Back to Impact Studies

BRICA: Optimizing Threat Intelligence with AI

Client Overview

BRICA Business Risk Intelligence BV is a Dutch leader in cybersecurity solutions, providing cutting-edge threat intelligence to organizations worldwide. We partnered with BRICA to revolutionize their threat intelligence capabilities, focusing on developing an advanced system that delivers fast, actionable insights on potential internal and external threats while ensuring compliance with EU regulations.

40%

Detection Speed

Faster threat detection

30%

Intelligence Delivery

Boost in actionable insights

200+

Industry Coverage

Expanded industry reach

65%

Response Time

Faster threat response

Challenge

BRICA faced several significant obstacles in their mission to provide comprehensive security intelligence:

  • Detection Latency: Traditional methods created unacceptable delays in threat identification
  • Actionable Intelligence Gap: High volumes of alerts without sufficient context for prioritization
  • Limited Domain Coverage: Manual classification processes constrained detection capabilities
  • Scalability Limitations: Expanding coverage required significant resource allocation
  • Integration Complexity: Dark web monitoring tools operated in isolation from other intelligence sources
  • Analyst Bottlenecks: Human analysts became overwhelmed during surge periods, creating response delays

Solution: AI-Powered Threat Intelligence Automation System

We implemented a comprehensive solution that transformed BRICA's intelligence capabilities:

1. Advanced Threat Classification Engine

  • Multi-Domain Categorization
  • Pattern Recognition
  • Contextual Analysis
  • Anomaly Detection
  • Continuous Learning

2. Real-Time Alert and Response Framework

  • Priority-Based Alerting
  • Contextual Enrichment
  • Response Automation
  • Escalation Intelligence
  • Visual Analytics

3. Dark Web Integration Module

  • Dark Web Scraping
  • Credential Monitoring
  • Source Credibility Assessment
  • Linguistic Analysis
  • Temporal Correlation

4. Industry-Specific Threat Intelligence

  • Sector-Specific Models
  • Regulatory Compliance Monitoring
  • Supply Chain Intelligence
  • Vertical-Specific Indicators
  • Cross-Industry Correlation

Data Protection & Security Architecture

Threat Intelligence Data Handling

  • • Encrypted storage of sensitive threat indicators
  • • Secure multi-tenant data isolation
  • • Access controls based on need-to-know principles
  • • Automated data classification and tagging

Dark Web Data Compliance

  • • Ethical collection practices for dark web intelligence
  • • Anonymization of personally identifiable information
  • • Secure handling of compromised credentials
  • • Compliance with data protection regulations

GDPR & NIS2 Compliance

The threat intelligence platform implements GDPR requirements for processing security-related personal data and aligns with NIS2 Directive requirements for cybersecurity risk management and incident reporting. All data processing activities maintain appropriate technical and organizational measures.

Implementation Process

1. Intelligence Assessment

Comprehensive evaluation of existing threat intelligence capabilities and identification of gaps in coverage and detection speed.

2. Data Integration

Integration of multiple threat intelligence sources including surface web, deep web, dark web, and proprietary security feeds.

3. Algorithm Development

Development of custom machine learning models for threat classification, pattern recognition, and anomaly detection.

4. Alert Engineering

Design and implementation of priority-based alerting system with contextual enrichment and automated response capabilities.

5. Industry Expansion

Development of sector-specific threat intelligence models covering 200+ industries with tailored detection parameters.

6. User Experience Design

Creation of intuitive dashboards and visualization tools for security analysts to efficiently process threat intelligence.

7. Iterative Testing

Rigorous testing with real-world threat scenarios and continuous refinement based on analyst feedback and performance metrics.

8. Production Deployment

Phased rollout with continuous monitoring, performance optimization, and ongoing model training for improved accuracy.

AI System Governance & Transparency

Threat Classification Explainability

  • • Transparent scoring methodology for threat severity
  • • Documented decision logic for alert prioritization
  • • Audit trails of AI-driven classifications
  • • Model performance metrics and validation

Human-AI Collaboration

  • • Security analyst review of high-priority alerts
  • • Feedback mechanisms for model improvement
  • • Manual override capabilities for critical decisions
  • • Continuous monitoring of false positive rates

EU AI Act Readiness

As a cybersecurity AI system, the platform maintains comprehensive documentation of training data, model architecture, and risk assessment procedures. Regular audits ensure alignment with emerging AI governance requirements for high-risk systems.

Results

40%
Faster Threat Detection
Compared to previous systems
30%
Increase in Actionable Intelligence
Delivery to security teams
200+
Industries Covered
Expanded coverage reach
65%
Faster Response Time
To emerging threats
78%
Reduction in False Positives
Improved alert accuracy
3.2x
Dark Web Intelligence
Extraction increase
94%
Analyst Satisfaction
With enhanced system
22
Zero-Day Vulnerabilities
Identified in first six months

Compliance & Security Outcomes

Zero
Data breaches or security incidents
100%
GDPR compliance for threat data processing
78%
Reduction in false positive alerts
ISO 27001
Certified information security management
Less than 15 minutes
Average critical threat alert response time

Regulatory Context

Cybersecurity intelligence platforms in the EU operate under multiple regulatory frameworks:

  • GDPR: Requirements for processing personal data in security contexts with appropriate safeguards
  • NIS2 Directive: Cybersecurity risk management and incident reporting obligations
  • EU AI Act: Governance requirements for AI systems used in critical security infrastructure
  • DORA: Digital operational resilience requirements for financial sector threat intelligence

The platform architecture addresses these requirements through technical controls, governance processes, and documentation practices that ensure regulatory compliance while maintaining operational effectiveness in threat detection and response.

Key Insights

  • AI excels at pattern recognition across disparate data sources
  • Contextual enrichment dramatically improves alert actionability
  • Industry-specific models significantly outperform generic threat detection
  • Integration between surface, deep, and dark web monitoring creates comprehensive visibility
  • Human-AI collaboration is more effective than either in isolation

Future Developments

  • Adversarial Intelligence: Anticipating threat actor behaviors
  • Geopolitical Risk Integration: Correlating global events with cybersecurity implications
  • Quantum-Resistant Security: Preparing for post-quantum cryptographic threats
  • Extended Reality Threats: Monitoring emerging threats in VR/AR environments
  • Autonomous Response Orchestration: Coordinating defensive measures across complex environments

Myth vs Reality

Myth: Real-time threat intelligence lacks scalability across diverse threat landscapes.

Reality: Our work with BRICA proves that modern AI-powered intelligence systems can combine speed, accuracy, and breadth without compromise. The system handles millions of data points daily while maintaining sub-minute detection times for critical threats, demonstrating effective scalability across industries and threat types.

"

I've had the pleasure of working with Jeremy, a visionary leader in AI Law and business automation. As CEO of Space & Miller LLC, his innovative approach and deep expertise drive significant industry advancements. Highly recommend!

Niels Groeneveld
Co-Founder, BRICA Business Risk Intelligence

In Memoriam

It is with deep sadness that we note Niels Groeneveld passed away on 14 September 2025. He was laid to rest at Merenborch 70, 4132 HC Vianen, The Netherlands.

We express our heartfelt condolences to Niels' family, friends, and all who knew him. His contributions to European cybersecurity throughout his lifetime have left an indelible mark on the industry, and his vision and leadership will be deeply missed.

We are grateful for the work Niels did in advancing cybersecurity and protecting organizations across Europe. His legacy continues through the systems and solutions he helped build.

Confidentiality Notice

Due to the sensitive nature of threat intelligence data processing and proprietary security information, Space&Miller LLC DBA REPCONN has signed a Non-Disclosure Agreement with BRI Business Risk Intelligence BV.

The information presented in this case study has been carefully reviewed and approved for public disclosure. For inquiries about additional technical details, implementation specifics, or resources that cannot be publicly discussed, please contact jeremy@repconn.com to discuss what can and cannot be shared under the terms of our agreement.