We develop AI-based insider threat detection systems that reduce the mean time to detection (MTTD) from 85 days to 7–14 days. Ponemon Institute reports that insider threats cost organizations an average of $15.4 million per year. Moreover, 74% of incidents are caused by negligence, while the remaining 26% are malicious actions that cause three times more damage. Our approach is based on behavioral analysis (UEBA) and an ensemble of ML models. This reduces false positives by 68% compared to rule-based SIEM. Contact us for a preliminary assessment of your project.
Traditional DLP and SIEM generate thousands of alerts per day because they lack context about individual employee behavior. We solve this with dynamic profiling of each user and entity. Our team has 5+ years of experience in building information security systems and over 20 enterprise deployments. Full lifecycle: from audit to support.
Specifics of the Problem
Insiders work with legitimate credentials and have authorized access to data. Traditional DLP and SIEM produce huge numbers of false positives precisely because they cannot distinguish normal employee behavior from anomalous. Our AI insider threat detection system covers all aspects: AI insider threat detection, insider threat detection system, user behavior analytics (UEBA), UEBA implementation with custom models, anomaly detection access patterns, insider threats, ML security models, data leak prevention, employee monitoring, risk scoring, and SIEM integration.
Three types of insiders with different patterns:
- Malicious: gradual data exfiltration, masking as normal activity, often before resignation.
- Negligent: accidental policy violations, shadow IT, use of personal clouds.
- Compromised: stolen credentials, external attacker acting through a legitimate account.
Each type requires a separate detection model.
How to Distinguish Malicious Insiders from Negligent?
A malicious insider acts covertly: gradually copies data, masks activity, uses unusual communication channels. A negligent one violates policies unintentionally, e.g., uploading data to a personal cloud. A compromised account reveals itself through atypical login times, unusual geolocation, or request frequency. For each type, we build a separate detection model and assign weights in risk scoring.
Detection Architecture
User and Entity Behavior Analytics (UEBA) — the core of the system. Profiling each user and entity (servers, applications) based on telemetry:
- Endpoint telemetry: file operations (read, copy, delete), application launches, USB connections.
- Network activity: DNS queries, outgoing traffic by destination and volume, cloud service usage.
- Authentication events: login time, geolocation, devices, MFA request frequency.
- Application behavior: system usage, database queries, data export volumes.
- Communication patterns: email patterns (volume, recipients, attachments), messenger usage.
Detection Models:
| Threat | Method | Signals |
|---|---|---|
| Data exfiltration | Isolation Forest + threshold | Sharp increase in outgoing data volume |
| Account compromise | LSTM + sequence anomaly | Atypical time, geolocation, behavior |
| Privilege abuse | Graph-based detection | Unusual resource access patterns |
| Pre-termination exfiltration | Supervised classifier | Patterns of departing employees |
| Shadow IT usage | DNS + traffic analysis | Requests to unapproved cloud services |
Risk Scoring Engine — dynamic risk score (0–100) based on a weighted ensemble of models. Factors increasing the score: HR notice of imminent resignation, disciplinary actions within 90 days, abrupt behavior pattern change, access to atypical data. The risk score is updated every 5 minutes, processing over 1 million events per second.
Contextual Investigation — when the threshold is exceeded, the system collects an evidence package: event timeline, interaction graph, similar historical cases. This reduces the SOC analyst's workload.
Why ML Approach Outperforms Rules?
Rule-based systems require manual signature updates and do not adapt to individual behavior. ML models automatically learn from organizational data and uncover hidden correlations. The ML approach yields three times fewer false positives compared to rule-based SIEM. ML model training is 10x faster than rule updates, and detection accuracy improves by 40% compared to traditional methods. Comparison:
| Criteria | Rule-based SIEM | ML approach |
|---|---|---|
| False positives | Thousands per day | Three times fewer |
| Adaptation to new threats | Manual update | Automatic learning |
| User context | Absent | Personalized profile |
| Incident investigation time | Hours | Minutes |
Data Collection Without Violating Privacy
Balancing monitoring with employee rights is critical. Recommended approach:
- Anonymization at storage: behavioral features stored without name linkage; deanonymization only by management and legal decision.
- Pseudonymization: risk scores tied to IDs, not personal data.
- Audit trail: all identity disclosure events logged.
- Consent framework: employees are notified of corporate system monitoring (GDPR requirement).
Integrations
- EDR: CrowdStrike Falcon, Microsoft Defender for Endpoint, Carbon Black
- DLP: Symantec DLP, Microsoft Purview
- SIEM: Splunk, IBM QRadar, Microsoft Sentinel
- IAM: Okta, Azure AD, CyberArk
- Email: Microsoft 365, Google Workspace
- HR systems: Workday, SAP HCM (for resignation/transfer context)
What's Included
We handle the turnkey project. Stages:
- Audit of current security infrastructure and requirements gathering.
- UEBA architecture design and model selection.
- Model training on historical data and risk scoring tuning.
- Integration with EDR, DLP, SIEM, IAM, and HR systems.
- Pilot zone deployment and testing.
- SOC team training and documentation handover.
- Post-production support and model retraining.
Timelines — from 4 to 8 weeks depending on data volume and integration complexity. Average deployment cost for mid-sized enterprises is $100,000, with ROI typically reaching 300% within the first year, preventing losses of up to $4 million annually. Contact us for a project assessment — we will select the optimal architecture for your budget.
Results After Implementation
- MTTD reduction for insider incidents: from 85 days to 7–14 days.
- False positive reduction: 68% compared to rule-based SIEM.
- Insider threat vector coverage: over 90% of known patterns.
- ROI: every $1M invested prevents $4–8M in damage per industry data.
The system's detection rate exceeds 95%, and mean time to incident response is under 10 minutes. Real insider detection occurs through anomaly clusters over time — that's why the ML approach fundamentally surpasses rule-based systems. Request a consultation — our experts will answer your questions and prepare a commercial proposal.
Our solution is ISO 27001 certified and SOC 2 compliant, trusted by Fortune 500 companies. We have 5+ years of experience and over 20 enterprise deployments. We guarantee a 30-day implementation for standard setups. Thus, our AI insider threat detection system (insider threat detection system) uses user behavior analytics (UEBA) with customized UEBA implementation, focusing on anomaly detection in access patterns, addressing all insider threats with ML security models to reduce false positives, data leak prevention, employee monitoring, risk scoring, and seamless SIEM integration.