Why teams look for Onyx Security alternatives

Onyx's runtime supervision idea is genuinely interesting, and its Guardian Agent catches classes of misbehavior that static rule engines miss. But when teams line it up against purpose-built AI security tools, several patterns keep coming back.

• Security is one of five product pillars. Onyx bills itself as a secure AI control plane, but the platform also handles LLM routing, cost optimization, and adoption dashboards. That is a lot of surface area for one product, and it can dilute the depth on the security side that many buyers actually came looking for.

• Endpoint and browser coverage is integration-mediated, not native. Shadow AI discovery, browser telemetry, and endpoint signal all come through the customer's existing browser, CNAPP, SASE, and EDR tools. There is no documented native Onyx browser extension that blocks employee prompts in real time on the device.

• Enforcement is fragmented across layers. Onyx splits enforcement between an AI gateway, an inline MCP gateway, third-party integrations, and the Guardian Agent on top. There is no single inline component that sees every agent call, which makes a unified policy plane and one audit trail harder to guarantee.

• The guardrail model is AI supervising AI. Guardrails run through the Guardian Agent's judgment of another AI's intent rather than through configurable policy primitives that fire on defined conditions. That is powerful for reasoning-time misbehavior but harder to audit and less predictable per decision than deterministic guardrails.

• MCP governance stops short of per-call authorization. Onyx proxies MCP traffic, sanctions approved tools, and surfaces supply-chain risk, but per-call MCP authorization, an allowlist registry, and MCP servers as first-class policy targets are not named primitives.

• No AI governance policy engine and no OWASP-mapped probe library. There is no per-employee, per-role governance layer for AI usage, and its red teaming is described as automated without published OWASP Top 10 coverage or probe depth.

• It is a new company. Onyx launched publicly only in March 2026, so track record, customer references, and third-party validation are still forming compared with more established vendors.

If any of these are dealbreakers for your environment, here are seven alternatives worth a look.

The alternatives at a glance

1. Akto

Where Onyx bundles security into a broader orchestration platform and leans on integrations for endpoint and browser reach, Akto is a security-first AI platform (Atlas for employee AI usage, Argus for homegrown agents and MCPs) that natively instruments the surfaces most likely to leak. Its browser extension and native IDE hooks (Cursor, Claude Code, Copilot, Gemini CLI, Codex) intercept prompts and MCP tool calls on the device before they execute. A single inline proxy sits between agents and their models, MCP servers, and tools, applying deterministic policy at one decision point with one audit trail. Bidirectional guardrails include tool-call authorization and Agent Intent Verification, tagged to OWASP Agentic Risk categories. An MCP Registry acts as an enterprise allowlist of approved MCP servers, and per-call MCP authorization treats MCP servers as first-class policy targets. Argus adds 4,300+ adversarial probes across the OWASP Top 10 for agents, MCPs, and LLMs. Teams get full agent visibility in hours.

Akto vs Onyx Security at a glance

Where it fits: best suited for organizations looking for a security-first approach that instruments real prompt and tool-call surfaces, centralizes deterministic policy enforcement, and independently monitors agent activity beyond what SASE, EDR, or CNAPP tools can see.

2. Aurascape

Aurascape sits inline on the network and decodes AI traffic natively across modern protocols rather than relying on API integrations or a browser extension for enforcement. It provides visibility into thousands of AI apps, intent-based policy, data lineage across chained tool calls, and prevention aimed at reducing DLP-style false positives on AI interactions. Its pitch is deploying alongside an existing SSE (for example Zscaler) rather than replacing it, with a 48-hour service level for supporting newly launched AI apps. The company launched from stealth in April 2025 with $50M, is CRN AI security list-recognized, and reports Fortune-scale healthcare deployments.

Where it falls short: network-inline control is powerful for employee AI usage and DLP, but it is a different lane from red teaming, MCP per-call authorization, and IDE-toolchain hooks. Teams whose core need is agent and MCP security at the application layer usually pair it with an agent-focused tool.

3. Operant AI

Operant AI runs a runtime AI defense platform anchored in cloud-native infrastructure. Its AI Gatekeeper suite includes MCP Gateway for real-time inspection and blocking of MCP traffic across developer tools like GitHub Copilot and Claude Desktop, plus remote agents on Kubernetes, AWS Bedrock, Azure, and Google Vertex AI. AI detection and response covers live cloud and AI workloads. The company's MCP research disclosed the "Shadow Escape" zero-click exploit class, and Gartner names it a representative vendor in AI TRiSM. Founding team is from Apple, VMware, and Google.

Where it falls short: center of gravity is MCP and cloud runtime, so employee AI usage discovery across browsers, offensive testing as a product, and posture management across the agent lifecycle are lighter. Gateway-based enforcement covers what routes through it, not agents that go around it.

4. AIM Intelligence

AIM Intelligence brings two products: Stinger for automated, multi-modal red teaming across text, image, audio, video, and physical AI, and Starfort for proxy-level guardrails with sensitive-data detection and abnormal API-call control. Deployment can be cloud or on-premise, and the company partners with OpenAI, Microsoft, and Meta.

Where it falls short: it is a testing and guardrail specialist, not a full-platform discovery and governance tool. Its footprint is deepest in Korea and APAC, so North American and European enterprise references are still building.

5. Repello AI

Repello AI takes a loop approach from the offensive side. Its ARTEMIS engine runs continuous adversarial testing across agents, MCP servers, and RAG pipelines with attack patterns tied to OWASP LLM Top 10, NIST AI RMF, and MITRE ATLAS. Findings feed Repello Guard, its runtime layer, so guardrails adjust to observed exploits rather than static rulesets. AI Inventory sits underneath, mapping assets and generating attack-path threat graphs from an AIBOM.

Where it falls short: it is a seed-stage company with a small team relative to established platforms. Enterprise buyers should test its scale story and support model before committing to it as a primary control plane.

6. Knostic

Knostic addresses a specific bottleneck in enterprise AI: when Microsoft Copilot or Glean is turned on, retrieval assumes every indexed document is fair game for everyone with a license. Knostic enforces need-to-know policy on top, catches oversharing, and traces which documents are being surfaced to which people. It plugs into Microsoft 365 and comparable stacks.

Where it falls short: its footprint is narrow by design. It is not built for offensive testing, MCP governance, or runtime agent security, and it needs mature classification metadata to work well. Outside a Copilot-heavy environment, its relevance drops sharply.

7. Lakera

Lakera's Guard API is designed to run inline in front of AI apps at low latency, filtering for prompt injection, jailbreaks, and unsafe outputs. Lakera Red pairs with it for adversarial testing before shipping. The mental model is a specialized filter that does one thing well and stays out of the rest of the stack.

Where it falls short: it is a focused defense layer, not a discovery, posture, or runtime governance platform. Teams needing the broader story will layer it into a larger stack.

Why teams choose Akto

Onyx frames itself as a control plane, and control planes make sense when what you actually need is orchestration plus supervision. But most security teams do not need an LLM router with security bolted on. They need an AI security platform, and the difference matters.

The clearest way to separate these tools is to sort them by where the enforcement actually happens. Aurascape enforces on the network. Operant enforces at the MCP gateway in cloud infrastructure. Repello, AIM, and Lakera enforce in-app or proxy-side. Knostic enforces at the retrieval layer inside enterprise assistants. Onyx enforces at four layers that its Guardian Agent orchestrates. Akto enforces at every layer through one inline decision point plus native on-device instrumentation.

Akto approaches the same problem the opposite way. A few questions make the gap concrete:

1. Do you want a security-first platform or a broader orchestration platform with security as one pillar? Akto is built around discovery, testing, and enforcement for AI. Onyx bundles those alongside orchestration, routing, cost, and adoption dashboards.

2. Do you want native, on-device coverage of employee AI usage? Akto instruments the browser and IDE toolchain directly, so prompts and MCP tool calls are seen and blocked at the source. Onyx reads from browser, CNAPP, SASE, and EDR integrations.

3. Should one component decide every agent call? Akto's inline proxy is a single decision point with one policy plane and one audit trail. Onyx splits enforcement across four layers.

4. Deterministic guardrails or AI judging AI? Akto uses configurable policy primitives with tool-call authorization, Agent Intent Verification, and OWASP tagging. Onyx's Guardian Agent judges another agent's intent, which is powerful but harder to audit.

5. How deep is your MCP need? Akto ships per-call MCP authorization and an MCP Registry allowlist with MCP servers as first-class policy targets. Onyx runs an MCP gateway but does not name those primitives.

6. How rigorous is the offensive side? Akto ships 4,300+ probes covering the OWASP Top 10 for agents, MCPs, and LLMs. Onyx describes automated red teaming without a published probe scope.

If you need a supervisory reasoning layer on top of a broader control plane, Onyx has an interesting take on that idea. If you need one AI-native platform that covers the whole attack surface, is GA today, and applies deterministic policy in one place, Akto is the stronger match, and that is why it opens this list. Either way, run a short proof of concept against your own agents and MCP servers and measure real detections, false positives, latency, and time to value.