Financial Services Firm Enhances Threat Detection using AI/ML-Based Detection
Client: Confidential Financial Services Organization (2,000+ Employees)
Industry: Financial Services and Investment Management
The Challenge
The client’s Security Operations Center (SOC) relied on traditional rule-based correlation within their SIEM platform to detect threats.
While effective for known attack signatures, this approach struggled with emerging, low-and-slow, or insider threats that did not follow predictable patterns.
The main challenges included:
- High false-positive rates from static correlation rules
- Limited detection of behavioral anomalies (e.g., credential misuse, data exfiltration)
- Manual tuning overhead for hundreds of SIEM rules
- Slow adaptation to new tactics, techniques, and procedures (TTPs)
The SOC team needed a smarter, adaptive detection framework capable of learning from historical data, identifying subtle deviations, and reducing analyst workload.
The Solution
NSecurity Consulting Inc (NCI) collaborated with the firm to design and implement an AI/ML-based threat detection framework, integrated into their existing SIEM and data lake environment.
The initiative focused on using machine learning models to enhance detection precision, prioritize alerts, and accelerate investigation workflows.
Step 1: Data Preparation and Feature Engineering
NCI began by analyzing existing SIEM and log data sources to identify valuable input features for machine learning models.
Custom features were engineered to detect abnormal user behavior, such as:
- Sudden logon from atypical geolocation
- Accessing unfamiliar systems or shares
- Off-hour data transfers
- Spikes in process creation or PowerShell execution
Step 2: Model Selection and Training
Several unsupervised learning models were evaluated, including Isolation Forest, K-Means clustering, and Autoencoders, to identify outliers representing potential security anomalies.
Using six months of historical log data, NCI trained and validated models in a controlled lab environment, focusing on metrics such as:
- Precision and recall for anomaly detection
- Reduction in false positive alerts
- Correlation accuracy across user and host entities
The final solution employed a hybrid detection pipeline, combining:
- Unsupervised ML models for anomaly detection
- Supervised classification models trained on known incidents
- Risk scoring logic to prioritize entities for analyst review
Step 3: Integration with SIEM and SOAR Platforms
Once models were operational, NCI integrated AI-driven detections directly into the client’s SIEM and SOAR systems.
This allowed:
- Automatic enrichment of alerts with ML-driven anomaly scores
- Contextual linking of related events into entity timelines
- Automated escalation and playbook execution when high-risk anomalies were detected
Alerts generated by the models were tagged with confidence scores, enabling analysts to focus on the most probable threats.
Step 4: Continuous Learning and Model Optimization
A feedback loop was established where analyst decisions (true positive or false positive) were fed back into the ML pipeline for retraining.
Weekly retraining cycles ensured that models adapted to evolving user behavior and seasonal variations in business activity.
Dashboards were developed to visualize model performance metrics, anomaly trends, and false positive rates.
Step 5: SOC Enablement and Knowledge Transfer
NCI conducted hands-on workshops for SOC analysts, focusing on:
- Interpreting AI/ML-driven alerts
- Investigating anomalies with context-based triage
- Tuning detection thresholds and retraining models
This empowered the SOC to manage the system autonomously and continuously evolve detection capabilities.
Key Benefits
- Smarter Detection: ML models identified anomalies beyond static rules.
- Reduced Alert Fatigue: AI-driven prioritization filtered out low-risk noise.
- Adaptive Security Posture: Continuous learning improved accuracy over time.
- Operational Efficiency: Analysts spent less time tuning and more time investigating.
- Future-Ready SOC: Foundation established for predictive and autonomous threat detection.
Client Testimonial
“The AI-driven use cases developed by NSecurity Consulting have revolutionized our SOC operations. We’ve dramatically reduced false positives and gained visibility into threats that traditional detections simply missed.” – SOC Director, Financial Services Firm