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PlainID

PlainID vs. Ping Identity for Agentic AI Authorization

Ping establishes who your AI agents are. PlainID controls what they can access, do, and expose at the moment of every action.


What will you do when:

See a Demo to Fix AI Access Control Gaps
The agent's delegated scope is valid,
but it retrieves 10x more sensitive data than the task requires?
Individual tool calls are permitted,
but their sequence leads to an unauthorized data exfiltration event?
Sub-agents spawn dynamically at runtime
and operate completely outside your registered identity model?

Ping verifies the agent's identity. PlainID controls what agents can actually do and expose across the entire agentic flow.

Ping Identity’s Identity for AI suite does what IAM should do for the agent era: it registers agents as trusted identities, scopes their delegated authority, and enforces least privilege at the tool layer. That is necessary infrastructure.

But a confirmed identity is not an authorization decision. The moment an agent acts, retrieving data, invoking a tool, returning a response, a different question applies: should this specific action, on this specific data, in this specific context, right now, be permitted?

Where Ping establishes the identity boundary, PlainID enforces what happens within it.

Full AI Flow Coverage (Not Just APIs)

Ping’s Agent Gateway and PingAuthorize secure the tool and API boundary. But PlainID enforces authorization everywhere decisions actually happen: blocking unauthorized intent before execution, filtering what data can be accessed, governing which tools agents can invoke, and masking what is exposed. We govern the full agentic flow: prompt, data, tools, and output.

Data Layer Protection & Output Masking

PingAuthorize provides ABAC at the structured API layer, but it is blind to the LLM's context window. PlainID enforces fine-grained access controls directly at the unstructured data platform layer. Pre-retrieval RAG filtering ensures the agent never retrieves what it shouldn't, while built-in dynamic output masking intercepts and redacts sensitive data in the AI-generated response before it reaches the user.

Binding Human and Non-Human Identities at Runtime

Ping implements delegated authority, linking an agent token to a human user at registration. But a delegation record is not the same as runtime binding. PlainID ties the initiating human's real-time entitlements, role, and clearance into every authorization decision the agent makes downstream. An agent cannot access what its initiating human cannot access, evaluated at the moment of every call.

Continuous Zero Standing Privileges

Least privilege at the delegation scope level still relies on standing permissions. PlainID enforces true Zero Standing Privileges (ZSP). Access is granted dynamically, just-in-time, per action, based on current context, purpose, and policy, and revoked immediately after use.

How PlainID's Agentic AI Is Different From Ping Identity's Ping Authorize

Discover why Fortune 500 enterprises trust PlainID to securely scale agentic AI systems:

Capability areaPlainIDPing Identity
Coverage Scope

Full coverage: End-to-end, prompt to output

Full coverage for Agentic, Data, APIs, Microservices and Apps. True end-to-end control: Prompt → Data → Tools → Output

Gap / limitation: API and token only

Token based or in application based on API only

Data Layer Protection

Full coverage: Row, column and field level

Row, column, and field-level enforcement including RAG pipelines and vector databases

Gap / limitation: API-only, blind to RAG

API-only data filtering; blind to unstructured AI data and RAG pipelines

LLM Output Masking

Full coverage: Masks generated output

Dynamically intercepts and masks sensitive data in LLM-generated outputs before delivery

Gap / limitation: API fields only

Can mask structured API response fields; blind to synthesized LLM output

Decision Enforcement

Full coverage: Real-time at every action

Real-time, continuous, dynamic authorization driven by context and intent at every action

Gap / limitation: Coded in app, based on token or APIs.

Identity & Accountability

Full coverage: Human and agent on every action

Every action tied to human & agent identities, with clear ownership and scope

Gap / limitation: Governed separately, not bound

Agent and human identities governed separately. No binding of an agent's action to the end user behind it

Prevention Model

Full coverage: Zero Standing Privileges per action

Proactive enforcement with Zero Standing Privileges (ZSP) minted per action

Gap / limitation: Static OAuth scopes

Least privilege via static OAuth scopes set at delegation time

Dynamic Runtime Agents

Full coverage: All agents, including spawned

Enforces on all agents including those spawned dynamically at runtime

Gap / limitation: Registered agents only

Enforces on registered agents; dynamically spawned agents may not be in scope

Packaging & Deployment

Full coverage: Unified, out of the box

Unified, out-of-the-box enforcement across APIs, data, and AI agent frameworks

Gap / limitation: Two products to buy and integrate

Requires two separate products: Agent Gateway + PingAuthorize, each purchased and integrated independently

Data Access Controls

Full coverage: Enforced before retrieval

Proactive enforcement before data is retrieved or exposed, with built-in output masking, for all data types.

Gap / limitation: Table or object level

Table or object level support, with no business context.

API / MCP access controls

Full coverage: The full transaction

Controls API/MCP, and the full transaction – including adaption of parameter input and masking of response.

Gap / limitation: Token and scopes

Controls API/MCP access with token and scopes.

  • Full coverage
  • Partial coverage
  • Gap / limitation

Ping enforces at the gateway and API boundary.
PlainID enforces inside the flow, on the prompt's intent, the retrieved content, and the response

Ping Identity verifies identity. PlainID controls what AI agents can actually do across the entire agentic AI flow.

Ping Identity’s for AI suite registers agents as trusted identities, scopes their delegated authority, and enforces least privilege at the tool layer. But authenticating identity alone is no longer enough. PlainID enforces authorization everywhere decisions actually happen: blocking unauthorized intent before execution, filtering what data can actually be accessed, governing which tools agents can invoke, and masking what is exposed.

Enterprises running Ping for identity do not have to wait to govern what their agents access, retrieve, and expose. PlainID works alongside Ping Identity, filling the critical runtime and data-layer gaps immediately.

The AI flow from prompt to response, with guardrails that block unauthorized questions, control data access, enforce tool access and mask sensitive output

Built for Agentic Reality

PlainID's Runtime Authorization Platform is designed for how AI systems actually operate by accessing data, invoking tools, and making decisions across systems in real time. It provides centralized management with distributed enforcement across your entire enterprise stack.

  • Runtime decisioning in place of one-time checks
  • Intent-based enforcement tied to every action
  • Context-aware policies across APIs, applications, data, and AI
The PlainID robot flanked by four labelled tiles: Focus, policy enforcement; Role, AI guardian; Control, access management; Follow, AI flow logic
An AI agent tile wired to tiles for people, chat, a database and a cloud, over a run of authorization log lines

Binding Human and Non-Human Identities

This is the critical control layer missing from traditional IAM. Every decision evaluates:

  • The human user
  • The AI agent
  • The intent of the action

Zero Standing Privileges

Access exists only when it’s needed, and only for the exact purpose it’s required. It’s later revoked immediately after use.

A fingerprint tile over lines of one-time session tokens, ending in a single auth context id
A key on the front card of a stack of policy attribute cards listing role, scope, decision and risk score

Your AI agents are acting. Are you authorizing every action?

  • Trusted by Fortune 500 enterprises to secure millions of identities
  • Millions of authorization decisions processed daily
  • Built for high-scale, real-time enforcement across complex environments
  • Recognized by Gartner and KuppingerCole in authorization and security platforms

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.

Through 2029, over 50% of successful cybersecurity attacks against AI agents will exploit access control issues.

Gartner, How to Secure Custom-Built AI Agents, Dionisio Zumerle, Jeremy D’Hoinne, 11 June 2025

Get control over your AI agents without replacing your IAM

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.

See how it works in your environment.

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Your AI agents are acting. Are you authorizing 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.

See PlainID Working in Your Environment