Singapore Case Study:

HSES and CRML Deployment at Singapore Financial Services

Executive Summary

This case study examines the implementation of Zeron’s open-source Human Security Exploitability System (HSES) and Cyber Risk Modeling Language (CRML) at a leading Singapore-based financial services institution. The deployment demonstrates how foundational cyber risk intelligence frameworks can transform traditional security operations into continuous, human-aware risk management systems aligned with Singapore’s robust regulatory environment and Smart Nation initiatives.

Organization Background

Organization: Singapore Financial Services Pte Ltd (SFS)

Industry: Financial Services & Digital Banking

Size: 2,800 employees, S$45 billion in assets under management

Regulatory Environment: Monetary Authority of Singapore (MAS) Technology Risk Management Guidelines, Personal Data Protection Act (PDPA), Cybersecurity Act 2018

Security Team: 42-person Security Operations Center operating 24/7, with regional responsibilities across APAC markets

Initial Challenges

Prior to implementation, SFS faced several critical challenges characteristic of mature financial institutions in Singapore:

Alert Fatigue and Human Performance Degradation

The SOC processed an average of 15,000 security alerts daily, with analysts handling 180-220 alerts per shift. During peak periods coinciding with quarterly financial reporting cycles, alert volumes exceeded 22,000 per day. This resulted in:

• Mean time to triage increasing from 8 minutes to 34 minutes during peak periods
• 47% of critical alerts being downgraded or dismissed without investigation during high-volume periods
• Three near-miss incidents where legitimate threats were initially classified as false positives under operational stress

Regulatory Compliance Gaps

MAS guidelines require financial institutions to demonstrate continuous cyber risk quantification and board-level visibility. SFS relied on quarterly manual assessments that could not:

• Provide real-time risk exposure calculations required for regulatory reporting
• Trace risk decisions from technical signals to business impact in auditable format
• Quantify the impact of human decision-making variability on overall security posture

Inability to Model Socio-Technical Risk

Traditional security frameworks treated human factors as non-quantifiable residual risk. SFS could not answer critical questions such as: How does analyst fatigue correlate with missed detections? When does alert volume create unsafe operating conditions? What is the actual exploitability surface created by workflow design versus technical vulnerabilities?

Solution Architecture

Implementation Approach

SFS adopted a phased implementation beginning in Q2 2025, leveraging the open-source nature of HSES and CRML to customize frameworks for Singapore’s regulatory context and operational requirements.

Phase 1: HSES Baseline Implementation (Weeks 1-6)

The team established baseline HSES measurements by instrumenting the SOC with telemetry capturing:

• Alert volume per analyst per hour
• Decision latency distribution across alert severity levels
• Escalation path complexity and handoff delays
• Shift changeover periods and associated error rates
• Correlation between analyst experience level and incident detection accuracy

HSES models were customized to reflect Singapore’s 24/7 SOC operations and MAS-mandated incident response timeframes.

Phase 2: CRML Core Implementation (Weeks 7-14)

The team deployed CRML to create machine-executable representations of:

• Critical asset dependencies across on-premise and cloud infrastructure
• Control effectiveness mappings to NIST Cybersecurity Framework and MAS TRM requirements
• Impact pathways from technical vulnerabilities to financial and regulatory consequences
• Uncertainty models for incomplete telemetry and control validation

CRML schemas were extended to incorporate Singapore-specific regulatory requirements, including PDPA data breach notification timelines and MAS incident reporting thresholds.

Phase 3: Integrated System Deployment (Weeks 15-20)

HSES-derived human exploitability signals were integrated into CRML models, enabling continuous computation of composite risk that accounts for both technical vulnerabilities and human-system interaction patterns. The integrated system feeds real-time risk metrics to executive dashboards and regulatory reporting systems.

Technical Architecture

Component	Implementation Details
HSES Engine	Python-based processing pipeline deployed on AWS Singapore (ap-southeast-1), consuming telemetry from Splunk Enterprise Security, ServiceNow ITSM, and custom SOC workflow systems
CRML Runtime	Containerized Kubernetes deployment with horizontal autoscaling, executing risk computations every 15 minutes during business hours and hourly overnight
Data Layer	PostgreSQL for CRML asset graphs and dependency models; TimescaleDB for HSES time-series analytics; encrypted at rest per PDPA requirements
Integration	REST APIs exposing risk metrics to Tableau executive dashboards and MAS regulatory reporting systems; Webhook notifications to ServiceNow for threshold breaches

Outcomes and Impact

Quantified Operational Improvements

Metric	Pre-Implementation	Post-Implementation	Δ
Mean Time to Triage (peak)	34 minutes	12 minutes	-65%
Critical Alert Miss Rate	47%	8%	-83%
False Positive Escalations	340/week	89/week	-74%
Risk Reporting Cycle Time	Quarterly (manual)	Real-time	—
Analyst Overtime Hours	680/month	210/month	-69%

Strategic Business Impact

Regulatory Confidence

SFS successfully demonstrated continuous cyber risk quantification to MAS during the November 2025 supervisory review. The CRML-based risk models provided full traceability from technical telemetry to board-reported risk metrics, addressing previous audit findings regarding risk calculation opacity. The implementation was cited by MAS as a model approach for technology risk management in Singapore’s financial sector.

Proactive Risk Detection

HSES modeling identified an unsafe operating regime during the September 2025 quarter-end processing cycle, when alert volume reached 19,800/day while three senior analysts were on medical leave. The system automatically triggered workflow adjustments and temporary analyst augmentation, preventing the degradation in detection accuracy that had occurred in previous quarters. This represented the first time the organization detected and mitigated human-system risk before incident manifestation.

Cost Optimization

By reducing false positive escalations by 74%, SFS achieved annual savings of S$1.2 million in operational costs. The reduction in analyst overtime hours improved team retention and reduced burnout-related turnover from 23% to 7% annually, saving an estimated S$840,000 in recruitment and training costs.

Critical Success Factors

Executive Sponsorship

The Chief Risk Officer and CISO jointly sponsored the initiative, ensuring alignment between enterprise risk management and security operations. Quarterly steering committee meetings with board risk oversight maintained strategic focus and resource commitment.

Singapore Regulatory Alignment

Early engagement with MAS during the design phase ensured CRML models mapped directly to Technology Risk Management Guidelines requirements. The open-source nature of both frameworks enabled regulatory auditors to independently validate risk calculation methodologies, building trust in automated risk reporting.

Open-Source Community Participation

SFS contributed 47 pull requests back to the HSES and CRML repositories, including extensions for APAC timezone operations, multi-language alert handling, and MAS-specific compliance mappings. This reciprocal contribution strengthened both the frameworks and SFS’s internal expertise.

Phased Deployment with Continuous Validation

Rather than replacing existing systems immediately, HSES and CRML ran in parallel for 12 weeks while the team validated model accuracy against historical incident data. This approach built organizational confidence and enabled model refinement before full operational deployment.

Outlook and Future Directions

Industry-Wide Adoption in Singapore

The SFS implementation has catalyzed broader interest across Singapore’s financial services sector. Three major banks and two insurance companies have initiated pilot programs based on the SFS architecture. The Monetary Authority of Singapore has indicated that continuous, human-aware cyber risk modeling may become a recommended practice in future Technology Risk Management Guidelines updates.

Regional Expansion

SFS is extending the implementation to regional operations in Hong Kong, Jakarta, and Sydney during Q1 2026. This expansion requires adaptation of HSES models to account for varying regulatory frameworks (HKMA, OJK, APRA) and cultural factors affecting human-system interaction patterns across different markets.

AI-Augmented Risk Intelligence

The next phase of development integrates large language models with CRML-based reasoning to enable natural language risk queries from executives and board members. This will transform the system from a monitoring platform into an interactive risk intelligence assistant, aligned with Singapore’s National AI Strategy goals for financial services innovation.

Smart Nation Integration

SFS is exploring partnerships with Singapore’s Cyber Security Agency (CSA) to contribute anonymized HSES and CRML data to national threat intelligence initiatives. This would enable sector-wide benchmarking of human exploitability patterns and contribute to Singapore’s position as a global leader in cyber resilience.

Lessons Learned

Cultural Change Requires Sustained Focus

Initial resistance from SOC analysts who perceived HSES monitoring as performance surveillance required extensive change management. Framing the system as workflow optimization rather than individual assessment, and demonstrating reduction in overtime hours, gradually shifted perception. Executive communication emphasizing that HSES models system properties rather than individual fault was critical to acceptance.

Data Quality Determines Model Accuracy

Early HSES models produced unreliable outputs due to inconsistent alert tagging and incomplete workflow telemetry. The team invested four weeks standardizing data collection before models became operationally useful. Organizations should expect initial model tuning periods proportional to existing data governance maturity.

Open-Source Requires Internal Expertise

While open-source frameworks eliminate licensing costs, they require skilled internal teams for customization and operation. SFS hired two additional risk engineers with Python and Kubernetes expertise. Organizations should factor engineering capacity into total cost of ownership assessments.

Conclusion

The implementation of HSES and CRML at Singapore Financial Services demonstrates that foundational cyber risk intelligence frameworks can transform security operations from reactive, control-centric models to continuous, human-aware risk management systems. The quantified improvements in operational efficiency, regulatory confidence, and strategic risk visibility validate the shift from static assessments to dynamic, computable risk intelligence.

For Singapore’s financial services sector, this case study illustrates a path to meeting increasingly sophisticated MAS expectations while building operational resilience aligned with Smart Nation digital transformation goals. The open-source nature of both frameworks enables independent validation, regulatory transparency, and collaborative improvement across the industry.

As cyber risk continues to converge with business risk, organizations that adopt continuously computable, human-aware risk models will gain decisive advantages in strategic decision-making, regulatory compliance, and operational efficiency. The SFS implementation establishes a replicable blueprint for this transformation in the Singapore context and beyond.

For Further Information

HSES Framework: https://qber.org/hses/

CRML Documentation: https://zeron.one/what-is-crml-the-new-standard-for-cyber-risk-quantification/

Zeron Platform: www.zeron.one

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Case Study Published: January 2026