Veza maps what AI agents can access. PlainID controls what they actually access, do, and expose at the moment of action.
What will you do when:
You can see an agent’s full blast radius,
but nothing shrinks it at the moment the agent acts?
The access review passed last quarter,
but the agent combines its permissions today in a way no review anticipated?
Sub-agents spawn at runtime
and operate outside the inventory your governance depends on?
Veza’s Access Graph does what identity security should do for the agent era: it maps every human and machine identity to its effective permissions, surfaces excessive access, and automates the reviews and revocations that keep posture clean. That is necessary infrastructure.
But a mapped permission 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 Veza builds the map of access, PlainID enforces the decision at the moment of access.
Veza’s platform connects through integrations it describes as agentless and read-only: it observes permissions and monitors activity. PlainID sits in the execution path. Every prompt, retrieval, tool call, and response passes a policy decision before it happens, evaluated against identity, context, and intent at millisecond latency.
Veza maps effective permissions to data resources in the language of create, read, update, delete. PlainID enforces fine-grained controls directly at the data platform layer: row, column, and field-level filtering applied before data is retrieved, including RAG pipelines and vector databases, with built-in dynamic masking of sensitive data in the LLM-generated response before it reaches the user.
Veza maps agents to the humans who own them, which establishes accountability for reviews. PlainID binds the initiating human’s real-time entitlements into every authorization decision the agent makes downstream, so an agent cannot access what its initiating human cannot access. Access is minted per action, scoped to the immediate intent, and revoked immediately after use.
Veza discovers when an AI agent connects to public MCP servers. PlainID controls what that agent can actually do inside those tools (such as Jira), intercepting the call to ensure the agent cannot pull unauthorized sensitive data or execute malicious commands at the moment of action.
Discover why Fortune 500 enterprises trust PlainID to securely scale agentic AI systems:
Discovers AI agents across platforms, maps agent-to-data connections, visualizes blast radius
True end-to-end control across the AI flow: Prompt → Data → Tools → Output
Maps effective permissions to data resources through read-only integrations
Row, column, and field-level enforcement including RAG pipelines and vector databases
Not part of Veza’s documented model; the focus is access posture, not generated responses
Dynamically intercepts and masks sensitive data in LLM-generated outputs before delivery
Posture assessment and remediation workflows; access changes execute through provisioning (Access AuthZ)
Real-time, dynamic authorization driven by context and intent at every action
Maps agents to human owners for accountability and access reviews
Runtime binding: every agent action constrained by the initiating human’s real-time clearance
Least-privilege posture: detect excess access, then remediate through reviews and automated grant/revoke
Proactive enforcement with Zero Standing Privileges (ZSP) minted per action
Inventory and discovery based; agents enter governance once discovered
Enforces on all agents including those spawned dynamically at runtime
Acquired by ServiceNow (March 2026); being integrated into ServiceNow AI Control Tower, packaging in transition
Unified, out-of-the-box enforcement across APIs, data, and AI agent frameworks
Discovers and inventories public MCP servers (2,000+)
Governs which MCP tools, parameters, and data agents can invoke at call time
Access visibility and governance layer: maps who can access what, and remediates excess
Runtime authorization layer: enforces what they can access, retrieve and expose at every action
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.
This is the critical control layer missing from traditional IAM. Every decision evaluates:
Access exists only when it’s needed, and only for the exact purpose it’s required. It’s later revoked immediately after use.
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.
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.
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.