Intelligent real-time threat detection.
We build AI-powered security monitoring systems that use machine learning for anomaly detection, behavioural analysis, and automated incident response — catching threats that traditional tools miss.
from sklearn.ensemble import IsolationForest
import numpy as np
class AnomalyDetector:
def __init__(self, contamination=0.001):
self.model = IsolationForest(
n_estimators=200,
contamination=contamination,
random_state=42
)
def train(self, normal_behaviour_data):
self.model.fit(normal_behaviour_data)
def detect(self, event_features):
score = self.model.decision_function([event_features])
prediction = self.model.predict([event_features])
return {
"is_anomaly": prediction[0] == -1,
"confidence": abs(score[0]),
"risk_level": self._classify_risk(score[0])
}
def _classify_risk(self, score):
if score < -0.5:
return "CRITICAL"
elif score < -0.3:
return "HIGH"
elif score < -0.1:
return "MEDIUM"
return "LOW"ML Anomaly Detection Engine
We train machine learning models on normal network and user behaviour patterns to detect anomalies in real time. Our models continuously learn and adapt, identifying zero-day threats and insider attacks that signature-based tools miss.
Tools & Frameworks We Engineer With
ML/AI
- Isolation Forest: Unsupervised anomaly detection for identifying outliers in high-dimensional security data
- LSTM Networks: Sequential pattern recognition for detecting unusual temporal behaviour in network traffic
- Transformer Models: Attention-based models for log analysis and natural language threat intelligence
- Federated Learning: Privacy-preserving model training across distributed environments without sharing raw data
SIEM
- Elasticsearch: Distributed search and analytics engine for real-time log ingestion and correlation
- Splunk: Enterprise security information platform with advanced search and reporting capabilities
- Sentinel: Cloud-native SIEM with built-in AI and integration with Azure security ecosystem
- Wazuh: Open-source security monitoring with host intrusion detection and compliance auditing
SOAR
- Cortex XSOAR: Enterprise SOAR platform with extensive integration library and case management
- Tines: No-code automation platform for building security workflows and response playbooks
- Shuffle: Open-source SOAR with visual workflow builder and community-driven integrations
- Custom Playbooks: Bespoke response automation tailored to your infrastructure and compliance requirements
Infrastructure
- Kafka: High-throughput event streaming for real-time security data pipeline processing
- Flink: Stream processing framework for complex event processing and windowed aggregations
- Kubernetes: Container orchestration for scalable, resilient deployment of detection services
- GPU Clusters: Accelerated compute infrastructure for model training and real-time inference
Who We Work With
Banking
Real-time fraud detection, transaction monitoring, and account takeover prevention using behavioural biometrics
Healthcare
Protected health information monitoring, HIPAA compliance enforcement, and medical device security
Critical Infrastructure
Operational technology monitoring, SCADA/ICS anomaly detection, and air-gapped network security
Cloud Providers
Multi-tenant security isolation, cross-account threat detection, and cloud-native workload protection
Government
National security threat intelligence, classified network monitoring, and advanced persistent threat detection
Telecom
Network traffic anomaly detection, DDoS mitigation, and signalling protocol abuse prevention
Our Delivery Lifecycle
We operate under a structured, predictable lifecycle. Click any step to inspect deliverables.
Threat Landscape Assessment
We map your attack surface, audit existing security tools, and identify detection gaps to build a prioritised threat model.
assessment:
attack_surface:
external_assets: 47
internal_segments: 12
cloud_accounts: 3
detection_gaps:
- lateral_movement
- credential_stuffing
- data_exfiltration
priority: criticalFrequently Asked
Our models achieve 99.7% detection rates with less than 0.1% false positives after the tuning phase. We use ensemble methods combining Isolation Forest, LSTM, and transformer models to catch different attack types. Models are continuously retrained on new threat patterns to maintain accuracy as attack techniques evolve.
Yes. We integrate with all major SIEM platforms (Splunk, Elasticsearch, Sentinel), EDR solutions (CrowdStrike, SentinelOne, Defender), firewalls, identity providers, and cloud security tools. Our SOAR layer acts as an orchestration hub connecting your entire security stack.
We implement a multi-layer approach: ML model threshold tuning during training, correlation-based validation that requires multiple signals before alerting, confidence scoring that prioritises high-fidelity alerts, and feedback loops where your SOC team's decisions automatically improve model accuracy.
We need 2-4 weeks of baseline network and user behaviour data to train initial models. Setup typically takes 6-8 weeks including assessment, model training, SIEM integration, and SOAR configuration. The system starts detecting threats from day one of deployment, with accuracy improving as models learn your environment.
Ready to detect threats with AI?
Get a threat detection architecture proposal with ML model recommendations and integration plan within one week.