workflows, and production runtime environments. Its scope makes it relevant for organizations that view agent security as part of a wider program for protecting AI development and deployment.
The platform’s AI Runtime Security capabilities monitor AI systems in production for attacks, anomalies, model manipulation, sensitive-data exposure, and unsafe behavior.
For agentic systems, HiddenLayer provides visibility into how agents interact with tools, data, and workflows.
Pillar Security provides a unified platform for discovering, testing, governing, and protecting AI systems. Its approach is particularly relevant to development, application security, DevSecOps, and AI engineering teams that need controls across the lifecycle of agents they build or deploy.
The platform connects AI discovery and posture management with red teaming, attack-surface analysis, policy enforcement, and runtime protection.
Business context is used to link findings across these stages, helping teams understand not only that an exposure exists but also which agent, workflow, data source, and business process may be affected.
Selecting a platform begins with understanding the organization’s actual agentic environment. A product that works well for SaaS-native business agents may not provide the same depth for local coding agents, cloud-hosted autonomous systems, or custom multi-agent applications.
Create an inventory covering:
The platform should cover the environments that create the greatest risk, not only the easiest assets to discover.
An agent is only one element of the system. Security teams should map the models, system prompts, memory, skills, plugins, tools, MCP servers, credentials, identities, applications, data sources, and external services surrounding it.
A safe model can still participate in an unsafe workflow. A correctly configured agent can become dangerous when connected to an overprivileged tool or compromised source.
Prompt and response inspection can protect customer-facing chatbots and generative AI applications. It does not necessarily reveal every action taken by an autonomous agent.
Determine whether the platform analyzes:
This distinction separates basic AI filtering from comprehensive agentic runtime security.
Alerts alone may be insufficient when an agent can act at machine speed. The platform should intervene before a high-impact action is completed.
Possible responses include:
Graduated enforcement is often more useful than a universal block. Low-risk activity can continue, while uncertain or high-impact actions receive stronger controls.
A platform may generate many findings without explaining them clearly. During an evaluation, give security analysts realistic scenarios and determine whether they can answer:
Clear context reduces investigation time and helps teams create more precise policies.
Agentic security should integrate with the organization’s identity, endpoint, cloud, DLP, SIEM, SOAR, ticketing, and developer systems.
The deployment model should also match the environment. Some organizations prefer API connections and existing management tools. Others need endpoint-level visibility, application integrations, gateways, runtime libraries, or network controls.
A pilot should test real enterprise workflows rather than isolated demonstration prompts. The evaluation should include sensitive data, multiple tools, indirect instructions, changing permissions, and multi-step agent behavior.
LLM security primarily protects the model and its direct interactions. It may inspect prompts, responses, model files, training data, and application interfaces. Agentic AI security covers the wider system in which a model operates.
An agent can retrieve information, use memory, call APIs, access tools, communicate with other agents, and perform actions. Securing those capabilities requires controls around identity, permissions, tool use, data movement, runtime behavior, and session intent.
AI Detection and Response, commonly called AIDR, is the continuous monitoring of AI applications and agents for risky, malicious, or unintended behavior.
AIDR can include prompt-injection detection, tool-use monitoring, session analysis, intent-drift detection, data protection, behavioral analytics, policy enforcement, and automated response.
It is similar in principle to detection and response technologies used for endpoints or cloud environments, but it requires AI-native context about prompts, models, agents, tools, and autonomous actions.
Of the 7 platforms we evaluated, Dash Security is the strongest overall choice for enterprises seeking a unified security and control plane across the full agentic workforce.
It combines agent and shadow AI discovery, governance, AI Security Posture Management, AI Detection and Response, AI DLP, spend visibility, intent-aware detection, complete session traceability, and runtime enforcement.
It also covers the supporting agentic supply chain, including MCP servers, skills, plugins, models, tools, identities, applications, and connected data.
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