[Black Hat USA 2026] Meet Akto team at Booth #8508. Book a meeting->
[Black Hat USA 2026] Meet Akto team at Booth #8508. Book a meeting->
[Black Hat USA 2026] Meet Akto team at Booth #8508. Book a meeting->
See why teams choose Akto over Zenity
Zenity is a decent governance platform for agents on select platforms like Microsoft, Salesforce, and ServiceNow. Akto covers every platform your agents run on, tests them with 4,300+ probes, and enforces per-call MCP authorization across the whole stack.
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When to use Akto vs. Zenity: A head-to-head comparison
When to use Akto vs. Zenity: A head-to-head comparison
Compare Akto and Zenity to find the best AI security solution for your needs.
Compare Akto and Zenity to find the best AI security solution for your needs.
Compare Akto and Zenity to find the best AI security solution for your needs.
Use Case
Akto
Zenity
Shadow AI discovery
Native, continuous, risk-scored
Real-time inventory of MCPs, LLMs, AI apps, agents, and agent skills across SaaS, browsers, IDEs, and endpoints, with risk mapped to every asset.
Network-level scanning
Network-inline discovery with a catalog of 4,000+ AI apps. Strong on network-visible traffic. Endpoint agent-skill inventory is not documented as a native capability.
Shadow AI discovery
Native, continuous, risk-scored
Real-time inventory of MCPs, LLMs, AI apps, agents, and agent skills across SaaS, browsers, IDEs, and endpoints, with risk mapped to every asset.
Network-level scanning
Network-inline discovery with a catalog of 4,000+ AI apps. Strong on network-visible traffic. Endpoint agent-skill inventory is not documented as a native capability.
Shadow AI discovery
Native, continuous, risk-scored
Real-time inventory of MCPs, LLMs, AI apps, agents, and agent skills across SaaS, browsers, IDEs, and endpoints, with risk mapped to every asset.
Network-level scanning
Network-inline discovery with a catalog of 4,000+ AI apps. Strong on network-visible traffic. Endpoint agent-skill inventory is not documented as a native capability.
Runtime Browser Protection
Native, on-device
A native browser extension intercepts AI interactions in real time and blocks unsafe prompts or sensitive data before they reach the model.
No browser based guardrails
Device-based monitoring of agent activity (Cursor, Copilot, Claude Desktop). No native on-device extension for real-time prompt blocking on third-party AI.
Runtime Browser Protection
Native, on-device
A native browser extension intercepts AI interactions in real time and blocks unsafe prompts or sensitive data before they reach the model.
No browser based guardrails
Device-based monitoring of agent activity (Cursor, Copilot, Claude Desktop). No native on-device extension for real-time prompt blocking on third-party AI.
Runtime Browser Protection
Native, on-device
A native browser extension intercepts AI interactions in real time and blocks unsafe prompts or sensitive data before they reach the model.
No browser based guardrails
Device-based monitoring of agent activity (Cursor, Copilot, Claude Desktop). No native on-device extension for real-time prompt blocking on third-party AI.
Input and output guardrails
Native, continuous, risk-scored
Deep guardrails on both input and output. Catches prompt injection, PII leakage, toxic content, and off-policy responses at the employee endpoint.
Intent-based detection
Intent-based detection across the full execution path (tool calls, memory access, data usage).
Input and output guardrails
Native, continuous, risk-scored
Deep guardrails on both input and output. Catches prompt injection, PII leakage, toxic content, and off-policy responses at the employee endpoint.
Intent-based detection
Intent-based detection across the full execution path (tool calls, memory access, data usage).
Input and output guardrails
Native, continuous, risk-scored
Deep guardrails on both input and output. Catches prompt injection, PII leakage, toxic content, and off-policy responses at the employee endpoint.
Intent-based detection
Intent-based detection across the full execution path (tool calls, memory access, data usage).
AI governance policy engine
AI-native governance
Granular policies per employee, role, team, and use case through a purpose-built governance layer.
No AI governance
No dedicated AI governance policy engine documented.
AI governance policy engine
AI-native governance
Granular policies per employee, role, team, and use case through a purpose-built governance layer.
No AI governance
No dedicated AI governance policy engine documented.
AI governance policy engine
AI-native governance
Granular policies per employee, role, team, and use case through a purpose-built governance layer.
No AI governance
No dedicated AI governance policy engine documented.
Agent builder integrations
50+ native integrations
Native integrations with AWS Bedrock, Databricks, Snowflake, n8n, LangChain/LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Vertex AI, Copilot, and 50+ others.
Limited integration coverage
Native integration with limited platforms like Microsoft Copilot Studio, Salesforce Agentforce, ServiceNow, and ChatGPT Enterprise.
Agent builder integrations
50+ native integrations
Native integrations with AWS Bedrock, Databricks, Snowflake, n8n, LangChain/LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Vertex AI, Copilot, and 50+ others.
Limited integration coverage
Native integration with limited platforms like Microsoft Copilot Studio, Salesforce Agentforce, ServiceNow, and ChatGPT Enterprise.
Agent builder integrations
50+ native integrations
Native integrations with AWS Bedrock, Databricks, Snowflake, n8n, LangChain/LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Vertex AI, Copilot, and 50+ others.
Limited integration coverage
Native integration with limited platforms like Microsoft Copilot Studio, Salesforce Agentforce, ServiceNow, and ChatGPT Enterprise.
AI security proxy
Single decision point
Managed inline proxy between agents and their models, MCPs, and tools. Every prompt, tool call, and MCP invocation passes through one policy plane with one audit trail.
Event-driven ingestion
SaaS, cloud, and endpoint connectors with event-driven ingestion. Not designed as an inline decision point sitting between agents and their tools.
AI security proxy
Single decision point
Managed inline proxy between agents and their models, MCPs, and tools. Every prompt, tool call, and MCP invocation passes through one policy plane with one audit trail.
Event-driven ingestion
SaaS, cloud, and endpoint connectors with event-driven ingestion. Not designed as an inline decision point sitting between agents and their tools.
AI security proxy
Single decision point
Managed inline proxy between agents and their models, MCPs, and tools. Every prompt, tool call, and MCP invocation passes through one policy plane with one audit trail.
Event-driven ingestion
SaaS, cloud, and endpoint connectors with event-driven ingestion. Not designed as an inline decision point sitting between agents and their tools.
MCP security and governance
Per-call authorization
Decides, per call, whether a specific agent can invoke a specific tool on a specific MCP server. MCP servers are first-class policy targets. The MCP Registry acts as an enterprise allowlist.
No per call policy enforcement
Approved MCP server list and agent behavior restrictions applied through Agentic Control. Per-call MCP authorization is not a named primitive.
MCP security and governance
Per-call authorization
Decides, per call, whether a specific agent can invoke a specific tool on a specific MCP server. MCP servers are first-class policy targets. The MCP Registry acts as an enterprise allowlist.
No per call policy enforcement
Approved MCP server list and agent behavior restrictions applied through Agentic Control. Per-call MCP authorization is not a named primitive.
MCP security and governance
Per-call authorization
Decides, per call, whether a specific agent can invoke a specific tool on a specific MCP server. MCP servers are first-class policy targets. The MCP Registry acts as an enterprise allowlist.
No per call policy enforcement
Approved MCP server list and agent behavior restrictions applied through Agentic Control. Per-call MCP authorization is not a named primitive.
AI agent identity governance
Complete identity governance
Discover all NHIs associated with AI Agents, map the identities to its owner, the agent and set identityb policies like rotation, segregation and more.
No agent identity governance
AI agent identity governance is not documented. Agent access is mediated through the AI gateway and the Guardian Agent's supervisory judgment.
AI agent identity governance
Complete identity governance
Discover all NHIs associated with AI Agents, map the identities to its owner, the agent and set identityb policies like rotation, segregation and more.
No agent identity governance
AI agent identity governance is not documented. Agent access is mediated through the AI gateway and the Guardian Agent's supervisory judgment.
AI agent identity governance
Complete identity governance
Discover all NHIs associated with AI Agents, map the identities to its owner, the agent and set identityb policies like rotation, segregation and more.
No agent identity governance
AI agent identity governance is not documented. Agent access is mediated through the AI gateway and the Guardian Agent's supervisory judgment.
Agent and MCP guardrails
Deterministic and AI-mediated
Bidirectional guardrails at every layer of the agent stack: prompt injection, output data leakage, tool-call authorization, Agent Intent Verification, denied topics, custom rules. Every policy tagged to OWASP Agentic Risk categories.
Intent-based ML guardrails
Intent-based ML applied to agent behavior. Strong on reasoning intent; less on deterministic policy primitives with per-decision auditability.
Agent and MCP guardrails
Deterministic and AI-mediated
Bidirectional guardrails at every layer of the agent stack: prompt injection, output data leakage, tool-call authorization, Agent Intent Verification, denied topics, custom rules. Every policy tagged to OWASP Agentic Risk categories.
Intent-based ML guardrails
Intent-based ML applied to agent behavior. Strong on reasoning intent; less on deterministic policy primitives with per-decision auditability.
Agent and MCP guardrails
Deterministic and AI-mediated
Bidirectional guardrails at every layer of the agent stack: prompt injection, output data leakage, tool-call authorization, Agent Intent Verification, denied topics, custom rules. Every policy tagged to OWASP Agentic Risk categories.
Intent-based ML guardrails
Intent-based ML applied to agent behavior. Strong on reasoning intent; less on deterministic policy primitives with per-decision auditability.
Red teaming and offensive testing
4,300+ probes
Actively attacks your own agents. Simulates adversarial inputs, probes tool-call boundaries, tests MCP interactions, identifies exploitable behaviors before production.
No red teaming
Detection and prevention only. No offensive red teaming, adversarial probe library, or attack simulation capability.
Red teaming and offensive testing
4,300+ probes
Actively attacks your own agents. Simulates adversarial inputs, probes tool-call boundaries, tests MCP interactions, identifies exploitable behaviors before production.
No red teaming
Detection and prevention only. No offensive red teaming, adversarial probe library, or attack simulation capability.
Red teaming and offensive testing
4,300+ probes
Actively attacks your own agents. Simulates adversarial inputs, probes tool-call boundaries, tests MCP interactions, identifies exploitable behaviors before production.
No red teaming
Detection and prevention only. No offensive red teaming, adversarial probe library, or attack simulation capability.
They said it, not us
Akto vs Zenity: Complete AI Security for Modern AppSec Teams
Comprehensive AI Security, MCP Security, Red Teaming, and Runtime Protection – all in one platform.
