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AI Research

One of the world's leading AI researchers behind the technology. Enterprise cybersecurity experts who has sold to Fortune 100 companies. Together, their work spans since 2006, grounded in published, peer-reviewed AI research.

Two decades of global research delivering market-leading maturity.  Leading AI research.
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A detection engine modeled on the brain's predictive error-correction loop.

It runs on PECNET, a predictive neural network that forecasts and corrects its own errors in real time.

Independently validated in peer-reviewed literature, including IEEE Access work on error-compensated wavelet neural networks, it is the foundation of CYBERDROID Neural Detection.

15
Algorithm families
in production
3
Patent-protected
architectures
241K+
Endpoints protected
in the field
Cortical coding · sparse activation across L1–L6

180+ algorithms across 15 families, from classical statistics to frontier mathematics.

Each family targets a distinct adversary behavior, fusing into attack-chain narratives with no single-method blind spots.

Adaptive Learning
01 / 15
EWMA Z-Score BaselinesRare Transition SequencesOnline Changepoint Detection
Deep Learning
02 / 15
AutoencodersVariational AutoencodersTemporal Convolutional NetworksTransformer Sequence Anomaly
Graph Intelligence
03 / 15
Graph Neural NetworksLink PredictionCommunity Detection DriftDynamic Graph Change-Point
Anomaly Detection
04 / 15
Isolation ForestLocal Outlier FactorOne-Class SVMHDBSCAN
Statistical Methods
05 / 15
Mahalanobis DistancePCA Subspace ResidualsGaussian Mixture ModelsKernel Density Estimation
Drift & Shift Detection
06 / 15
KL-DivergenceCUSUMWasserstein DistancePopulation Stability Index
Sequence Analysis
07 / 15
Variable-Order MarkovHidden Markov ModelsSelf-Supervised EmbeddingsGrammar-Based Automata
Causal & Bayesian
08 / 15
Causal Discovery (PC / FCI)Granger CausalityBayesian ChangepointDempster-Shafer Fusion
Frontier Mathematics
09 / 15
Topological Data AnalysisPath SignaturesDiffusion MapsNeural ODE
Advanced Theory
10 / 15
Optimal TransportStein DiscrepancySheaf ConsistencyTropical Geometry

Five more proprietary families under NDA, including identity drift, kernel-level egress, and cross-product attack-chain reconstruction.

The science, in production.

Cortical coding and 180+ algorithms aren't lab curiosities. Two surfaces put them in front of SOC teams daily.

Cyberdroid Detection

The detection engine: cortical coding at line rate.

Cortical coding in production. CYBERDROID fuses 11 telemetry classes across all 15 algorithm families into one actor-centric reasoning graph.

  • Sparse activation: anomalies in seconds, not minutes
  • Cross-instrument correlation across 11 telemetry classes
  • Local AI inference, zero cloud calls
CYBERDROID Atlas: host risk graph and correlation view
Cyberdroid SOC

The analyst surface: investigations, not alert queues.

Where detection meets the operator. Every event arrives as a structured investigation: actor context, evidence chains, recommended actions, not a queue line.

  • Living handoff with priority-ordered actions and full evidence
  • Auditable reasoning: hypotheses, positions, tie-breaks
  • 99.3% machine-cleared escalation
CYBERDROID SOC Analyst: living handoff and reasoning chain

Every claim here is anchored in publicly verifiable research.

A five-year program in predictive error-compensated architectures behind CYBERDROID Neural Detection. Full bibliography under NDA.

01

High-Performance Time Series Prediction With Predictive Error Compensated Wavelet Neural Networks.

Ustundag, B. B., & Kulaglic, A.

IEEE Access, 8, 210532–210541.

02

Stock Price Prediction Using Predictive Error Compensation Wavelet Neural Networks.

Kulaglic, A., & Ustundag, B. B.

Computers, Materials & Continua, 68(3), 3577–3593. 33% RMSE improvement over LSTM.

03

Predictive Error Compensating Wavelet Neural Network Model for Multivariable Time Series Prediction.

Kulaglic, A., & Ustundag, B. B.

TEM Journal, 10(4), 1955–1963. Extends PEC-WNN to multivariable input.

2021 · Multivariate doi.org/10.18421/TEM104-61
04

Improvement in Prediction Performance Using Predictive Error Compensated Neural Networks.

Kulaglic, A., & Ustundag, B. B.

Springer Lecture Notes in Networks and Systems. Ongoing program investment through 2024.

Request a technical deep dive.

A 60-minute session with the engineers who built it: architecture, the algorithms, a live investigation on your telemetry, and the bibliography under NDA.

Under mutual NDA · Engineer-led session · No sales overlay

ONE PLATFORM. EVERYTHING COVERED.
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Website content is for informational purposes only and does not constitute a warranty or guarantee of specific security outcomes or performance.
Capabilities and limits vary by plan and may change as the platform evolves. Some features and pricing are in limited release.
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