Zenity is the recognized vendor in AI agent governance. Mentioned in multiple Gartner AI security reports, it is genuinely strong at discovering agents and governing how they behave across Microsoft, Salesforce, and ServiceNow. But its governance-first design, its concentration on a handful of enterprise SaaS platforms, and its lack of offensive testing push a lot of teams to evaluate alternatives before they commit. Here is what is worth looking at.

Why teams look for Zenity alternatives

Zenity built the AI agent governance category early and does the governance job well: continuous discovery, posture management, intent-based detection across the full execution path, and deep coverage of the major copilot platforms. For an enterprise standardized on Microsoft and Salesforce, that is a strong fit. But as teams weigh it against purpose-built alternatives, a few patterns come up.

• Governance-first, not attack-first. Zenity discovers what agents exist and governs what they should do, but it does not offer offensive red teaming, adversarial probe libraries, or attack simulation. It can tell you how an agent is configured, not what an attacker could actually break if the underlying model or toolchain is weak.

• Coverage is concentrated on the big SaaS suites. Zenity is purpose-built for Microsoft, Salesforce, ServiceNow, and ChatGPT Enterprise. A large and fast-growing share of homegrown agents is built on frameworks like LangChain, LangGraph, CrewAI, and AutoGen, or inside data platforms like Databricks, which sit outside its core model.

• Developer and MCP coverage is surface-level. Endpoint developer agents such as Cursor and Copilot are monitored at a surface level, and Zenity does not secure MCP tool execution or the actual runtime API interactions that agents make.

• There is an alert-to-action gap. Even with its correlation engine improving incident narratives, findings still need human interpretation. There is no built-in path to push them into CI/CD pipelines or into a format developers can act on directly.

• SaaS-centric blind spots. The fastest-growing agentic surface, autonomous homegrown agents wired to internal APIs and databases, is largely outside the coverage model that makes Zenity strong on managed SaaS.

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

The alternatives at a glance

1. Akto

Akto is a purpose-built AI security platform (Atlas for employee AI usage, Argus for homegrown agents and MCPs) designed for the agentic threat surface from day one. Where Zenity leads with governance, Akto pairs discovery with offense and enforcement. It automatically discovers agents, MCP servers, and LLM endpoints across the whole environment, including shadow resources, and is not limited to specific SaaS platforms. It runs 4,300+ adversarial probes mapped to the OWASP Top 10 for agents, MCPs, and LLMs, enforces bidirectional guardrails with tool-call authorization and Agent Intent Verification, and secures MCP tool execution per call rather than only monitoring it. Native IDE hooks cover Cursor, Claude Code, Copilot, Gemini CLI, and Codex, so findings are developer-actionable. Teams get full agent visibility in hours.

Akto vs Zenity at a glance

Where it fits: strongest when you want the full agentic attack surface covered by one AI-native product that discovers, tests, and enforces across every platform your agents run on rather than only the major SaaS suites.

2. Operant AI

Operant AI, founded by ex-Apple, VMware, and Google engineers, focuses on runtime defense for AI in cloud infrastructure. Its AI Gatekeeper suite includes an MCP Gateway that provides real-time discovery, detection, and inline blocking of MCP traffic across local developer tools like Claude Desktop and remote agents on Kubernetes, AWS Bedrock, Azure, and Google Vertex AI. It publishes MCP security research including its "Shadow Escape" zero-click disclosure, and Gartner names it a representative vendor in AI TRiSM.

Where it falls short: coverage is strongest on MCP and cloud-native runtime rather than on employee AI usage discovery or offensive red teaming as a product. Because enforcement runs through the gateway, agents that route around it are less visible.

3. Repello AI

Repello AI is a Bangalore-based startup that made offensive testing its centerpiece. Its ARTEMIS engine actively simulates prompt injection, tool poisoning, and permission-escalation attacks against LLMs, agents, RAG pipelines, and MCP servers, with findings mapped to the OWASP LLM Top 10, NIST AI RMF, and MITRE ATLAS. Around that engine sit two more products: AI Inventory, which builds an AI Bill of Materials with attack-path graphs, and Repello Guard, a runtime guardrail layer configured directly from the red-team results.

Where it falls short: Repello raised a seed round in 2024 and is still building enterprise references at scale. Teams evaluating it should probe support tier, roadmap velocity, and multi-region readiness rather than assume parity with better-funded platforms.

4. AIM Intelligence

AIM Intelligence, based in Korea, pairs Stinger, a red-teaming engine that generates high volumes of multi-modal attack scenarios including agentic tests, with Starfort, a low-latency proxy-level guardrail that handles sensitive-data detection and abnormal API-call control. Deployment supports cloud or on-premises, and its named partners include OpenAI, Microsoft, and Meta.

Where it falls short: it is deepest on offensive testing and guardrails rather than shadow AI discovery, posture management, or governance workflow. Enterprise references are stronger in APAC than in North America.

5. Lakera

Lakera ships two things that matter for AI apps: Lakera Guard, an inline guardrail API focused on prompt injection and jailbreak detection with minimal overhead, and Lakera Red for adversarial testing before deployment. It fits well when the constraint is latency budget and the team wants a focused guardrail layer rather than a discovery-to-runtime stack.

Where it falls short: it does not do agent and MCP inventory, posture management, or fleet-wide governance. Teams that want more than a guardrail layer will need to combine it with other tools.

6. Runlayer

Runlayer is an MCP gateway that treats MCP governance as an IAM problem. It supports native SSO and SCIM, group sync, conditional access, and attribute-based access control across the user, device, client, server, session, and request. Admins can add existing MCP endpoints, deploy custom MCPs to managed infrastructure, or wrap internal APIs into governed MCPs published to approved users and agents through a catalog. It supports 300+ AI clients including Cursor, Claude Code, ChatGPT, and Windsurf.

Where it falls short: it protects the servers it routes and cannot see agents that go elsewhere. It is a gateway and IAM layer, not an offensive testing or discovery tool, and some of its constructs sit above the MCP spec rather than conforming to it. Its documentation is largely gated behind a sales demo.

7. Knostic

Knostic solves an adjacent problem: enterprise assistants like Microsoft Copilot and Glean surface information from a corpus that assumes users have permission to see everything indexed. That is often not true. Knostic layers need-to-know controls on top of these assistants, flags oversharing risk, and detects data-leakage paths through search and retrieval.

Where it falls short: it does not overlap with agent runtime security, MCP governance, or offensive testing. It is a permissions-and-search-hygiene tool. Teams looking to secure homegrown agents or govern agent behavior at runtime need something else on top.

Why teams choose Akto

Each of these tools is strong in its lane. Repello AI focuses on red teaming with inventory and runtime, Knostic on need-to-know governance for enterprise assistants, Lakera and AIM Intelligence on guardrails and red teaming, Operant AI on runtime MCP defense, and Runlayer on MCP gateway governance. The catch is that AI risk does not stay in one lane. A single organization is usually exposed across employee AI usage, homegrown applications, and autonomous agents at the same time, so a point tool tends to leave surfaces uncovered. Zenity is strong at governance and detection, but it is governance-first, concentrated on the big SaaS suites, and has no offensive testing.

That is the case for a complete platform that discovers, tests, and enforces, and it is where Akto separates itself. A few questions make the gap concrete:

1. Do you need coverage across every platform your agents run on, or just the big SaaS suites? Akto covers agents wherever they live and is not tied to specific SaaS platforms, where Zenity is deepest on Microsoft, Salesforce, and ServiceNow.

2. Do you need offensive testing, not just governance? Akto ships 4,300+ adversarial probes for agents, MCPs, and LLMs. Zenity does not offer red teaming, so it cannot tell you what an attacker could actually break.

3. How deep is your MCP and agent-framework footprint? Akto secures MCP tool execution per call and covers LangChain, LangGraph, CrewAI, n8n, Bedrock, and Databricks, where Zenity monitors MCP at the posture level and is thin on frameworks.

4. Do discovery, red teaming, and runtime need to work together in a developer-actionable form? Akto closes the alert-to-action gap with IDE hooks and CI/CD-friendly findings, rather than leaving results for a human to interpret.

5. Do you need it in production today? Akto is GA across all of the above and stands up in hours.

If your need is enterprise agent governance on the Microsoft and Salesforce stack, Zenity may fit well. But if you want one platform that covers the full AI attack surface, tests it, and enforces at runtime, Akto is the strongest choice, which is why it leads this list. Whichever way you lean, run a short proof of concept against your own agents and MCP servers and measure real detections, false positives, latency, and time to value.