Onyx Alternatives: Enterprise AI Agent Security Platforms Worth Evaluating in 2026

Onyx alternatives are hard to shortlist because the AI agent security market has split into two different jobs. The first is securing endpoint, SaaS, and cloud AI agents, the productivity layer where employees use coding assistants, browser AI, and copilots to work faster. The second is AI application security: securing the actions that custom-built AI agents take inside business workflows, the category Gartner is defining as AI agent security for custom-built AI. Onyx Security is built for the first job. This guide is for teams whose priority is the second.

The urgency comes from the rapid move of AI agents into production workflows. Agents are now embedded in payments, claims processing, healthcare workflows, customer support, and internal automation, where a single injected instruction or misconfigured action can affect revenue, customer data, or audit records. At the same time, compliance teams are being asked to produce behavioral evidence under frameworks like the EU AI Act, ISO 42001, and NIST AI RMF, and an inventory of approved AI tools alone does not provide it.

For these agents, security teams look for execution-context visibility, action-level control, and an architecture that keeps execution data inside the organization. Frontier models are accelerating this. A Mythos-class model can find and exploit flaws in agents, frameworks, and their dependencies faster than any CVE feed can name them, which leaves what the agent does at runtime as the only thing still available to enforce.

Onyx Security Overview

Onyx Security positions itself as a secure AI control plane for the agentic era. Architecturally, it is an inline proxy gateway: agent traffic, prompts, and MCP tool calls route through the control plane before reaching downstream systems, covering browser AI, coding assistants, desktop agents, and MCP servers. Those are productivity agents, and overseeing that layer is the job Onyx is built for, sharing the category with the rest of the productivity-agent field. An MCP gateway governs the calls that route through it, which is useful and not sufficient on its own for agents whose logic runs inside the application.

Business-critical enterprise agents are a different category. They run inside production applications, and the security question changes with them: not which AI exists and what it may use, but what a specific agent just did to a customer account, a payment, or a claim. NIST’s AI Agent Standards Initiative and OWASP’s Top 10 for Agentic Applications 2026 treat those agent actions as their own security domain, and that is the job the platforms below are evaluated against.

What Drives Security Teams to Consider Onyx Alternatives

The search usually starts when the agents that matter most are custom-built enterprise agents rather than productivity tools. Five signals point that way:

• The agents execute inside production applications, at the application layer runtime, where traffic inspection and MCP gateways do not reach
• Depth of execution evidence matters more than breadth of coverage
• Actions need business context: the same tool call can be a refund or a records update
• Sensitive execution data cannot be routed through an intermediary, self-hosted or not
• Auditors under the EU AI Act, ISO 42001, and NIST AI RMF ask what agents actually did

The shift is from traffic visibility to full execution visibility.

6 Best Onyx Alternatives for AI Agent Security

The following tools approach AI security from different angles, including execution-level agent security, application guardrails, model security, and governance. The list focuses on established platforms enterprise security teams already evaluate for AI risk, plus the emerging execution-level agent security category. Earlier-stage AI governance startups sit outside that scope.

1. Rein Security

Rein Security is purpose-built for business-critical enterprise agents, not the productivity agents employees use to browse, write, and code. It runs as a code-native sidecar at the application layer runtime, not the cloud runtime, capturing the complete execution chain: every prompt, every service call, every tool invocation, every resource touched, and the business outcome attached to each action. Guardrails enforce at the code level, at the moment an action executes, and the record stays inside the application boundary.

Rein is strongest where agent actions carry direct business risk: payment, claims, healthcare, SaaS, support, and internal workflow agents with access to sensitive systems. Its platform is built on four pillars: visibility into every agent action and its business context, contextual action-level controls that stop deviations in real time, posture management covering code security, vulnerabilities, and supply chain risk, and complete data sovereignty that keeps every byte of execution data inside the organization.

Best for: Production enterprise agents wired into business-critical workflows.

Pro: Rein gives security teams execution-level visibility into agent behavior and business outcomes, recorded inside the workflow itself, without sensitive execution data leaving the organization.

Con: Rein is not a tool for endpoint AI protection. Teams whose immediate need is controlling employee AI use on managed devices will want that layer alongside it.

2. Pangea

Pangea, acquired by CrowdStrike in 2025, offers AI Guard capabilities for detecting prompt injection, redacting sensitive data, blocking malicious content, and applying configurable detection recipes. It suits teams that want composable APIs and SDKs for LLM guardrails.

Best for: API-based AI guardrails for application builders.

Pro: Pangea provides flexible, developer-friendly guardrails that application teams can embed into LLM workflows through APIs, SDKs, or gateway integrations.

Con: AI Guard inspects data at the integration points the application team configures. Building a complete execution trace means those teams must instrument and correlate every relevant model, tool, service, and business action themselves.

3. Protect AI

Protect AI, now part of Palo Alto Networks, focuses on securing AI applications and ML systems across model security, AI application discovery, red teaming, vulnerability assessment, and runtime protection. It fits AI and ML programs that need coverage across the development lifecycle.

Best for: AI and ML security across model development and runtime.

Pro: Broad lifecycle coverage helps security teams manage AI assets, models, vulnerabilities, and runtime risks within a single security program.

Con: Organizations may need additional context to translate its runtime findings into the specific business impact of agent actions across production workflows.

4. F5 AI Guardrails, formerly CalypsoAI

F5 acquired CalypsoAI and introduced F5 AI Guardrails as its runtime security offering for deployed AI models and agents. The platform focuses on adversarial attack defense, data leakage prevention, responsible AI governance, AI observability, and compliance support, and is most relevant for teams already running F5’s application delivery and security portfolio.

Best for: Runtime AI guardrails and model usage governance within the F5 ecosystem.

Pro: F5 AI Guardrails combines adversarial defense, data leakage prevention, observability, and governance in a runtime offering that aligns well with existing F5 environments.

Con: Organizations outside the F5 ecosystem should evaluate how easily the platform integrates with their existing security tools and processes.

5. Robust Intelligence, now Cisco AI Defense

Robust Intelligence is now part of Cisco AI Defense. It focuses on AI application risk detection, model validation, testing, and controls for organizations standardizing on Cisco security.

Best for: AI model validation, testing, and risk detection within the Cisco ecosystem.

Pro: Cisco AI Defense combines AI application risk detection, model validation, and testing with the broader Cisco security environment.

Con: Organizations not already using Cisco should consider how well the platform fits their existing tools, procurement processes, and security operations.

6. Securiti AI

Securiti AI, acquired by Veeam, focuses on data and AI governance, privacy, security, and compliance across hybrid and multicloud environments. It is best suited for teams focused on AI usage, data flows, sensitive data controls, and governance obligations.

Best for: AI governance, data controls, privacy, and compliance.

Pro: Securiti AI provides data governance, privacy, security, and compliance capabilities across hybrid and multicloud environments.

Con: Its data-centric focus is less suitable when the primary requirement is in-process tracing and action-level control of production enterprise agents.

Onyx Alternatives Comparison Overview

Compare on architectural fit, not feature breadth. A control plane and an execution-level platform are built for different jobs.

The AI Control Plane vs. Enterprise Agent Execution Security Distinction

Rein and Onyx protect different types of agents. The key buying distinction is between an AI control plane and execution-level agent security, and some enterprises run both.

A control plane governs the productivity AI used across a workforce: browser assistants, copilots, and desktop agents. It enforces policy on the traffic routed through it. This is the category Onyx competes in.

AI application security covers the custom-built AI agents wired into payments, claims, records, and support, at the application layer runtime. In these workflows, approving a tool is not the same as approving every action taken with it, and a blocked phrase is not a blocked transaction.

In one published demonstration, Rein’s Agent Breakers research chained five vulnerabilities in a major U.S. retailer’s AI shopping agent into remote code execution, run entirely through the public mobile app. The retailer had a dedicated gateway with intent classification. The exploited vulnerabilities existed beyond the gateway, in the tool calls, the retrieval pipeline, and the backend environment it could not observe.

Key Criteria for Evaluating Onyx Alternatives

Weight these by the risk profile of the agents being protected.

Migration and Deployment Considerations When Moving Beyond an AI Control Plane

Moving from adoption oversight to execution-level security changes what teams measure and where controls are enforced.

• Usage policies become action controls. Control plane policy governs which tools are approved; production agent policy answers whether this agent, under this user context, may perform this action.
• Coverage spans the production environment. A production agent reaches across cloud services, internal APIs, MCP servers, and data stores. Security coverage should span that whole environment rather than treating the agent as an isolated model interaction.
• Audit evidence must reflect real execution. Ask each platform to answer from one record what the agent did, who triggered it, which systems were touched, what the business outcome was, and which policy applied. If answering them means correlating data from five separate systems, every audit and every 2 AM investigation gets slower and harder.

Conclusion

Evaluate Onyx alternatives by the job that needs doing. Enterprise-wide discovery, usage governance, and ROI measurement are the control plane’s job. Model testing and data governance should be evaluated as separate requirements.

Production enterprise agents are different. They run inside business workflows, touch sensitive systems, make customer-facing decisions, and create audit obligations. They need execution security, and some enterprises run both layers. Era 1 of agents was prompts. Era 2 was productivity. Era 3 is the company.

Rein is built around this distinction between employee AI adoption oversight for third-party tools and AI application security for custom-built AI. For teams evaluating Onyx alternatives in 2026, the question is simple: do you need to oversee how AI is adopted across the enterprise, or do you need to see, control, and audit what your production agents actually do?