AI Agent Testing
The First Cyber Range Built for AI Agents
Building AI security agents has never been easier. Validating whether they actually work is the hard part. Test your agents in realistic enterprise environments — with the same infrastructure, attack scenarios, and evidence-based scoring used to evaluate human SOC analysts.
The Problem
Why Test AI Agents?
As organizations deploy AI for SOC operations, the critical question is not whether it works in a demo — it is whether it works when it matters.
Can It Actually Detect?
Synthetic benchmarks do not replicate production conditions. Test whether your AI agent can identify real attack patterns buried in realistic enterprise noise — Active Directory events, endpoint telemetry, and legitimate user activity.
Can It Respond Correctly?
Detection is only half the problem. Validate that your AI agent takes the right containment actions — disabling accounts, isolating hosts, blocking IPs — without causing operational disruption or false positive damage.
Does It Perform Under Pressure?
Real environments have noise, latency, and ambiguity. Evaluate AI agent performance under the same realistic conditions human analysts face — with evolving attack chains, concurrent benign activity, and time-sensitive escalation.
The Validation Gap
Anyone Can Build an Agent. Few Can Prove It Works.
No-code tools and pre-built connectors mean any team can ship a SOC agent in hours. But building is the easy part — knowing whether it actually performs under real conditions is what separates production-ready agents from expensive liabilities.
Building Is Now Zero-Engineering
Pre-built connectors, no-code studios, and plug-and-play logic apps mean anyone can assemble an investigation or triage agent. The barrier to entry has collapsed — but the barrier to quality has not.
Testing Requires Real Environments
Synthetic datasets and curated demos cannot validate agent performance. You need realistic enterprise infrastructure — Active Directory, SIEM data, network traffic, and authentic user behavior — to know if your agent works.
Evidence-Based Scoring Is the Answer
MTTD, MTTI, MTTC, MTTR — the same metrics used to evaluate human analysts. CymBytes gives you objective, reproducible evidence of agent performance that you can take to leadership, auditors, and customers.
Signal vs. Noise
Stop False Positives Before They Hit Production
AI agents generate alerts — but how many are real? Validate your AI in environments with authentic noise before it overwhelms your SOC.
Measure False Positive Rates
Living environments generate realistic user activity — email, web browsing, file operations, logins — that AI agents must learn to ignore. Measure exactly how many false alerts your AI produces under production-like conditions.
Validate True Detections
AI-driven attack simulations create real threats buried in authentic noise. Verify that your AI catches what matters — lateral movement, credential abuse, data exfiltration — without flagging normal business activity.
Precision & Recall Scoring
Get concrete metrics on your AI's detection accuracy. Track precision (how many alerts are real) and recall (how many threats are caught) across every lab session with audit-ready reports.
Test Before You Deploy
An AI agent that fires hundreds of false alerts is worse than no AI at all. Use CymBytes as your staging environment — validate accuracy, tune thresholds, and build confidence before going live.
No Special Treatment
Same Range, Same Metrics
AI agents are scored on identical MTTD, MTTI, MTTC, and MTTR metrics as human analysts. No synthetic benchmarks. No curated datasets. Real environments, real attacks, real scoring.
How quickly does the AI agent identify indicators of compromise? Measured identically to human analyst benchmarks — same attack, same noise, same clock.
Does the agent investigate deeply enough? Track how thoroughly and quickly it reconstructs attack chains, correlates events, and identifies root cause.
How fast does the agent neutralize the threat? Measure containment actions — account lockouts, network isolation, process termination — with precision and speed.
End-to-end resolution time from detection to full recovery. The definitive metric for autonomous security agent operational readiness.
One Platform, Two Benchmarks
CymBytes is the only platform where you can evaluate human analysts and AI agents on the exact same scenarios with the exact same scoring engine. No translation layer. No adjusted criteria. True apples-to-apples comparison.
Comparative Analysis
Human vs. AI Benchmarking
Compare AI agent performance against human SOC analyst baselines on identical scenarios. Objective, side-by-side evaluation with no bias.
Human Baseline Data
Compare AI agent performance against aggregated human SOC analyst performance on identical scenarios. Understand where AI excels and where it falls short.
Side-by-Side Evaluation
Run the same scenario simultaneously with human analysts and AI agents. Identical environments, identical attack chains, identical scoring — objective comparison.
Version Comparison
Track AI agent performance across development iterations. Run regression tests to ensure new model versions improve detection without sacrificing response quality.
Developer Experience
For AI Developers
Test your security AI in realistic enterprise environments before deployment. Integration-ready API, detailed performance reports, and automated regression testing.
Integration-Ready API
RESTful API for programmatic lab provisioning, scenario execution, and results retrieval. Integrate CymBytes directly into your AI agent development and CI/CD pipeline.
Detailed Performance Reports
Structured JSON reports with granular timing data, action logs, decision traces, and scoring breakdowns. Every action your agent takes is captured and analyzed.
Regression Testing
Automated test suites that run your AI agent against a battery of scenarios on every release. Catch performance regressions before they reach production.
Scenario Library
Growing library of enterprise attack scenarios — from commodity threats to APT campaigns. Each scenario is versioned, reproducible, and designed for consistent benchmarking.
Ready to benchmark your AI agents?
The first platform purpose-built for testing AI security agents in realistic enterprise environments. Same range, same metrics, real answers.