Artificial intelligence is rapidly becoming embedded in enterprise applications and workflows, operational environments, and critical infrastructure—and with it comes a new class of security risks. Large language models (LLMs), AI assistants, and autonomous agents increasingly interact with internal data sources, APIs, and enterprise systems, creating new attack surfaces that organizations must secure.

To help organizations understand these risks, MITRE introduced the Adversarial Threat Landscape for Artificial-Intelligence Systems (ATLAS) framework. Similar to MITRE ATT&CK, ATLAS catalogs adversarial tactics and techniques used to target AI systems. Mapping security controls to ATLAS helps organizations evaluate how effectively their defenses address emerging AI threats.

As AI models and autonomous agents move into production environments, they create new risk scenarios that extend beyond traditional IT threats. Because AI systems often operate with broad access to data, APIs, and infrastructure, strong privilege control and containment become essential. Securing these environments requires clear enforcement boundaries around how models and agents access, process, and transmit information.

Several security challenges consistently appear as organizations deploy AI systems into production environments. Xage addresses some of the most critical AI-specific risk scenarios:

• Containing Rogue or Compromised AI Agents: Limiting the impact of rogue agents or compromised LLMs by preventing them from accessing or affecting systems beyond their authorized scope, while continuously monitoring their behavior to detect and stop suspicious activity.
• Preventing AI-Driven Privilege Escalation: Stopping AI models or agents from escalating privileges or gaining unauthorized access to sensitive systems and data.
• Blocking AI-Enabled Data Exfiltration: Restricting unwanted data access and preventing potential data exfiltration through privileged AI components, APIs, or service accounts.

Addressing these risks requires strict identity enforcement, least-privilege access controls, and clearly defined security boundaries around AI systems.

Mapping Xage to MITRE ATLAS

Xage mapped its Fabric platform to the MITRE ATLAS matrix to evaluate how its capabilities mitigate AI attack techniques. The Xage Fabric Platform delivers technical controls across the MITRE ATLAS stack, helping organizations secure AI systems throughout the attack lifecycle. By aligning platform capabilities directly with ATLAS tactics and techniques, organizations can better understand how their security architecture addresses real-world AI attack scenarios.

The analysis shows strong alignment between Xage’s architecture and the adversarial behaviors defined in MITRE ATLAS. Xage provides coverage for more than two-thirds of the techniques identified in the framework, spanning a broad portion of the AI attack lifecycle.

In particular, the platform delivers comprehensive protection across several high-impact tactic categories, including privilege escalation, credential access, lateral movement, collection, command and control, and exfiltration.

A deeper look at the full technique mapping and heatmap analysis is available in the MITRE ATLAS brief, which details how Xage capabilities align to individual tactics and techniques across the framework.

Zero Trust for AI Systems

Xage secures AI environments by applying core Zero Trust principles to how AI systems access data, services, and infrastructure.

• Identity-centric access control ensures that users, services, and AI components must authenticate before accessing systems or data.
• Least-privilege policies restrict what AI models and agents can access, reducing the risks associated with over-permissioned systems.
• Segmentation isolates AI infrastructure components to prevent attackers from moving laterally between systems.
• Continuous monitoring and enforcement provide visibility into AI activity and help detect suspicious behavior.

Together, these controls ensure that AI systems operate within clearly defined security boundaries while reducing the risk of unauthorized access, privilege abuse, and data exposure.

As organizations continue deploying AI across enterprise environments, aligning security controls to frameworks like MITRE ATLAS provides a practical way to understand and mitigate emerging threats. Applying Zero Trust principles to AI systems enables organizations to deploy these technologies with greater confidence while maintaining control over access to critical data, infrastructure, and services.