11 Best Agentic AI Security Solutions for Enterprise in 2026

Agentic AI security solutions protect AI agents as they retrieve enterprise data, reason over context, invoke external tools, and take autonomous actions. Because AI agents can make decisions and execute tasks across multiple systems, they introduce attack surfaces that traditional security platforms were not designed to address.

These risks persist even as models become more capable. In the 2025 WASP benchmark, researchers tested realistic web-agent workflows and found that simple, human-written prompt injections partially succeeded in up to 86% of cases. In other words, an agent may recognize the user’s task and still act on malicious instructions hidden in the content it processes.

For CISOs, the practical question here is which platform covers their architecture and highest-priority attack paths. This guide compares 11 solutions across runtime protection, deployment flexibility, compliance coverage, and support for MCP and multi-agent systems. It also explains how these capabilities fit into a broader AI agent security strategy.

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TL;DR - Key takeaways

• Traditional security tools can’t reliably inspect the natural-language instructions and evolving internal state that drive agent behavior.
• Runtime protection and governance solve different problems. Enterprises need to identify whether their main gap is visibility, enforcement, or both.
• AI-native platforms provide deeper coverage of agent workflows than security products that add AI monitoring to existing controls.
• As a buyer, you should evaluate a platform’s deployment architecture, MCP and multi-agent support, compliance evidence, and latency under production load before choosing one.

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Top agentic AI security solutions: Quick review

Agentic AI security platforms vary significantly in the types of risks they address. Some focus on runtime protection, while others prioritize AI governance, posture management, or enterprise AI visibility.

We evaluated each platform based on its primary security focus, deployment model, support for MCP and tool-call protection, multi-agent capabilities, compliance framework coverage, analyst recognition, and customer reviews. The comparison below provides a high-level view to help security and engineering teams identify solutions that align with their AI architecture, security requirements, and attack surface.

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Why traditional security controls fail against AI agents?

Traditional security tools are built to inspect code, network traffic, identities, and known indicators of compromise. AI agents introduce a different challenge: they interpret natural language, retain context, call tools, and take actions across connected systems. A malicious instruction embedded in an email or tool response may look like ordinary data to a firewall or endpoint control, while the agent treats it as a command.

In Agent Security Bench, published at ICLR 2025, researchers evaluated attacks and defenses across 10 agent scenarios, more than 400 tools, 13 LLM backbones, and nearly 90,000 test cases. They found vulnerabilities in prompt handling, tool use, and memory retrieval, with the strongest attacks reaching an average success rate of 84.3%. Existing defenses showed limited effectiveness.

These findings show that securing the surrounding stack remains necessary, but it isn't sufficient. Organizations also need runtime controls that validate intent and enforce policy before actions execute.

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The attack surface most enterprises underestimate

Most enterprise threat models still treat the model endpoint as the primary security boundary. In agentic systems, the bigger risks tend to sit around it, including retrieved content, tool responses, memory, delegated agents, and MCP servers. The risk is especially pronounced for internal AI assistants and copilots, which often connect directly to company data and workflows.

Instead of attacking the model itself, attackers target the inputs and systems the agent relies on. The following attack paths highlight the most common ways AI agents are compromised in practice.

• Prompt injection via tool outputs: Attackers can embed malicious instructions in API responses, database results, or SaaS outputs that agents add to their context.
• Indirect prompt injection: Malicious instructions hidden in emails, documents, webpages, or support tickets can cause agents to treat poisoned inputs as instructions to transfer data or execute code.
• Memory poisoning: Attackers plant fabricated information in an agent’s memory, influencing future decisions when that memory is retrieved.
• Privilege escalation across agent chains: A compromised agent can pass a manipulated task to another agent with broader permissions, allowing an attacker to cross privilege boundaries if authority isn't revalidated.
• MCP server compromise: A compromised MCP server can manipulate tool descriptions, capture parameters, alter results, or steer agents toward unauthorized actions.
• Agent-to-agent trust abuse: A compromised agent can send malicious instructions to peer agents that trust its messages, spreading manipulated context or triggering unauthorized actions.

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AI-native vs. AI-augmented: A distinction that matters

AI-augmented platforms add AI features to existing security tools while relying on traditional controls. AI-native platforms are built for agent behavior, inspecting prompts, context, memory, and tool use to decide in real time whether actions should be allowed. Here’s an overview of how both differ:

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Best agentic AI security solutions for runtime protection

Agentic AI runtime security solutions protect AI agents while they execute tasks. The platforms below detect attacks, enforce security policies, and block unsafe or unauthorized actions in real time.

1. NeuralTrust: Best for enterprise-scale runtime protection with multilingual coverage and EU compliance

NeuralTrust is a centralized AI Agent Security platform for large enterprises that use, develop and deploy AI agents. It gives security teams a unified control layer to discover AI agents, monitor their interactions with enterprise systems, and enforce policies before unsafe actions execute.

The platform helps security teams understand how agents behave as they connect to enterprise data, business applications, external models, and other agents. It provides visibility into the relationships and dependencies that shape an agent's risk profile, eliminating the need to evaluate each model or application in isolation.

NeuralTrust supports security across the AI agent lifecycle. Teams can assess risk before deployment and maintain control as agents handle live requests and take actions across enterprise workflows. Centralized visibility helps security and engineering teams investigate incidents, validate policy enforcement, and maintain governance as AI adoption expands.

The platform is well suited to regulated enterprises that need production-grade protection while retaining control over sensitive data. Its split-plane architecture supports on-premises and private cloud deployments for organizations with strict data sovereignty and infrastructure requirements. Gartner has also recognized NeuralTrust as a Representative Vendor in its Market Guide for AI Agent Security.

Key capabilities:

• TrustLens and TrustGuard: Traces prompts, responses, tool calls, and agent actions. Searchable logs help teams investigate incidents and understand why policies were triggered.
• TrustGuard: Blocks unsafe interactions before execution, while Guardian Agents offer real-time oversight across multi-agent systems and tool-calling workflows.
• TrustGate: Centralizes access to models, tools, MCP servers, and other agents. It applies identity-aware permissions and security policies to every call.
• TrustLens: Identifies agents across the enterprise, maps their connected models, tools, data sources, and identities, and highlights risks based on configuration and observed behavior.
• Compliance support: Provides audit trails and controls that support alignment with frameworks and regulations such as the EU AI Act, GDPR, ISO 42001, and NIST AI RMF.

Deployment options: On-premise, SaaS, hybrid

User testimonial:

“With NeuralTrust, we stress-tested our chatbot with GenAI ‘SOFia,’ validating a safe go-live that meets financial-sector security and regulatory standards.” - Juan Manuel Sanchez-Quinza, Director of Transformation, ABANCA

Book a demo to see how NeuralTrust can enforce security policies and provide visibility across your production AI agents.

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2. Lasso Security: Best for securing open-source MCP server integrations

Lasso Security is an AI security platform built to secure enterprise AI agents and applications throughout their lifecycle. Organizations use it to prevent data exfiltration, eliminate Shadow AI, and secure highly vulnerable tool connections. Its capabilities allow security teams to apply intent-aware runtime policies as agents interact with sensitive corporate data repositories and internal APIs.

Key capabilities:

• AI detection and response: Monitors agent execution traces, tool calls, memory access, and sub-agent interactions to detect runtime threats and policy violations
• Inline policy enforcement: Applies contextual security policies before AI agents execute tool calls, API requests, or other actions
• Behavioral anomaly detection: Builds behavioral baselines for AI agents and users to identify intent-based attacks and suspicious activity
• Runtime investigation: Records complete execution traces to support incident response, auditing, and forensic analysis
• SIEM and AI gateway integrations: Streams alerts and audit logs into existing security and observability platforms Deployment options: SaaS

User testimonial

“They have a good focus on AI, security vault, and touch on some of the key areas for our business.” User review

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3. DeepKeep: Best for AI security across multimodal applications

DeepKeep is an AI security platform that secures AI applications and agents from development through production. Companies use it to identify risks before deployment, apply security controls throughout the AI lifecycle, and monitor AI applications and agents in production. The platform supports organizations building AI systems with multiple models, multimodal applications, and agentic workflows while maintaining visibility and control across the AI ecosystem.

Key capabilities:

• AI firewall: Applies security controls and monitors AI interactions before and after deployment
• AI red teaming: Evaluates AI applications against adversarial attacks to identify security weaknesses before production
• AI agent security: Monitors AI agent activity, assesses agent attack surfaces, and recommends controls for MCP server usage and agent behavior
• Model scanning: Scans AI models to verify provenance, identify security risks, and support compliance requirements

Deployment options: SaaS

User testimonial: No user testimonial available

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4. Prompt Security: Best for cross-enterprise GenAI governance

Prompt Security is an AI security platform that secures AI applications, AI agents, and enterprise AI usage. Its MCP Gateway extends those capabilities to agentic AI by monitoring MCP interactions, assessing MCP server risk, and enforcing security policies for autonomous AI systems. Organizations can use the platform to identify AI-specific risks before deployment while maintaining visibility and control over AI applications in production.

Key capabilities:

• MCP gateway: Monitors MCP interactions, evaluates MCP server risk, and enforces allow or block policies for AI agents
• AI red teaming: Tests homegrown AI applications against prompt injection, jailbreaks, privilege escalation, and other AI-specific threats
• AI application protection: Inspects AI requests and responses to identify prompt injection, data leakage, and unsafe model outputs
• AI risk assessment: Scores AI applications and MCP servers to prioritize security risks and remediation efforts AI usage governance: Provides visibility into enterprise AI usage while enforcing organizational security and compliance policies

Deployment options: SaaS, on-premises

User testimonial:

“Honestly, very little. If anything, the reporting dashboard could offer a few more customization options for large enterprises, but it’s still very intuitive. Deployment and integration were smooth, and the learning curve was minimal.” User review

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Best agentic AI security solutions for governance and visibility

Organizations need visibility into where AI agents operate, what they can access, and how they interact with enterprise systems to effectively manage risk. The platforms below help discover AI agents, assess risks, enforce governance policies, and support compliance across enterprise AI environments.

1. Geordie: Best for context-engineered runtime governance

Geordie is an AI agent security platform that helps organizations discover, understand, and govern AI agents across enterprise environments. It inventories AI agents, maps their configurations, and records agent behavior to identify security and compliance risks while providing controls for responsible AI adoption at scale.

Key capabilities:

• Agent discovery: Automatically discovers AI agents across cloud, code, endpoint, and SaaS environments and maintains an up-to-date inventory
• Behavioral observability: Records prompts, plans, responses, and tool invocations to provide an auditable view of AI agent activity
• Agent posture management: Maps agent configurations, permissions, MCP connections, models, and knowledge sources to identify security risks
• Risk intelligence: Evaluates AI agent risks and maps findings to frameworks including OWASP, NIST, ISO 42001, and the EU AI Act

Deployment options: SaaS

User testimonial: No user testimonial available

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2. Optro: Best for AI governance within enterprise GRC programs

Optro is an AI-powered governance, risk, and compliance (GRC) platform that brings AI governance into enterprise risk management and compliance workflows. Organizations use it to inventory AI systems, manage AI risks, automate governance processes, and monitor compliance from a single platform. It also supports AI governance alongside broader audit, cybersecurity, regulatory compliance, and third-party risk management programs.

Key capabilities:

• AI inventory management: Tracks AI models, applications, and agentic systems through centralized intake, review, and approval workflows
• AI compliance management: Maps AI systems to frameworks including the EU AI Act, ISO 42001, and NIST AI RMF to support regulatory compliance
• AI risk management: Prioritizes AI risks using risk scoring, control mappings, and mitigation recommendations
• Continuous AI monitoring: Monitors AI risks and governance controls to identify changes that require review
• Connected GRC platform: Integrates AI governance with audit, cybersecurity, compliance, and enterprise risk management workflows

Deployment options: SaaS

User testimonial: “Customization options are somewhat limited when compared to other platforms.” - User review

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3. ZScaler: Best for network-level AI traffic visibility and SASE data loss prevention

Zscaler AI Security is an AI security platform that extends the company's Zero Trust architecture to protect AI applications, AI agents, and AI services. It helps enterprises secure AI adoption while maintaining visibility, governance, and policy enforcement across AI environments.

Key capabilities:

• AI asset management: Discovers AI applications, models, MCP servers, and AI development tools across the enterprise
• AI access controls: Enforces user access, data protection, and acceptable use policies for AI applications
• AI red teaming: Tests AI applications against predefined and custom attack scenarios to identify security weaknesses
• AI runtime protection: Blocks prompt injection, data poisoning, malicious URLs, and unsafe AI outputs during production use
• AI governance: Monitors AI deployments, supports regulatory compliance, and provides reporting for AI risk management

Deployment options: SaaS

User testimonial:

“From the end user perspective, I don't think there is anything great about Zscaler. It sometimes can completely hinder your ability to do work. If you're an employer looking at this for an option, know that because of Zscaler, your team may not be able to access their work without IT support for long periods of time.” - User review

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4. Credo AI: Best for enterprise AI governance across agents, models, and applications

Credo AI is an AI governance platform that helps organizations govern AI agents, models, and applications throughout their lifecycle. It provides centralized oversight for AI discovery, risk management, policy enforcement, and regulatory compliance to support consistent governance as AI adoption scales.

Key capabilities:

• AI registry: Discovers and inventories AI agents, models, applications, and shadow AI across enterprise environments
• AI risk management: Continuously assesses AI risks through automated evaluations, drift detection, and contextual risk scoring
• AI policy management: Applies governance workflows, policy packs, and automated evidence generation for regulatory compliance
• Runtime governance: Monitors AI agent behavior, evaluates execution traces, and identifies policy violations during production use
• Regulatory compliance: Maps governance controls to frameworks including the EU AI Act, NIST AI RMF, ISO 42001, and SOC 2

Deployment options: SaaS

User testimonial: No user testimonial available

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Best agentic AI security solutions for large enterprise platforms

Large enterprise security platforms are extending their cybersecurity and Zero Trust portfolios to support AI agents and AI applications. The platforms below combine AI security with cloud, identity, network, and security operations capabilities.

1. Microsoft (Agent 365 / Security Copilot): Best for organizations invested in the Microsoft AI ecosystem

Microsoft Security Copilot is an AI-powered cybersecurity platform that integrates with Microsoft security, identity, cloud, and AI services. It helps organizations secure AI applications, Microsoft Copilot deployments, and AI agents while extending security operations across the broader Microsoft ecosystem.

Key capabilities:

• AI security operations: Assists security teams with incident investigation, threat analysis, and response using generative AI
• AI application protection: Secures Microsoft Copilot, Azure AI services, and AI applications with integrated security controls
• AI posture management: Identifies AI assets, evaluates security risks, and provides recommendations to strengthen AI environments
• AI governance: Supports AI policy enforcement, compliance monitoring, and responsible AI deployment across Microsoft environments

Deployment options: SaaS

User testimonial:

“Data privacy and security are paramount concerns. Given that Copilot processes sensitive security data, robust measures must be in place to protect it from unauthorized access or breaches.” - User review

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2. Palo Alto Networks (Prisma AIRS): Best for network-layer AI runtime firewalling and micro-perimeter isolation

Prisma AIRS is an AI security platform that secures AI agents, applications, models, and data throughout the AI lifecycle. It helps enterprises discover AI assets, assess AI-specific risks, and protect AI systems from development through production using a unified platform.

Key capabilities:

• AI agent security: Verifies AI agent identities and enforces real-time controls to prevent unauthorized actions across agent ecosystems
• AI red teaming: Simulates adversarial attacks against AI agents and applications to identify vulnerabilities before deployment
• AI runtime security: Monitors live AI interactions to prevent prompt injection, data exposure, and unsafe agent behavior
• AI model security: Scans third-party AI models for tampering, malicious code, and supply chain vulnerabilities
• AI posture management: Discovers AI assets and evaluates the security posture of AI models, agents, applications, and training data

Deployment options: SaaS

User testimonial:

“Complexity, especially during initial deployment and tuning. It often requires skilled resources to configure properly, and the learning curve can be steep for new users. Additionally, the cost can be high compared to other solutions, which may not be ideal for smaller organizations.” - User review

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3. Radware: Best for perimeter-layer agent threat mitigation and automated API edge protection

Radware Agentic AI Protection is an AI agent security platform that helps organizations discover, monitor, and protect AI agents across enterprise environments. It combines AI security posture management with intent-aware behavioral analysis to identify risks, enforce guardrails, and secure agent activity throughout the AI lifecycle.

Key capabilities:

• Agent discovery and mapping: Discovers AI agents, tools, and dependencies while mapping relationships across the agent ecosystem
• Intent-aware runtime protection: Monitors agent behavior and blocks prompt injection, jailbreaks, supply chain attacks, and other malicious activities in real time
• Agent behavior analytics: Tracks agent usage, detects behavioral anomalies, and identifies changes in performance and activity
• MCP tool access control: Allows or blocks tool access for individual agents based on security policies

Deployment options: SaaS

User testimonial:

“In general terms, the solution is very good and effective. I suggest improvements in support for customers and, in particular, the ease with which they can request it and the solution time.” - User review

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Agentic AI Security Solutions: Market Map

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Key features to look for in an agentic AI security solution

Agentic AI security platforms differ significantly in their capabilities and deployment models. Choosing the right platform depends on your AI architecture, security requirements, and operational constraints.

The following sections outline the key capabilities to evaluate when comparing agentic AI security platforms.

1. Deployment model: SaaS, on-premises, or split-plane

Deployment architecture determines where prompts, retrieved data, tool parameters, and model responses are inspected. SaaS deployments are often easier to adopt, but they may require sensitive AI traffic or telemetry to leave your environment, which may not be acceptable in regulated industries with strict data residency or sovereignty requirements.

It's also important to distinguish between a private management plane and a true private data plane because full on-premises support is uncommon. Split-plane architectures offer a middle ground by centralizing policy management while keeping sensitive processing inside the customer's environment.

2. Runtime protection vs. governance coverage

Governance platforms help organizations discover AI agents, assign ownership, assess risk, and verify compliance with internal policies. On the other hand, runtime protection inspects agent activity in real time, blocking unsafe prompts, memory changes, tool calls, or data disclosures before execution.

Most enterprises need both, but the priority depends on whether your main gap is visibility or enforcement. A governance platform can’t stop an authorized agent from taking an unsafe action unless it also intervenes at runtime.

3. Multi-agent and MCP support

Multi-agent systems create trust relationships as agents delegate tasks, exchange context, and invoke tools with different identities and permission levels. Security controls must preserve provenance and ensure downstream actions remain within the original user's authority.

MCP expands the attack surface by standardizing how agents discover and access external tools. A compromised MCP server can manipulate tool descriptions, alter responses, or expose agents to additional connected systems.

Evaluate whether the platform inspects tool selection, parameters, and responses, protects MCP servers and gateways, enforces trust policies between agents, and maintains visibility into how actions propagate across connected systems. Prompt filtering alone is insufficient.

4. Compliance framework alignment

Regulated companies should verify support for the EU AI Act, NIST AI RMF, ISO/IEC 42001, SOC 2, and GDPR.

A framework logo on a product page isn’t enough. Look for policy mapping, audit trails, retention controls, evidence collection, access logs, and reporting that support internal reviews or external audits. Confirm which certifications the vendor actually holds and which frameworks its features merely help customers comply with.

Deployment architecture is just as important as compliance features. Even strong security controls cannot satisfy regulations if sensitive AI data is processed or transferred in ways that violate data residency or sovereignty requirements.

5. Latency and production-grade performance

Excessive latency can slow user-facing applications and disrupt multi-step workflows. Offline scans and async log analysis can’t stop an unsafe action before it occurs. Runtime platforms must take enforcement decisions while the agent processes a request or prepares a tool call.

That’s precisely why you should ask vendors for detection latency under production load instead of isolated test results. Sub-100 ms is the target benchmark for comprehensive, inline AI guardrails. Security teams must also closely examine throughput, tail latency, failure behavior, and performance consistency across multilingual traffic and complex agent workflows.

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Choose the right agentic AI security solution for your enterprise

The right platform depends on where your current security gaps lie. Governance platforms provide visibility, but production AI agents also require runtime controls to prevent unsafe actions.

Verify where sensitive data is processed, whether the data plane can remain on-premises or in a private cloud, and how the platform supports your compliance obligations. Before choosing a platform, you should also confirm that multi-agent and MCP protection extend beyond basic prompt filtering to cover delegated actions and tool access.

If you’re an enterprise company, especially in a regulated industry, that needs to secure AI agents in production without moving sensitive data outside your environment, NeuralTrust is a great choice.

The platform combines runtime enforcement with centralized observability, and its split-plane architecture keeps sensitive processing within the customer’s infrastructure. Additionally, Guardian Agents extend those controls across multi-agent environments, making the platform suited to regulated organizations that need protection without giving up data sovereignty.

Request a demo to see how NeuralTrust can secure and monitor your AI agents in production.

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FAQs about Agentic AI Security Solutions

1. What is agentic AI security, and how is it different from traditional AI security?

Agentic AI security protects systems that can reason, use tools, access data, retain memory, and take actions with limited human input. Traditional AI security often focuses on model risks such as prompt attacks, data leakage, or unsafe outputs. Agentic AI security extends that coverage to the full execution chain, including tool calls, memory changes, delegated tasks, MCP connections, and interactions between agents.

2. What are the most common threats to enterprise AI agents?

Common threats include indirect prompt injection, malicious tool outputs, memory poisoning, privilege escalation across agent chains, compromised MCP servers, and agent-to-agent trust abuse. These attacks often exploit information or components the agent already trusts, allowing attackers to influence decisions or trigger unauthorized actions without compromising the underlying model.

3. What is the difference between an AI gateway and an AI security platform?

An AI gateway provides a centralized control point between enterprise applications and AI models. It can route requests, enforce access policies, apply guardrails, and log interactions.

On the other hand, an AI security platform offers broader coverage across the agent lifecycle, including discovery, observability, runtime enforcement, risk assessment, memory protection, tool-call monitoring, and multi-agent security. Some platforms include an AI gateway as part of a wider security architecture.

4. Do agentic AI security solutions support multi-agent environments?

Some do, but support varies significantly. Basic platforms may only inspect prompts and responses from individual agents. More advanced solutions track task delegation, shared context, tool use, and trust relationships across multiple agents. Before choosing a platform, you should verify that it preserves provenance and enforces permissions between agents. It should also block unsafe actions before they spread through the workflow.

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About the Author

Roger Howroyd is Head of Global SEO and AI at NeuralTrust, where he leads the company's search strategy across SEO, AEO, GEO, and LLM optimization. He specializes in AI-powered search, content strategy, backlink development, and SEM. Connect on LinkedIn

NeuralTrust is an AI agent security platform, recognized in the Gartner 2025 Market Guide for AI Gateways and Guardian Agents, and the KuppingerCole 2025 Leadership Compass for Generative AI Defense. Headquartered in Barcelona with ISO 27001 certification.