Key Takeaways
- AI agents create new risks because they can reason, retrieve data, use tools, call APIs, and take actions across enterprise systems.
- Dash Security leads this list because it provides an agent-centric security and control plane for discovery, governance, posture management, runtime enforcement, AIDR, AI DLP, and spend visibility.
- Enterprises should evaluate whether a tool protects agents at build time, deployment time, runtime, or across the full lifecycle.
- AI agent security should support productivity, not block adoption. The goal is governed agent use, not blanket restriction.
Traditional security tools were built around users, endpoints, applications, networks, workloads, identities, and data. AI agents touch all of those layers, but they do it in a way that is harder to predict. An agent may start with a user request, retrieve context from multiple sources, reason through a plan, call a tool, receive a response, adjust its next step, and continue acting across systems. Each turn creates a new opportunity for misuse, drift, data exposure, or unauthorized action.
An agent may follow hidden instructions inside an email, document, ticket, repository, webpage, or retrieved record. It may expose sensitive data in a response. It may call a tool outside the user’s intent. It may use excessive permissions. It may interact with a compromised plugin. It may move data between systems in a way that violates policy. It may create code, tickets, queries, or workflows that introduce new risk.
This is why AI agent security has become its own category.
Best AI Agent Security Tools in 2026
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Dash Security
Dash Security is the best AI agent security tool in 2026 because it is built specifically as a security and control plane for the agentic enterprise. It addresses the core problem enterprises now face: agents are spreading across research, development, and business functions, but most organizations do not have a unified way to discover them, govern them, harden them, monitor them, and enforce controls during live activity.
Dash’s approach is agent-centric from the start. The platform is designed to discover known and shadow agents across workstations and managed cloud platforms. That matters because many organizations are already using agents before security teams have a complete inventory. Agents may appear in coding environments, browsers, desktop assistants, cloud services, enterprise AI platforms, and autonomous workflows. Without discovery, there is no governance.
Dash goes beyond agent discovery by mapping the broader agentic ecosystem. That includes models, MCP servers, skills, plugins, extensions, tools, identities, flows, connected systems, applications, and data. This is important because agent risk often comes from the supply chain around the agent. A single agent may be safe in isolation but risky when connected to an over-permissioned tool, an untrusted MCP server, a sensitive data source, or a poorly governed plugin.
Key Capabilities
- AI agent discovery
- Shadow AI visibility
- Agentic estate governance
- AI Security Posture Management
- AI Detection and Response
- Runtime agent enforcement
- AI DLP
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Check Point AI Security
Check Point AI Security is an option for enterprises and builders that need AI guardrails, prompt injection protection, data leakage prevention, content controls, and agent behavior defense. It is especially useful when teams want runtime defenses embedded directly into AI applications and agents.
Lakera Guard became well known for prompt injection and LLM security defenses. In the agentic era, that protection has expanded into a broader AI security model. Check Point AI Security positions its agent security around protecting AI applications and agents across prompts, model outputs, tool calls, tool responses, and tool descriptions.
Key Capabilities
- Data leakage prevention
- Content moderation
- Agent behavior defense
- Tool call inspection
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Palo Alto Networks Prisma AIRS
Palo Alto Networks Prisma AIRS is a strong AI security platform for enterprises that want runtime security for AI applications and agents inside a broader security ecosystem. Prisma AIRS AI Runtime Security monitors prompts, responses, and data flows to detect and stop AI-specific threats in real time.
Prisma AIRS is especially relevant for large enterprises that already use Palo Alto Networks products or want AI security connected to a broader cloud, network, and application security strategy. AI agents do not operate in isolation. They interact with applications, infrastructure, data, users, APIs, and runtime environments. A large security platform can help connect these layers.
Key Capabilities
- Data flow inspection
- Policy enforcement
- Threat prevention for AI interactions
- Prompt injection detection
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Pillar Security
Pillar Security is a strong AI agent security platform for enterprises that need to discover, test, secure, and govern AI agents across the full AI software lifecycle. Its positioning around the agentic workforce makes it highly relevant for organizations that are deploying agents across internal and external workflows.
Pillar’s value comes from connecting discovery, testing, and protection. That is important because agent security cannot begin at runtime alone. A risky agent may already have excessive permissions, unsafe tool access, weak guardrails, exposed prompts, vulnerable configurations, or insecure integrations before a user ever interacts with it.
Key Capabilities
- MCP server and tool cataloging
- AI lifecycle security
- Agent testing
- Multi-turn attack testing
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Lasso Security
Lasso Security is a strong AI security platform for enterprises that need to secure AI agents and AI applications from discovery through runtime. It is positioned around securing any agent and AI application, with capabilities that include discovery, risk assessment, runtime protection, AI detection and response, and red teaming.
Lasso is especially useful for organizations that need broad visibility across AI adoption. Many enterprises now have AI applications running in cloud platforms, third-party services, copilots, internal agents, and product workflows. Some are sanctioned. Others are adopted by teams without central security review. Lasso helps discover and assess these assets so security teams can understand their exposure.
Key Capabilities
- Runtime protection
- AI Detection and Response
- Agent execution trace visibility
- AI firewall and gateway options
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Prompt Security
Prompt Security is a strong AI agent security tool for enterprises that want AI red teaming, discovery, remediation, and runtime protection connected in one workflow. It is especially relevant for teams that are worried about prompt injection, data exposure, unsafe agent behavior, and uncontrolled generative AI adoption.
Prompt Security’s AI red teaming capabilities can help organizations identify critical risks before deployment. This is important because AI agent weaknesses often emerge from specific workflows. A customer support agent, code assistant, HR assistant, or data analysis agent may each have different tools, permissions, prompts, and risk boundaries. Automated red teaming helps teams understand where those workflows may fail.
Key Capabilities
- Unsafe agent behavior detection
- GenAI discovery
- Remediation workflows
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Zenity
Zenity is a strong AI agent security platform for enterprises that need to govern AI agents, copilots, and low-code or no-code AI workflows across business environments. It is especially relevant for organizations adopting tools such as ChatGPT Enterprise, Copilot Studio, Salesforce Agentforce, ServiceNow, Power Platform, AWS Bedrock, Azure AI Foundry, and Google Vertex AI.
Zenity’s strength is enterprise visibility across AI agent usage and business-driven automation. Many agentic workflows are not built only by central engineering teams. They may be created by business users, operations teams, automation teams, citizen developers, or departments using low-code environments. These agents can still access sensitive systems and data.
Key Capabilities
- AI agent governance
- Enterprise AI platform visibility
- ChatGPT Enterprise security support
- Copilot Studio security support
- Salesforce Agentforce security support
- Low-code and no-code AI security
- Runtime anomaly detection
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Aembit
Aembit is a strong AI agent security tool for enterprises that need to solve the identity and access problem behind AI agents. While many tools focus on prompts and model behavior, Aembit focuses on workload identity and access management for non-human identities, including AI agents.
This is a critical layer because agents need access. An AI agent may call APIs, query databases, interact with SaaS platforms, access internal services, run tools, or act on behalf of a user. If that access is managed through static secrets, shared tokens, unmanaged service accounts, or broad permissions, the enterprise creates serious risk.
Key Capabilities
- Blended human and agent identity
- MCP authorization support
- Short-lived credential access
- Non-human identity governance
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HiddenLayer
HiddenLayer is a strong AI security platform for enterprises that need to protect AI models, AI applications, and AI-enabled workflows from prompt attacks, model extraction, data leakage, and unauthorized tool usage. It is especially relevant for organizations that view AI agent security as part of a broader AI asset protection strategy.
HiddenLayer’s strength is breadth across AI security. The platform includes capabilities for runtime monitoring, AI supply chain security, model scanning, and AI asset protection. For enterprises building agents on top of internal models, hosted models, or third-party AI services, this broader view can be valuable.
Key Capabilities
- Input and output monitoring
- Prompt attack prevention
- Model extraction protection
What to Look for in an AI Agent Security Tool
Enterprise buyers should evaluate AI agent security tools based on how they handle the full agentic lifecycle.
Agent Discovery
The tool should identify sanctioned and unsanctioned agents across workstations, cloud environments, SaaS platforms, developer tools, browsers, IDEs, CLIs, and enterprise AI platforms.
Agentic Asset Coverage
The tool should map not only agents, but also the surrounding ecosystem: MCP servers, plugins, skills, models, extensions, tools, identities, data stores, workflows, and connected applications.
Policy and Governance
Security teams need built-in and custom policies that control what agents can access, which tools they can use, what actions they can take, and which scenarios require human approval.
Runtime Protection
Agent security cannot stop at posture. The tool should monitor live sessions and detect prompt injection, intent drift, unsafe commands, tool misuse, sensitive data exposure, unauthorized actions, and anomalous behavior.
Enforcement
The strongest tools can take action at the point of risk. This may include blocking, terminating, requiring approval, restricting tool use, enforcing guardrails, or remediating exposed assets.
Session Traceability
Security teams need to reconstruct what happened. A strong tool should provide session-level context, including user intent, agent behavior, tool calls, responses, data movement, and decision points.
Identity and Access Control
Agents need scoped access. The tool should help distinguish human actions, agent actions, workload identities, and delegated authority.
Integration With Existing Security Workflows
AI agent security should connect to identity providers, EDR, SIEM, SOAR, cloud security, DLP, ticketing, developer workflows, and governance processes.
FAQs
What is an AI agent security tool?
An AI agent security tool helps organizations discover, govern, monitor, and control AI agents. It may detect prompt injection, unsafe tool use, data leakage, intent drift, excessive access, shadow AI, risky MCP servers, agent misconfigurations, and suspicious runtime behavior. The goal is to let enterprises adopt agents safely.
How is AI agent security different from LLM security?
LLM security focuses mainly on model inputs, outputs, and behavior. AI agent security is broader because agents can use tools, access systems, retrieve data, call APIs, execute workflows, and act on behalf of users. This requires controls around identity, permissions, runtime behavior, tool use, data access, and session intent.
What risks do AI agents create?
AI agents can create risks such as prompt injection, indirect prompt injection, data leakage, unauthorized tool use, excessive permissions, unsafe actions, shadow AI adoption, risky plugins, MCP exposure, credential misuse, and unclear accountability. These risks increase when agents are connected to sensitive enterprise systems.
Which AI agent security tool is best for enterprise-wide governance?
Dash Security is the strongest option on this list for enterprise-wide governance because it is designed as a control plane for the agentic enterprise. It combines discovery, governance, AI-SPM, AIDR, AI DLP, runtime enforcement, session visibility, and usage analytics across agents and enterprise AI platforms.
