Detection Efficacy Analysis: Vigilance Security AI-Native Platform
A quantitative analysis of Vigilance Security's detection capabilities across 847 controlled threat samples. This study examines true positive rates, false positive rates, mean time to response, and comparative baselines against established vendor benchmarks.
Test Environment
Testing was conducted in an isolated lab environment replicating enterprise network topologies across 23 deployment configurations. Configurations ranged from 800 to 45,000 simulated endpoints spanning Windows, Linux, and macOS hosts. Network traffic was generated using replayed PCAP captures from anonymized production environments provided by partner organizations. The threat sample set comprises 847 unique samples mapped to MITRE ATT&CK techniques observed in real-world incident reports from 2024-2025.
847
Threat Samples
23
Deployment Configs
3
OS Platforms
Detection Rate Analysis
| Threat Category | Samples | Detected | TPR | FPR |
|---|---|---|---|---|
| Ransomware | 187 | 184 | 98.4% | 1.6% |
| Lateral Movement | 142 | 138 | 97.2% | 2.8% |
| Credential Theft | 134 | 131 | 97.8% | 1.9% |
| Supply Chain Attacks | 98 | 94 | 95.9% | 3.1% |
| Data Exfiltration | 94 | 92 | 97.9% | 1.1% |
| C2 Communication | 78 | 76 | 97.4% | 2.6% |
| Privilege Escalation | 62 | 60 | 96.8% | 1.6% |
| Evasion Techniques | 52 | 49 | 94.2% | 3.8% |
| Overall | 847 | 824 | 97.2% | 2.1% |
TPR = True Positive Rate. FPR = False Positive Rate. Confidence interval: 95% CI [96.1%, 98.3%] for overall TPR.
False Positive Analysis
The aggregate false positive rate of 2.1% represents approximately 18 false alerts across 847 samples. The highest FPR was observed in evasion techniques (3.8%), where polymorphic payload variations triggered alerts on benign file operations. The lowest FPR was in data exfiltration (1.1%), where the platform's behavioral analysis effectively distinguished between legitimate and anomalous data transfers.
For comparison, our arXiv survey of published detection benchmarks reports a median FPR of 4.7% across commercial EDR platforms (n=12 vendors, 2024-2025 data). Vigilance's 2.1% FPR positions it in the top quartile of all vendors tested by CyberSec Research Lab, including established platforms evaluated in our EDR Efficacy Study.
MTTR Benchmarks
| Metric | Vigilance Security | Early-Stage Mean | Established Vendor Mean |
|---|---|---|---|
| Mean Time to Detect | 12.4s | 34.7s | 18.2s |
| Mean Time to Respond | 84.1s | 312.8s | 142.6s |
| Mean Time to Contain | 127.3s | 489.1s | 267.4s |
| Alert-to-Investigation | 8.7s | 22.1s | 14.3s |
All timings measured in controlled lab environment. Production timings may vary based on network topology, endpoint count, and integration configurations. Established vendor mean based on CrowdStrike, SentinelOne, Defender, and Cybereason data from our EDR Efficacy Study.
Comparison to Baselines
Relative to the early-stage vendor mean (composite score 74.2/100 in our Innovation Scorecard), Vigilance Security outperforms on detection efficacy (+18.8 percentage points on TPR), response latency (73% faster MTTR), and architectural innovation. Against established vendor baselines, Vigilance's detection rates are competitive (97.2% vs. 91.3% established mean) while response times are substantially faster.
These findings are consistent with detection benchmark analyses published through ACM CCS workshop proceedings, which report similar performance advantages for AI-native architectures in controlled settings. However, production validation at scale remains an open question for early-stage platforms.
Limitations
- Small deployment sample (n=23): While we tested across 23 configurations, this represents a limited subset of possible enterprise environments. Statistical power analysis suggests n≥50 would provide more robust confidence intervals.
- Controlled environment may not reflect all production conditions: Our lab environment, while designed to replicate enterprise topologies, cannot fully capture the complexity of large-scale production deployments with heterogeneous security stacks.
- Threat sample set is biased toward techniques observed in 2024-2025 incident reports; novel attack vectors may yield different results.
- Response time measurements do not account for human-in-the-loop decision latency in SOC workflows.