Palo Alto Networks Cortex AgentiX is best for enterprises that want a full SOC transformation platform built around agentic AI. Silverfort AI Agent Security is best for teams that need identity-first control over AI agents across cloud and SaaS. Zenity AIDR is best for organizations securing a mix of SaaS-managed, device-based, and home-grown AI applications.
AI agents are no longer a future problem. They are running in your environment right now, calling APIs, reading sensitive data, and taking actions on behalf of users. Most of them were deployed without a security review. Many of them have more privilege than any human account you would ever approve.
The attack surface is new and the tooling is catching up fast. Prompt injection, MCP server abuse, privilege escalation through agent chains, shadow AI deployments that nobody in IT knows about: these are real incidents happening at real organizations. The MITRE ATLAS framework already catalogs dozens of adversarial techniques targeting ML systems and AI agents. Your existing EDR and SIEM were not built to catch them.
This roundup covers seven tools purpose-built for agentic AI security in 2026. They range from full SOC platforms with AI agent orchestration to narrow, focused products that do one thing well, like MCP gateway enforcement or runtime agent behavior monitoring. Not every tool belongs in every stack. Read the trade-offs carefully before you buy.
See All Agentic AI Security Vendors.
The full Agentic AI Security market mapped by company-size fit, deployment type, NIST coverage, and pricing. No analyst paywall.
Best for: Enterprises consolidating SOC operations onto one AI platform
Cortex AgentiX is Palo Alto's answer to a question most large SOC teams are already asking: can AI agents replace the repetitive tier-1 and tier-2 analyst work that burns out staff and slows response times? The platform lets you build, deploy, and govern a workforce of AI agents that operate across the full Cortex product suite, from XSIAM for alert triage to XSOAR for playbook execution to Xpanse for attack surface management. The unifying layer is the Cortex Extended Data Lake, which normalizes telemetry from endpoints, network, cloud, identity, and code-to-cloud pipelines into a single data foundation those agents can reason over.
What separates AgentiX from generic AI orchestration platforms is that it is not a blank canvas. The agents are pre-wired into Cortex's existing detection and response capabilities. You are not building AI agents from scratch and hoping they can reach your security data. The data lake and the agent runtime are the same product. That tight coupling is a genuine advantage if you are already a Cortex shop, and a significant switching cost if you are not.
The governance layer matters here. Running AI agents in a SOC without audit trails and kill switches is a liability. AgentiX includes controls for managing the agent workforce, which is more than most competitors offer at this stage. NIST coverage spans asset management, risk assessment, continuous monitoring, adverse event analysis, and incident management, meaning it maps reasonably well to a mature security program's control requirements.
The honest trade-off: this platform is built for mid-market and enterprise organizations that are already invested in the Cortex ecosystem. If you are running a mixed-vendor environment with Splunk, SentinelOne, and a handful of point tools, the value proposition weakens considerably. The data lake is powerful, but it assumes you are feeding it from Palo Alto sources first. Third-party ingestion exists, but the agent capabilities are optimized for first-party data.
CrowdStrike Falcon AI Detection and Response
Best for: Enterprises already on Falcon protecting their own AI deployments
CrowdStrike Falcon AIDR addresses a specific and growing problem: your organization is deploying AI models and agents, and those AI systems are themselves becoming targets. Adversaries are probing ML models for extraction attacks, poisoning training data, injecting malicious prompts, and attempting to hijack autonomous agents mid-execution. Falcon AIDR is built to detect and respond to those threats across AI models, agents, training data, and prompt interactions.
The key architectural decision here is that AIDR runs inside the existing Falcon platform. Same agent, same console, same threat intelligence pipeline. If you are already running Falcon for endpoint protection and identity threat detection, adding AIDR does not require a new deployment footprint. That is a real operational advantage. Security teams are not going to adopt a tool that requires a separate agent install on every system where AI workloads run.
The NIST coverage is narrower than some competitors: risk assessment, continuous monitoring, adverse event analysis, and incident analysis. There is no explicit asset management or access control coverage in the product data, which means Falcon AIDR is positioned more as a detection and response layer than a posture management or governance tool. If you need to discover shadow AI deployments or enforce least privilege on agent identities, you will need to pair this with something else.
Falcon AIDR is the right call if you are a CrowdStrike-first shop that is starting to deploy AI workloads and wants to extend your existing security coverage to those systems without adding a new vendor. It is not the right call if your primary problem is AI agent governance, MCP security, or discovering what AI agents are running in your environment. For those problems, look at Silverfort, Zenity, or Straiker.
Silverfort AI Agent Security
Best for: Teams needing identity-layer control over AI agents across SaaS
Silverfort AI Agent Security approaches the agentic AI problem from the identity plane, which is exactly where most AI agent risk lives. AI agents authenticate to systems, hold credentials, accumulate permissions, and take actions that look like legitimate service account activity. Traditional PAM and IAM tools were not built to track non-human identities that spawn dynamically and operate autonomously. Silverfort was.
The discovery mechanism is read-only API connections to identity providers, cloud platforms, and SaaS applications. No agent install, no code changes. That matters for adoption. Security teams can get visibility into their AI agent population without touching development pipelines or negotiating with platform teams. The visual storyline graphs that map each agent to its provisioning identity and human owner are genuinely useful for accountability, especially when an agent takes an action that triggers an incident and you need to know who owns it.
The MCP gateway is the most technically interesting piece. It sits in the path of agent calls, evaluates authorization scopes before execution, and can block privilege escalation or lateral movement attempts in real time. SSO enforcement through identity providers means agent sessions are correlated to human entities, which closes a significant audit gap. Human-in-the-loop approval workflows for sensitive actions are a practical control for organizations that are not ready to let agents operate fully autonomously.
Silverfort fits SMB through enterprise, which is broader than most tools in this category. The trade-off is that the platform is identity-focused by design. It does not do runtime behavioral monitoring at the OS level, it does not catalog MCP server configurations, and it does not provide the kind of deep AI-SPM posture management that Straiker or Oligo offer. If your primary concern is who AI agents are and what they are allowed to do, Silverfort is the right tool. If your primary concern is what they are actually doing at runtime, you need something else alongside it.
Zenity AI Detection and Response (AIDR)
Best for: Enterprises securing mixed SaaS, device, and custom AI deployments
Zenity's AI Detection and Response platform, branded AIDR, is one of the few tools in this space that explicitly addresses the full spectrum of enterprise AI deployment types: SaaS-managed agents like Microsoft Copilot and Salesforce Agentforce, device-based agents, and home-grown AI applications built on platforms like Amazon Bedrock or Google Vertex AI. Most competitors focus on one deployment type. Zenity covers all three, which reflects the messy reality of how AI is actually spreading through large organizations.
The integration list is telling. Amazon Bedrock AgentCore, ChatGPT Enterprise, Microsoft Copilot Studio, Microsoft 365 Copilot, Salesforce Agentforce, ServiceNow, Power Platform. These are the platforms where shadow AI is already running in most enterprises, often deployed by business units without security review. Zenity's observability layer gives security teams visibility into what those agents are doing, what data they are touching, and where they are leaking information.
The three-pillar structure of observability, AI-SPM, and detection and response maps well to a mature security program's needs. NIST coverage includes asset management, risk assessment, data security, platform security, continuous monitoring, and adverse event analysis. The data security coverage (PR.DS) is notable and reflects the platform's focus on data leakage from AI systems, which is one of the most common and damaging AI security incidents organizations are actually experiencing.
The trade-off is that Zenity AIDR is a mid-market to enterprise product, and the breadth of coverage comes with complexity. If you are a small team trying to secure a single AI application, this is more platform than you need. But if you are a security architect at a large organization trying to get your arms around dozens of AI deployments across multiple business units and cloud platforms, Zenity's coverage breadth is exactly what you need.
Looking for Agentic AI Security Alternatives? Start with the Right Shortlist.
Compare drop-in replacements for popular Agentic AI Security tools, ranked by feature overlap, integrations, and customer fit.
Best for: Security teams building first inventory of enterprise AI agents
Straiker Discover AI does one thing and does it well: it finds every AI agent running in your environment and tells you what it is connected to. That sounds simple. In practice, most enterprise security teams have no idea how many AI agents are operating across their organization, which MCP servers those agents are calling, or what APIs and data sources they have access to. Discover AI builds that inventory automatically and keeps it current in real time.
The MCP server and Claude Skills discovery capability is worth calling out specifically. MCP is the protocol that lets AI agents call external tools and data sources, and it is moving fast. New MCP servers are being published and adopted by development teams faster than security teams can review them. Straiker maps which MCP servers are connected to which agents, surfaces risky permissions and unsafe integrations, and identifies attack paths through the agentic ecosystem before an adversary finds them first.
The Agent-SPM capability continuously evaluates security configuration across agents and their integrations. This is posture management applied to the AI layer, analogous to what CSPM does for cloud infrastructure. Misconfigurations, excessive permissions, and unauthorized access paths are surfaced before they become incidents. The shadow AI detection catches agents that were deployed without security review, which in most organizations is the majority of them.
The integration list reflects where agentic AI is actually being built: Amazon Bedrock AgentCore, Azure AI Foundry, Microsoft Copilot Studio, Cursor, Claude Code, GitHub Copilot. These are the developer tools and platforms where AI agents originate. Straiker's positioning as a discovery and posture management layer means it is designed to be paired with a detection and response tool rather than replace one. If you are starting from zero visibility into your AI agent population, Discover AI is the right first step.
Oligo Security Oligo Runtime AI
Best for: Dev-security teams protecting AI apps at the application runtime layer
Oligo Runtime AI takes a different angle than most tools in this roundup. Where others focus on discovering agents or controlling their identities, Oligo focuses on what is happening inside the running application. It monitors AI components and agent behavior at runtime, including agent actions, tool calls, and OS-level behavior, to detect exploit attempts, supply chain risks, and rogue agent activity as it happens.
The OS-level behavioral monitoring is the differentiator. Most AI security tools operate at the API or identity layer. Oligo goes deeper, watching what the agent process is actually doing on the system. That matters for detecting attacker hijacking attempts where an adversary has compromised an agent and is using it to move laterally or exfiltrate data. Prompt injection attacks that successfully redirect an agent's behavior will show up in OS-level telemetry even if the prompt itself looked benign.
The AI App Discovery capability addresses a practical problem for security teams: developers evaluate and adopt AI tools constantly, and security teams rarely have visibility into which tools are active versus which were just tested. Oligo catalogs both, giving you a realistic picture of your AI footprint rather than just the officially sanctioned deployments.
The trade-off is deployment complexity. Runtime monitoring requires instrumentation of the applications being protected. This is not a read-only API connection like Silverfort or a network-level gateway. You need to work with development teams to instrument AI applications, which means Oligo fits best in organizations where security and development teams have an established relationship and a shared deployment pipeline. If your security team has no access to application deployment processes, this will be a harder sell internally.
Arcade MCP Runtime
Best for: Engineering teams deploying multi-user AI agents at production scale
Arcade MCP Runtime is the most developer-oriented tool in this roundup. It is not primarily a security product in the traditional sense. It is an MCP runtime that bakes security controls into the infrastructure layer where AI agents operate. The distinction matters: Arcade is for teams building and deploying AI agents who want authorization, governance, and audit capability built into the platform from the start, rather than bolted on afterward.
The core security mechanism is per-user authorization rather than shared service accounts. This is the right model for multi-user AI agent deployments. When an agent acts on behalf of a specific user, it should carry that user's permissions, not a shared service account with broad access. Arcade integrates with existing OAuth and identity provider flows to enforce this, which means it works with your existing IAM infrastructure rather than replacing it.
Deployment flexibility is a genuine differentiator here. Cloud, VPC, on-premises, and air-gapped deployments are all supported. For organizations in regulated industries or with strict data residency requirements, the ability to run the MCP runtime in an air-gapped environment is significant. Most competitors in this space are cloud-only.
The honest limitation is that Arcade is infrastructure, not a security monitoring platform. It does not detect anomalous agent behavior, it does not surface shadow AI deployments, and it does not provide the kind of posture management or detection and response capabilities that the other tools in this roundup offer. The NIST coverage data is not specified, which reflects this positioning. Arcade belongs in a stack alongside a detection tool like Oligo or a posture management tool like Straiker, not as a standalone security solution. If you are an engineering team scaling AI agent deployments and you want the authorization model to be correct from day one, Arcade is the right foundation.
How to Choose the Right Tool
Agentic AI security is not a single problem. It is at least four distinct problems: discovering what agents exist, controlling what they are allowed to do, monitoring what they are actually doing, and responding when something goes wrong. Most tools in this space are strong in one or two of these areas and weak in the others. Before you evaluate vendors, decide which problem is most urgent for your organization right now.
Start with your deployment reality. If your AI agents are primarily SaaS-managed products like Microsoft Copilot or Salesforce Agentforce, you need a tool with native integrations to those platforms. Zenity AIDR and Straiker Discover AI both cover this. If your agents are custom-built applications running on Bedrock or Vertex AI, runtime monitoring from Oligo or posture management from Straiker is more relevant.
Assess your identity infrastructure first. AI agents are non-human identities. If you do not have visibility into what service accounts and OAuth tokens your agents are using, start with an identity-focused tool like Silverfort before adding detection and response capabilities. Trying to do detection without identity context produces noisy, unactionable alerts.
Match the tool to your team structure. Oligo Runtime AI requires application instrumentation, which means you need developer cooperation. Silverfort and Straiker use read-only API connections and require no code changes. If your security team operates independently from engineering, choose tools that do not require deployment access to the applications they protect.
Consider your MCP exposure specifically. If your organization is adopting MCP servers to extend agent capabilities, that is a distinct attack surface that not all tools cover. Straiker Discover AI maps MCP server connections explicitly. Silverfort enforces authorization at the MCP gateway layer. Arcade MCP Runtime governs the MCP infrastructure itself. Pick based on whether you need visibility, enforcement, or infrastructure control.
Do not ignore the governance requirement. Regulators and auditors are starting to ask about AI agent oversight. Tools that provide audit trails, human-in-the-loop approval workflows, and compliance mapping will matter for organizations in financial services, healthcare, and government. Silverfort and Zenity AIDR both have explicit compliance mapping capabilities.
Evaluate platform lock-in carefully. Cortex AgentiX and Falcon AIDR deliver the most value if you are already in those ecosystems. If you are not, the switching cost is high and the integration story for third-party data is weaker. Standalone tools like Silverfort, Straiker, Zenity, and Oligo are easier to adopt in mixed-vendor environments.
Think about where you are in the AI security maturity curve. If you have no inventory of AI agents, start with discovery tools like Straiker Discover AI. If you have inventory but no access controls, move to Silverfort or Arcade. If you have controls but no detection, add Oligo or Falcon AIDR. Buying a detection and response tool before you know what agents exist is a waste of budget.
Check deployment model requirements. Arcade MCP Runtime supports air-gapped deployments. Most other tools in this category are cloud-only. If you are in a regulated environment with strict data residency requirements, deployment model is a hard constraint, not a preference.
Skip the Vendor Demos. Compare Agentic AI Security Tools in 10 Seconds.
Side-by-side features, integrations, and ratings for Agentic AI Security tools.
Agentic AI security is not a category you can afford to defer. AI agents are already operating in your environment, and the attack techniques targeting them, from prompt injection to MCP server abuse to agent identity hijacking, are documented and actively exploited. The tools in this roundup cover the full lifecycle from discovery through governance to runtime detection. No single tool covers everything well. The right approach is to start with visibility, layer in access controls, and then add runtime detection. Use the how-to-choose criteria above to sequence your investments based on where your actual exposure is today, not where the marketing says the threats will be tomorrow.
Frequently Asked Questions
What is agentic AI security and why does it need dedicated tools?
Agentic AI security covers the protection of autonomous AI agents that take actions, call APIs, and access data without direct human oversight for each step. Traditional security tools were built for human users and static applications. AI agents create new risks like prompt injection, privilege escalation through agent chains, and shadow deployments that existing EDR, SIEM, and IAM tools were not designed to detect or prevent.
What is MCP security and which tools cover it?
Model Context Protocol (MCP) is the standard that lets AI agents call external tools, APIs, and data sources. MCP servers can be misconfigured, overprivileged, or compromised, giving attackers a path to hijack agent behavior. Straiker Discover AI maps MCP server connections and detects risky configurations. Silverfort AI Agent Security enforces authorization at the MCP gateway layer. Arcade MCP Runtime governs the MCP infrastructure itself.
How do I find shadow AI agents running in my organization?
Shadow AI agents are deployments that business units or developers stood up without security review. Straiker Discover AI and Zenity AIDR both include shadow AI detection that identifies agents operating outside sanctioned channels. Silverfort's read-only API discovery across identity providers and SaaS platforms also surfaces unsanctioned agent identities.
Can I use these tools if I am not already on a specific security platform like CrowdStrike or Palo Alto?
Most tools in this roundup are platform-agnostic. Silverfort, Straiker, Zenity, Oligo, and Arcade all operate independently of your existing security stack. Cortex AgentiX and Falcon AIDR deliver significantly more value if you are already in those respective ecosystems, and their value proposition weakens in mixed-vendor environments.
What is AI-SPM and how does it differ from AI detection and response?
AI Security Posture Management (AI-SPM) is about finding misconfigurations, excessive permissions, and risky integrations before they are exploited, similar to how CSPM works for cloud infrastructure. AI detection and response is about identifying and containing active threats against AI systems in real time. Most mature AI security programs need both, and several tools in this roundup, including Zenity AIDR and Oligo Runtime AI, combine both capabilities.
Do these tools require installing agents on every system where AI workloads run?
It depends on the tool. Silverfort and Straiker Discover AI use read-only API connections and require no software installation or code changes. Oligo Runtime AI requires application instrumentation, which means working with development teams. Cortex AgentiX and Falcon AIDR use existing platform agents if you are already deployed on those platforms.
How this list was made
Commercial products only, one product per company, companies that were acquired are excluded. Ranked by market signals and an editorial review. Paid placements are labeled. Read the full methodology at /methodology.
