Axiomatics decides who can access what. PlainID governs what AI agents do with it, at every step of the flow.
What happens when:
Your policy engine approved the query
but the prompt behind it was never evaluated
Individual records were masked correctly
but the answer the agent generated from them wasn’t
An agent acted on a request its own initiating user
couldn’t have made
Axiomatics has spent two decades proving out ABAC: attribute-based policy decisions for applications, APIs, and data. That engine works. It also recognizes, in its own materials, that agentic AI is a new problem to solve, with language about controlling AI agent access, restricting tool execution, and governing MCP calls.
Recognizing the problem is not the same as enforcing it. What Axiomatics documents today is a decisioning model extended toward AI, evaluating requests after they reach the policy layer, masking the data records an agent retrieves. What it does not document is authorization at the moments that actually determine what an AI agent does: before the agent acts on a prompt, on the answer it generates from multiple sources, and on the gap between what an agent can do and what the human behind it is actually allowed to do.
PlainID enforces all three, natively, in production, today.
A policy engine that evaluates the request an agent’s tool call generates is not the same as a system that evaluates the intent behind the prompt that triggered it. PlainID authorizes prompts, retrievals, tool calls, and outputs individually, each at the moment it is attempted, not just the API call that eventually results.
Every AI agent acts for someone. PlainID binds the initiating human’s real-time entitlements into every action the agent takes, so the agent never exceeds what its user could do directly. Axiomatics’ policy engine evaluates the agent’s own attributes. It has no documented mechanism for injecting the human’s entitlements into that same decision.
Axiomatics masks sensitive fields in the records an agent retrieves. That is source-level protection. It does not follow the data into the answer an LLM synthesizes from it. PlainID evaluates and masks the generated output itself, immediately before it reaches the user, closing the gap where partially-masked sources recombine into a fully exposed answer.
PlainID’s full-flow enforcement, prompt, retrieval, tool and MCP calls, output, runs in production today, on the same policy engine, same Authorizer suite, same audit trail used across the rest of the enterprise stack. Every action is enforced natively, not orchestrated through a separate integration layer.
Discover why Fortune 500 enterprises trust PlainID to securely scale agentic AI systems:
ABAC/PBAC policy decisioning for apps, APIs, and data (Orchestrated Authorization)
Runtime authorization across the full agentic AI flow
Positioning language on AI agent and MCP/tool governance; no documented GA proof points
Native, production enforcement across prompt, retrieval, tool/MCP calls, and output
Not documented
Evaluates user intent and prompt scope before execution
Masks retrieved/source data before delivery
Masks and filters the generated output itself, at the moment of delivery
Not documented
Binds the initiating human’s real-time entitlements into every agent action
Zero Trust policy enforcement (general); no documented Zero Standing Privileges or just-in-time model for agents
Zero Standing Privileges enforced at the action level, just-in-time
Audit and compliance tracking of authorization decisions
Per-decision, full-flow audit trail across every step of the agentic workflow
Vision/solutions page; no named customers or case studies found for AI capability
Production-ready today, named enterprise customers, analyst recognition
Axiomatics can confirm that an AI agent’s request matches an attribute-based policy. That confirmation happens at the point the request reaches the decision engine, after the prompt has already been interpreted, after the agent has already decided what to ask for.
At the moment an agent acts, retrieving a record, calling a tool, generating a response, the question is not whether the resulting API call matches a policy. The question is whether this specific action, on this specific data, for this specific user, at this specific moment, should happen at all.
That decision has to account for the prompt’s intent, the initiating human’s entitlements, the sensitivity of the data in play, and what the agent is about to generate, not just retrieve. Axiomatics’ materials do not describe a decision engine that evaluates all four. PlainID does, on every action, in milliseconds.
Authorization must be evaluated every time an action is attempted, not once the request has already taken shape. A policy engine is the starting point. Full-flow enforcement is the security control.
Most authorization platforms secure the request. PlainID secures the flow that produced it.
PlainID enforces across:
Every AI agent acts on behalf of someone. If that relationship isn’t enforced at runtime, the agent can exceed what its initiating human was ever entitled to do.
PlainID evaluates both identities in every authorization decision: the human behind the request and the agent executing it. It does not document a mechanism for pulling the initiating human’s real-time entitlements into that same decision.
PlainID grants access just-in-time, scoped to the specific task being performed, and revoked immediately after. No assumption that what was valid at the start of a session remains valid three tool calls later. Every action is a new decision, evaluated against live context.
As AI agents take on more of the enterprise workload, standing privileges become the liability that scales fastest. Authorization built for the AI era can’t depend on what was true at provisioning. It has to be true at the moment of action.
Masking a data source is not the same as masking what an AI agent says about it.
An LLM can synthesize a fully exposed answer from several partially-masked records, none of which individually violated a policy. The masking rule did its job at the source. The generated response undid it.
PlainID evaluates the output itself, immediately before it reaches the user, applying the same policy logic to generated content that Axiomatics applies to retrieved records. Data-layer masking protects the input. PlainID protects the answer.
Gartner, How to Secure Custom-Built AI Agents, Dionisio Zumerle, Jeremy D’Hoinne, 11 June 2025 GARTNER is a registered trade mark and service mark of Gartner, Inc. and/ or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
A policy match at the API layer confirms the request was well-formed. It does not confirm the prompt was safe to act on, the output was safe to deliver, or the agent stayed within its user’s real entitlements. PlainID enforces all three, at agent speed, at enterprise scale, at the moment of every action.
Privilege management determines who can access your environment. Runtime authorization governs what happens inside it, at agent speed, at enterprise scale, at the moment of every action.