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
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.

PlainID: The Market-Leading Authorization Platform for the Agentic AI Era — trusted by Fortune 2000 enterprises, proven at scale.

PlainID: Secure the Entire AI Flow with Enterprise-Grade Authorization

Control the Prompt

Enforce query authorization so agents only ask within approved scope. Block attempts to extract sensitive data before retrieval.

Control the Data

Apply policy-based guardrails to manage who can access which data, in real time, by identity and context. Prevent retrieval of unauthorized documents.

Control the Tools (Beta)

With MCP, govern which services and tools agents can invoke. Context-aware access ensures only the right identities can use the right tools.

Control the Response

Mask and filter generated responses to avoid exposing sensitive or unauthorized insights. Keep output secure, compliant, and controlled.

The stakes are high.

In agentic AI, access control gaps are amplified, raising enterprise-wide liability and exposure.

Privilege Escalation icon

Privilege Escalation

Agentic AI acts on behalf of users or systems with insufficient enforcement of contextual identity and permissions, leading to unauthorized access to sensitive data or restricted actions.

Data Exposure icon

Data Exposure

Gaps in access control may lead to regulatory and compliance failures, increasing the risk of fines and legal consequences. Breaches erode trust and brand value, resulting in reputation damage.

Lack of Auditability icon

Lack of Auditability

Multi-step reasoning and external calls (APIs, tools, data) make it difficult to trace and verify which services and data were accessed.

See Policy Enforcement in Action

 

Why Policy Management is Non-Negotiable
for AI Applications

Don’t stop your business from scaling agentic AI

 

 

Ensure every AI action is tied to the right identity and entitlements, supporting zero trust.


Reduce data exposure and unauthorized actions with real-time controls.

Real-time controls reduce data exposure and unauthorized actions

Ensure regulatory compliance with continuous, auditable access decisions.

Continuous, auditable access decisions for regulatory compliance

Manage access at every stage: prompt, data retrieval, tools, and response.

Centralized control across the full AI workflow: prompt, data retrieval, tools, and response

How AI Access Controls Work:
From Static Guardrails to Dynamic
Policy Management

PlainID integrates directly into your AI development stack, embedding policy enforcement into
the fabric of your agentic AI — without slowing innovation.

Choosing the Right Policy Management
Solution for your Agentic AI Systems

  • End-to-End AI Flow Guardrails: Enforce identity-aware controls from prompt to response, covering data retrieval, tool/API access, and output generation.

  • Enterprise-Ready Scale: Trusted by Fortune 500s and proven in complex, high-volume environments — ensuring your AI access controls scale securely with enterprise demands today and tomorrow.

  • Identity-First Enforcement: Support for people, devices, services, and AI agents, with context-aware decisions at every stage of the AI flow.

  • Business and Code Alignment: Simple for stakeholders to review, Policy-as-Code for developers to implement, fully integrated with enterprise AI.

  • Granular Permissions: Enforce least-privilege, fine-grained entitlements for both users and AI agents, ensuring no excessive access rights.