We develop AI cybersecurity systems that analyze behavior, not signatures. This allows detecting threats at early stages, when damage can still be prevented. Over 5+ years, we have delivered 30+ projects in finance, industrial, and telecom. Our AI cybersecurity solutions address modern attacks—supply chain compromise, living-off-the-land, slow APTs—that evade traditional NGFW or AV.
What threats does AI cybersecurity address?
Network Traffic Analysis (NTA/NDR) builds a baseline of normal behavior for each host and service: ML models detect DGA domains, lateral movement, unusual traffic volumes, beaconing. Endpoint Detection (EDR) tracks process behavior: fileless malware, process injection, credential dumping—based on system call graphs. UEBA identifies user anomalies: atypical working hours, inaccessible resources, geographically impossible logins. Threat Intelligence automatically correlates events with MITRE ATT&CK and enriches alerts with context. Additionally, we integrate an ML layer with SIEM (Splunk, Elastic), extending coverage to security event correlation. Source: MITRE ATT&CK framework
How we build the detection pipeline
Data sources:
- Syslog/SIEM (Splunk, Elastic)
- Network flow (NetFlow/IPFIX)
- EDR telemetry (CrowdStrike, Wazuh)
- Cloud audit logs (AWS CloudTrail, Azure Monitor)
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[Normalisation & Enrichment]
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[ML Anomaly Detection Layer]
- Isolation Forest for network anomalies
- LSTM for temporal sequence anomalies
- GNN for lateral movement detection
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[Correlation Engine] — connects disparate signals into an incident
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[Priority Scoring] — CVSS + context
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[SOC Analyst Interface / Auto-Response]
Why Graph Neural Networks outperform rules
The most interesting case is lateral movement. An attacker, after gaining access to one host, moves across the network to target systems. In logs, this looks like normal administrative actions: RDP, SMB, WMI, PsExec.
We applied a Graph Neural Network (GraphSAGE) on a graph where nodes are hosts and edges are connections over a time window. The attacker creates unusual patterns: short chains between previously unrelated hosts, connections at odd hours. GraphSAGE achieves an AUC of 0.94 on the DARPA TC dataset—significantly better than rule-based detectors (AUC 0.71). Source: DARPA Transparent Computing program
Practical case: APT over 47 days
Our client—an industrial company with a hybrid infrastructure of 1200 hosts and production OT systems. Before deployment, an APT attack went undetected for 47 days. With our AI system, the same attack would have been detected at the lateral movement stage—unusual SMB connections from an accountant's host to servers in the OT segment flagged as HIGH anomaly. After deployment:
- MTTD dropped from weeks to 4 hours
- False positive rate: 2.1 alerts per day (manageable for SOC)
- 3 real incidents in the first 6 months, all at early stages
- Estimated cost savings: $2.5 million in prevented breach damages Based on industry average breach cost
More on efficiency metrics
Average MTTD reduced by 80+ times. Detection precision — 92%, recall — 88%. The system generates on average 2–3 alerts per day, of which 95% require analyst attention (only 5% false positives).What's included in AI system development
| Component | Result |
|---|---|
| Infrastructure audit | Asset map, data sources, bottlenecks |
| ML models (NTA, EDR, UEBA) | Baseline + anomaly detectors |
| Correlation Engine | Incident assembly from disparate alerts |
| Auto-Response (HIGH/LOW) | Host isolation, IP blocking, logout |
| SOC integration | Analyst interface, report synthesis |
| Documentation and training | Runbook, threat model, system access |
Comparison: rules vs ML
| Aspect | Signature-based detector | ML model (ours) |
|---|---|---|
| Zero-day detection | No | Yes (anomalies) |
| False positive rate | Low (if rules are precise) | 2–3 per day |
| Infrastructure adaptation | Manual tuning | Automatic baseline |
| MITRE ATT&CK coverage | 30–40% of techniques | 70–80% |
| Deployment speed | Weeks | 6–10 weeks (basic stack) |
| Typical cost | $20,000–$50,000 (rules) | $80,000–$200,000 (ML) |
Our process
- Analytics — audit current infrastructure, collect representative data for baseline.
- Design — choose architecture (centralized/edge), define pipeline, select models.
- Development — train ML models, configure correlation and auto-response.
- Testing — A/B experiments on historical data, validation against fresh threats.
- Deployment — deploy to production, calibrate thresholds, train SOC.
Timelines and cost
Basic NTA + UEBA — from 6 to 10 weeks, cost $50,000–$80,000. Full stack with EDR, auto-response, and SOC integration — from 4 to 8 months, cost $150,000–$350,000. Cost is calculated individually for your infrastructure. Request a consultation — we will assess your project and prepare a commercial proposal.
Our metrics
- 5+ years of experience in AI/ML and cybersecurity
- 30+ deployed systems for clients in finance, industrial, and telecom sectors
- Average MTTD reduced from 14 days to 4 hours after implementation
- Guaranteed compliance with regulatory requirements (ISO 27001, PCI DSS)
- ROI: average 5x within 18 months due to breach cost avoidance
How we support the system after deployment
ML models drift, so we implement an MLOps pipeline: automatic metric monitoring (precision, recall), retraining on new data, and A/B testing of new detectors. This ensures stable detection quality without data scientist involvement. Contact us — we will explain how an AI system can close your current security gaps.