Astrix Named in the Gartner® Market Guide for Guardian Agents
We’re proud to announce that Astrix Security has been recognized as a Representative Vendor in the inaugural Gartner® Market Guide for Guardian Agents (February 2026), listed in the Agent Identity category alongside a select group of vendors managing identities and access for AI agents with zero-trust policies and governance.
This is Gartner’s first dedicated coverage of the Guardian Agent market, and we believe it marks a defining moment for enterprise AI security.
Why Guardian Agents Matter Now
AI agents are moving fast. According to the report, 17% of CIOs have already deployed AI agents, and another 42% plan to do so within the next year. Speed introduces risk. Most organizations are not yet equipped to manage it.
Gartner defines guardian agents as technologies that supervise AI agents, ensuring their actions align with intended goals and boundaries. They monitor, block risky behavior, and are evolving from passive oversight tools into autonomous enforcers of enterprise policy across clouds, platforms, and identity systems.
Gartner predicts that by 2029, independent guardian agents will eliminate the need for nearly half of incumbent security systems protecting AI agent activities in over 70% of organizations.
Where Astrix Fits
Astrix is recognized in the Agent Identity category, which covers vendors specializing in managing identities and access for AI agents using zero-trust principles. This aligns directly with our mission: securing AI agents and non-human identities (NHIs) at enterprise scale.
Every AI agent is, at its core, an identity. It holds credentials, accesses systems, takes actions, often without direct human oversight. Astrix gives security teams full visibility and control over these identities, from discovery and posture management to runtime enforcement and lifecycle governance.
The report highlights a key market trend: the deep integration of agent identity and access management with information governance. As Gartner notes, the traditional separation between identity and data governance is narrowing, and forward-looking organizations are managing these as integrated capabilities.
Gartner predicts that through 2029, over 50% of successful cybersecurity attacks against AI agents will exploit access control issues, using prompt injection as an attack vector. Astrix’s identity-centric approach directly addresses this risk by ensuring robust authentication, authorization, and risk-aware access policies for every agent.
What This Means for Enterprises
Three takeaways from the report that we think every security leader should have on their radar:
1. You can’t govern what you can’t see. Gartner recommends that agent discovery (finding every sanctioned and unsanctioned AI agent across your environment) is the most critical starting point. This is foundational to everything Astrix does.
2. Platform-native controls aren’t enough. No single cloud provider or AI platform can govern agents once they operate across environments. Gartner is clear: organizations need independent guardian agents that work across clouds, identity systems, and data platforms, free from vendor lock-in.
3. Agent identity is where security starts. By 2027, Gartner predicts that over 70% of AI agent identity providers will classify the sensitivity of data agents interact with as part of granting and scoping access rights. Least-privilege, just-in-time access for AI agents isn’t a nice-to-have. It’s becoming the standard.
Looking Ahead
The Guardian Agent market is in its early stages but moving fast. Gartner forecasts that by 2028, organizations will allocate 5 to 7% of their total agentic AI spend to guardian agent solutions, up from less than 1% today.
As the market matures, Astrix’s identity-centric foundation (discovery, access governance, and runtime enforcement) positions enterprises to scale agentic AI without outpacing their security controls.
Read the full Gartner Market Guide for Guardian Agents for the complete vendor landscape, evaluation criteria, and strategic planning assumptions.
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