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Gartner® adds agentic AI to the Global Attack Surface Grid.
SecureAuthSecureAuth

Agent Authority

Zero trust for AI agents.

Securely adopt AI agents faster. One control layer to govern access at the moment agents take action.

Get a demoSee how it works

The problem

Agents make autonomous decisions and act on them

Every way an agent differs from a user is a business risk.

  • A user can spot a bad instruction and stop

    An agent will execute a malicious instruction

    Compromised

  • A user follows a process

    An agent improvises to reach its goal

    Destructive

  • A user has one identity

    An agent acts as many without a chain back to the delegating user

    Unaccountable

  • A user needs approvals to spend

    An agent spends unmetered and unapproved

    Expensive

Capabilities

Control agent access and action from discovery to record

With Agent Authority find every agent, decide each action it takes, protect the data it touches, cap what it spends, and keep a record of all of it.

Capability flywheel: Agent Identity SecuritySix capabilities ring the core, Agent Identity Security: Agent identity & discovery, Runtime authorization & enforcement, Detection & risk, Prompt & data protection, Cost & operational controls, Evidence & traceability. Each capability links to its section below.
  1. Agent identity & discovery
  2. Runtime authorization & enforcement
  3. Detection & risk
  4. Prompt & data protection
  5. Cost & operational controls
  6. Evidence & traceability

Control AI usage across systems

One authority for every path an agent takes

Agent Authority

One policy on every path

From

Employees and desktop agents

Endpoint Security

Discover AI apps, block unsanctioned tools

  • Shadow AI discovery
  • App control
  • Device posture
To

Local files and shadow AI

From

Agents calling tools over MCP

MCP Gateway & Security

Authorize every tool call, scoped to the task

  • Tool authorization
  • Task scoping
  • Context-aware policy controls
To

Enterprise apps and data

From

Prompts from users and agents

LLM Security

Redact sensitive data, stop prompt injection

  • Data redaction
  • Injection defense
  • Token limits
To

Model providers

Agent identity & discovery

Every other control depends on knowing which agent is acting and which user it is acting for. A rule that only knows the user cannot tell Claude Code from a rogue script using the same credentials.

  • One register of every agent in use, each with a named owner.

The Agents & NHIs screen in the Agent Authority console: totals for registered agents, agents active in the last 24 hours and tool executions, above a table of every agent instance with its owner, runtime, tags such as sanctioned, under-review and shadow-ai, status, last active time and creation time.

Agent inventory & ownership, 1 of 4

Runtime authorization & enforcement

Every call is decided as it happens, based on the agent acting, the delegating user and the context. Then a call is allowed, denied, or escalated to a person.

  • Every tool call is checked against policy before it runs, down to the operation and its arguments.

The Agent Actions screen in the Agent Authority console: a numbered, drag-ordered list of allow and deny rules evaluated top to bottom so the first match wins, each scoped to an MCP server, an agent instance or specific tools, with its live, draft or conditional status and its effect, under a banner counting the active response filters and an Add Rule action.

Tool & action authorization, 1 of 4

Detection & risk

Every agent instance is observed against its own baseline, so abnormal behavior from an agent within reach of sensitive data is flagged before it becomes an incident.

  • Each agent instance is baselined, so drift in what it calls, or how often, shows up as it happens.

An anomaly finding open in the Agent Authority console: a medium-severity tool volume spike for one user, flagged because 120 calls in an hour were six times the typical 20 for that hour, with an evidence chart of calls against the expected range over 30 days, the affected entity, and Dismiss and Acknowledge actions over the findings table.

Behavioral anomaly detection, 1 of 3

Prompt & data protection

Authorization decides whether a call happens. Based on the context, responses are masked or blocked in flight, so your data is protected from agents reading it.

  • Sensitive fields are removed from prompts and responses based on who is asking and why.

A response filter open in the Data Protection screen of the Agent Authority console: redact salary and compensation data in Slack and Atlassian responses for two named people in the analysts group using Claude Code, unless they are in the hr or finance-leads groups, with the condition shown as a validated, tested expression and pattern filters that replace matches with redaction labels.

Context-aware redaction, 1 of 4

Cost & operational controls

Spend is attributed to the agents, people and teams driving it, and capped before a loop runs up the bill.

  • Cap how often an agent can call a tool or model, per instance, team or time window.

The Usage Limits screen in the Agent Authority console: rate limits that cap how fast agents may call tools, such as an org-wide burst guard, 60 calls per minute per tool for code search, 100 tool calls per hour for analysts and 200 calls per hour for each Claude Code instance, each with its scope, budget, the key it counts by, whether it logs or denies, and its status.

Rate limits & quotas, 1 of 4

Evidence & traceability

Agent Trail records every call an agent made, the delegating user, the context it carried, and the rule that decided it.

  • Every call an agent made, the person behind it, the arguments it carried and the rule that decided it.

The Agent Trail in the Agent Authority console: every action agents took, in order, such as a coding agent reading file contents or opening a pull request on GitHub or a workspace agent listing Jira issues, each naming the agent, the tool, the server and the person it acted for, flagging any redactions and stamped with a time.

Agent Trail, 1 of 4

Why it’s different

We are not just another MCP gateway

If you only need tool aggregation, an MCP gateway is genuinely useful. If you need to decide what an agent does with those tools, it isn’t enough.

Granularity
A typical MCP gateway: Allow or deny per tool, not per operation, argument or field
Agent AuthorityRe-decides every call on the attributes it carries
Identity
A typical MCP gateway: One key per deployment, so every run looks the same
Agent AuthorityAn identity per run, from the person and the process
Containment
A typical MCP gateway: Revoke the key and every agent stops
Agent AuthorityOne instance suspended and revoked in flight, with every other agent left running
Response
A typical MCP gateway: Forwards the response unchanged
Agent AuthorityFilters the response before the agent reads it
Spend
A typical MCP gateway: Caps total spend
Agent AuthorityMeters the spend and attributes it to the people driving it
Evidence
A typical MCP gateway: Forwards the call and moves on
Agent AuthorityKeeps the record, denied calls included
Authentication
A typical MCP gateway: Not its job
Agent AuthoritySits downstream of your IdP and delegates authentication to it
Coverage
A typical MCP gateway: Sees only the agents pointed at it, blind to others
Agent AuthorityAlso enforced at the model and on the endpoint

Integrations

Integrate seamlessly

Your identity provider stays where it is. Your agents keep their vendors. We sit on the call between them. No code changes, no model lock-in.

  • Identity Providers

    SecureAuth, Okta, Ping, Microsoft Entra ID & more. Keep the one you have

  • Agent Vendors & Models

    Claude, ChatGPT, Gemini, Microsoft Copilot, Cursor, Replit & in-house agents

  • Enterprise Apps & Data

    Salesforce, DocuSign, Slack, Microsoft 365, GitHub, Snowflake & more

  • SIEM, SOAR & XDR

    Splunk, CrowdStrike, Datadog, Microsoft Sentinel & more

Connected on secure standards

  • OAuth 2.1
  • OpenID Connect
  • SPIFFE / SVID
  • mTLS
  • Token exchange
  • Model Context Protocol

Get started

Start this week

Try it yourself

Point one agent at it this afternoon.

No agent rewrite, no SDK, no change to how your team works. You change an endpoint, and the first audit trail appears immediately.

// .mcp.json — any MCP-capable agent
{
  "mcpServers": {
    "secureauth": {
      "url": "https://gateway.secureauth.ai/mcp",
      "auth": "oauth"
    }
  }
}

// every tool the agent is entitled to,
// through one brokered endpoint.
// nothing else is reachable.
Read the docs

Work with us

Bring us your riskiest agent.

Start with a read-only Agent Risk & Spend Snapshot: what is running, what it can reach, and what it costs. No deck, no instrumentation, no commitment. Just your actual environment.

In your snapshot

  • What's running

    Every agent in your environment, including the unsanctioned ones.

  • What it can reach

    The tools, apps and data each agent can touch.

  • What it costs

    Spend by the agents, people and teams driving it.

Read-only · No SDK · No agent changes

Get the snapshotTalk to an architect