[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 WitnessAI
WitnessAI is a network-inline control layer for enterprise AI usage. Akto is a security-first AI platform, GA today, with deterministic policy applied at a single decision point across employee AI usage, homegrown apps, and agents.
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When to use Akto vs. WitnessAI: A head-to-head comparison
When to use Akto vs. WitnessAI: A head-to-head comparison
Compare Akto and WitnessAI to find the best AI security solution for your needs. .
Compare Akto and WitnessAI to find the best AI security solution for your needs. .
Compare Akto and WitnessAI to find the best AI security solution for your needs. .
Use Case
Akto
WitnessAI
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.
Network inspection only
Browser AI traffic is inspected at the network layer. No documented native on-device extension for interception at the browser session.
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.
Network inspection only
Browser AI traffic is inspected at the network layer. No documented native on-device extension for interception at the browser session.
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.
Network inspection only
Browser AI traffic is inspected at the network layer. No documented native on-device extension for interception at the browser session.
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 ML
Intent based guardrails with AI judging whether to enforce guardrails or not. Prone to hallucinations.
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 ML
Intent based guardrails with AI judging whether to enforce guardrails or not. Prone to hallucinations.
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 ML
Intent based guardrails with AI judging whether to enforce guardrails or not. Prone to hallucinations.
Native IDE hooks
Enforce Guardrails across IDEs
Native hooks in Cursor, Claude Code, Copilot, Codex & more. Enforce policy, capture agent skill invocations, and block malicious MCP tool calls at execution.
No IDE Support
IDE-originated traffic is covered via the network. No documented native IDE toolchain hooks that block malicious MCP invocations at execution.
Native IDE hooks
Enforce Guardrails across IDEs
Native hooks in Cursor, Claude Code, Copilot, Codex & more. Enforce policy, capture agent skill invocations, and block malicious MCP tool calls at execution.
No IDE Support
IDE-originated traffic is covered via the network. No documented native IDE toolchain hooks that block malicious MCP invocations at execution.
Native IDE hooks
Enforce Guardrails across IDEs
Native hooks in Cursor, Claude Code, Copilot, Codex & more. Enforce policy, capture agent skill invocations, and block malicious MCP tool calls at execution.
No IDE Support
IDE-originated traffic is covered via the network. No documented native IDE toolchain hooks that block malicious MCP invocations at execution.
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
Agent-specific identity governance with per-agent ownership and rotation policy is not documented.
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
Agent-specific identity governance with per-agent ownership and rotation policy is not documented.
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
Agent-specific identity governance with per-agent ownership and rotation policy is not documented.
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.
Undocumented scope
Described as "automated red teaming." No published OWASP Top 10 coverage, probe depth, or attack pattern count.
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.
Undocumented scope
Described as "automated red teaming." No published OWASP Top 10 coverage, probe depth, or attack pattern count.
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.
Undocumented scope
Described as "automated red teaming." No published OWASP Top 10 coverage, probe depth, or attack pattern count.
They said it, not us
Akto vs WitnessAI: Complete AI Security for Modern AppSec Teams
Comprehensive AI Security, MCP Security, Red Teaming, and Runtime Protection – all in one platform.
