EnforceAuth Introduces AI‑Native Security Fabric; Opens Waitlist for Enterprises

Executive Summary

Autonomous AI agents and machine‑driven workflows are reshaping the enterprise. Recent reports warn that 80 % of organizations have already encountered risky behaviors from AI agents and 39 % have seen rogue agents access unauthorized systems. Analysts project that the agentic‑AI market will grow 56 % in 2025 and that by 2028 at least 15 % of work decisions will be made autonomously by AI, up from virtually zero in 2024. This surge in autonomy creates an entirely new attack surface; AI agents now read emails, approve transactions and modify databases without human oversight. Legacy identity‑based security models can’t keep up.

This shift is especially acute for organizations that have already moved authorization logic out of application code and into centralized, policy-as-code systems, where decisions increasingly operate as shared infrastructure rather than isolated checks.

Even organizations that have adopted policy-as-code often discover a second-order challenge: evaluating decisions is only part of the problem, while operating, governing, and proving those decisions across services introduces a new class of infrastructure risk.

EnforceAuth’s AI‑Native Security Fabric is a decision‑centric authorization platform built for this era. Rather than asking whether a user can log in, EnforceAuth evaluates whether every action—whether initiated by a human, an AI agent or an automated workflow—is safe, justified and allowed in real time. The company today opened a free enterprise waitlist for early adopters to pilot the platform.

Market Challenge

• New risk drivers: Autonomous AI agents and low‑code tools operate across systems with minimal human prompts. Security analysts note that these agents think and act like humans but lack corresponding threat models, making their triggers a blind spot for governance.
  
  

• Escalating incidents: Recent case studies show AI agents reading corporate email, approving financial transactions, modifying databases and taking thousands of actions before any human notices. A survey of security leaders found that 33 % discovered AI agents had inadvertently shared sensitive data.
  
  

• Inadequate controls: Traditional identity and access management focuses on who is allowed into a system. With AI agents, permission boundaries alone are insufficient; agents need context about whether an action is appropriate at this moment. Prompt injection attacks and “excessive agency” vulnerabilities have been elevated to OWASP’s top risks.
  
  

• Regulatory and fiduciary pressure: CISOs, CIOs and boards face heightened scrutiny as regulators require auditable controls for AI decision‑making. Only 10 % of executives currently have a strategy for managing agentic identities.
  
  

The EnforceAuth Solution

• Decision‑centric authorization: EnforceAuth’s Security Fabric shifts from identity‑based to real‑time decision governance, extending policy-as-code into continuous operational control and auditability.. Each action—whether read, write, or execute—is evaluated against policy‑as‑code that captures business intent. This ensures that AI agents and automated workflows cannot act without explicit authorization.
  
  

• AI‑native architecture: Built for agentic and autonomous systems from the ground up, the platform supports machine‑to‑machine, human‑in‑the‑loop and fully automated scenarios. It integrates with common AI frameworks and enterprise systems to enforce granular policies without slowing down workflows.
  
  

• Zero‑trust controls: The Security Fabric enforces least privilege, short‑lived credentials and human verification for high‑impact actions, addressing the zero‑trust principles recommended by analysts.
  
  

• Continuous observability and auditability: EnforceAuth records the context and outcome of every decision, enabling compliance teams to trace actions across AI agents, APIs and services. This transparency responds directly to concerns that AI decisions are opaque and untraceable.
  
  

• Operator‑grade scalability: The founders have deep experience building policy‑as‑code and authorization systems for highly regulated environments. The platform is designed to operate at enterprise scale with minimal latency, supporting thousands of decisions per second.
  
  

Founder Perspective

“Security isn’t just about who gets in anymore; it’s about what gets done, by whom, for whom and when,” said Mark Rogge, founder of EnforceAuth. “AI agents can already read emails, approve wire transfers and modify data across systems. Our Security Fabric treats every action as a policy‑driven decision, ensuring that autonomous workflows are controlled, auditable and aligned with business intent.”

Early Access Program

EnforceAuth is inviting enterprises to join a capacity‑based waitlist for free early access to the AI‑Native Security Fabric. Organizations building AI agents, deploying autonomous workflows or moving towards machine‑driven decisioning can work directly with EnforceAuth to tailor policies, integrate existing systems and shape future features. Early participants will receive:

• Pilot deployment and integration support with existing IAM, policy management and AI infrastructure.
  
  

• Policy‑as‑code templates aligned with multiple compliance frameworks to accelerate adoption.
  
  

• Executive briefings and training for security, development and compliance teams.
  
  

• Influence over the roadmap as EnforceAuth continues to expand capabilities.
  
  

Enterprises interested in early access can sign up at EnforceAuth.com.

About EnforceAuth

EnforceAuth is a pioneer in AI‑native security. Headquartered in San Diego, the company builds decision‑centric authorization platforms that govern human and machine actions in real time. EnforceAuth’s leadership has decades of experience in policy‑as‑code, IAM and enterprise security.