Closing the Sim-to-Real Gap: An Evaluation Framework for Autonomous Cyber Defense Configuration of Commercial EDR

Researchers developed the first evaluation framework for autonomous AI defense agents operating within commercial endpoint detection and response (EDR) systems, revealing critical gaps between simulation environments and real-world enterprise security. Testing with Microsoft Defender XDR and LLM-based agents uncovered that commercial EDR telemetry is optimized for human analysts rather than benchmarking, creating attribution challenges and unpredictable autonomous system behavior.

The cybersecurity industry faces a significant blind spot as commercial EDR systems evolve from rule-based tools into autonomous AI-driven platforms. This research addresses a fundamental problem: existing evaluation methodologies rely on either simulated environments or open-source tools that fail to capture the complexity of proprietary enterprise security systems. The study's findings expose three critical issues that directly impact how organizations deploy and trust autonomous defense systems.

The shift toward autonomous EDR represents a broader industry trend where vendors embed AI decision-making into security products, reducing operator control while increasing complexity. Commercial systems like Microsoft Defender XDR now make vendor-specific security decisions independently, creating a new category of "black-box autonomous tools" that traditional security benchmarking cannot adequately evaluate. This gap matters because organizations deploying defense agents must now contend with two layers of autonomous decision-making: their own AI agents and the EDR's built-in autonomous components.

The research's key findings have immediate implications for enterprise security teams and security vendors. Commercial EDR telemetry design prioritizes Security Operations Center workflows—human-readable alerts and contextual information—rather than machine-readable attribution for automated benchmarking. The inability to clearly separate which security actions originated from the defense agent versus the EDR's autonomous systems fundamentally undermines evaluation reliability. Additionally, the EDR's behavior itself fluctuates during testing windows, introducing non-deterministic factors that simulation-based evaluations never encounter.

Looking ahead, this work establishes methodology for a critical evaluation challenge. Security vendors must redesign telemetry systems to support automated benchmarking, while enterprises need standardized frameworks to evaluate autonomous defense agents in production-like conditions. This research provides the foundation for more rigorous AI security tool evaluation.

• →Commercial EDR systems exhibit autonomous behavior that cannot be evaluated through simulation or open-source alternatives alone.
• →EDR telemetry engineering prioritizes human analyst workflows, creating measurement challenges for automated defense agent benchmarking.
• →Distinguishing between defense agent actions and autonomous EDR actions requires explicit per-policy attribution mechanisms.
• →Black-box autonomous tools demonstrate variable behavior during evaluation windows, introducing non-deterministic factors into security testing.
• →Enterprise security evaluation methodology requires fundamental restructuring to account for multi-layer autonomous systems in modern EDR platforms.