Dropzone AI

What is it?

Dropzone AI is an autonomous alert investigation system for security operations centers. It receives alerts from detection systems (SIEM, EDR, cloud security, email security), investigates each alert by querying available log sources and security tools, and produces a disposition: dismiss as false positive or escalate for human review.

The system connects to existing security infrastructure via API. It does not require predefined playbooks; investigation logic is generated dynamically based on alert context and available data sources.

Why did they build it?

SOC alert volume exceeds human analyst capacity. The bottleneck is not detection of sophisticated attacks; most detection tools identify true positives adequately. The bottleneck is the volume of false positives requiring human triage.

Dropzone addresses this by autonomously dismissing false positives with high accuracy, reducing the volume of alerts requiring human attention. The stated goal is accuracy at or above the level of a typical human Tier 1 analyst.

How does the system minimize false negatives?

Large language models hallucinate. Dropzone acknowledges this as an inherent property of the technology that cannot be eliminated entirely. The system architecture is designed to control and minimize hallucination impact:

  • Task decomposition: Alert investigation is broken into discrete cognitive steps, each designed to be simple enough that error probability is low. A typical investigation involves approximately 100 separate LLM invocations.
  • Multi-model validation: Different models with different configurations critique each other’s outputs. Disagreement triggers escalation rather than autonomous dismissal.
  • Escalation bias: When uncertainty exists, the system escalates to humans rather than dismissing.

The system can be benchmarked against human analysts. MSSP customers run parallel evaluations during proof-of-concept: 100 alerts through Dropzone, 100 through human analysts, results compared.

How does the system learn organizational context?

Generic alert investigation produces generic results. Dropzone builds context about each deployment environment: organizational policies, expected behavior patterns, risk tolerance, asset criticality.

The system is designed for coachability, accepting feedback and adjusting behavior. A less sophisticated model that responds to organizational feedback outperforms a more capable model that ignores environmental context.

When the system observes recurring false positives from specific detection rules, it generates recommendations for detection rule modifications.

What are the data requirements?

The system requires log visibility to investigate alerts. If an AWS security alert triggers but no AWS logs are available in the SIEM, investigation cannot proceed. Autonomous investigation does not solve visibility gaps.

Alert investigation quality depends on log completeness. Organizations with partial logging will receive partial investigation capability.

What are the deployment patterns?

  • Tier 1 replacement: All alerts route to Dropzone first. Dismissed alerts are logged; escalated alerts go to human analysts.
  • After-hours coverage: Small teams (2-3 analysts) use the system to maintain coverage during non-business hours.
  • Volume reduction: Organizations report 75%+ reduction in manual triage time.

What are the limitations?

Dropzone investigates alerts from existing detection systems. It does not generate detections. Organizations still require SIEM, EDR, and other detection infrastructure to produce the alerts that Dropzone investigates.

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Sources

Disclosure: Patrick Gray is an advisor to Dropzone AI.