Intelligence Hub
Proprietary Research

Cybelle Frameworks

Proprietary intelligence frameworks developed through Cybelle research — each offering a structured lens for evaluating enterprise AI risk, pan-African digital resilience, model accountability, and zero-trust maturity. Interact with live simulations below.

ATP · Flagship FrameworkCRS · Regional IntelligenceMAI · Benchmark ToolZTV · Maturity Model

Simulation only. These interactive models use illustrative sample data for demonstration purposes. They are not production assessment tools and do not constitute professional security, legal, or regulatory advice.

ATP · Flagship Framework

AI Trust Perimeter

A comprehensive framework for defining, measuring, and enforcing trust boundaries in enterprise AI deployments across regulated industries.

Data Sensitivity45
Higher = more sensitive data flowing through the model
Model Risk Level30
Higher = model capable of higher-impact decisions
User Access Trust70
Higher = users have stronger verified credentials
Compliance Requirements60
Higher = more regulatory controls active
PERIMETER BOUNDARYCONTROLLED RISK
AI Trust Score
64%
Controlled Risk

The trust perimeter shows moderate stress. Elevated data sensitivity or model risk requires tighter controls. Recommend stricter access policies and enhanced monitoring before expanding this deployment.


CRS · Regional Intelligence

Continental Resilience Stack

Pan-African digital resilience architecture that maps regulatory, infrastructure, and threat environments across 54 African nations.

Select Region
Composite Resilience Score
60%
Southern Africa
Infrastructure Readiness74
Score: physical + digital infrastructure capacity
Regulatory Maturity67
Score: legislative framework completeness and enforcement
Cyber Threat Exposure78
Score: active threat actor presence and incident frequency
Digital Adoption Level76
Score: enterprise and consumer digital penetration
Analysis NoteSouthern Africa leads the continent in infrastructure maturity and digital adoption, underpinned by established financial services and technology sectors. Cyber threat exposure remains elevated due to high-value target density.

MAI · Benchmark Tool

Model Accountability Index

Quantitative benchmarking tool for evaluating AI model behaviour against enterprise governance, ethics, and auditability standards.

Transparency55
Model decision pathways are documented and accessible
Explainability50
Predictions can be explained to non-technical stakeholders
Bias Control45
Active monitoring and mitigation of model bias
Audit Readiness60
Model artefacts are versioned, logged, and auditable
Governance Controls50
Formal oversight, escalation, and review processes in place
Benchmark Scale
Emerging (0–40)
Developing (41–60)
Advanced (61–80)
Enterprise Ready (81–100)
52Score / 100
Developing

Developing accountability framework. Core governance structures exist but require systematic strengthening. Prioritise bias control and audit readiness before scaling deployment.


ZTV · Maturity Model

Zero Trust Velocity Curve

A maturity model that measures the speed and quality of zero-trust security adoption across enterprise network and identity estates.

Traditional
Identity-Based
Zero Trust
Continuous Verification
Autonomous
TIBZTCVAS0%100%
Adoption Progress40%
TraditionalAutonomous
Zero Trust

Core zero trust principles deployed. Least-privilege enforced. Policy engine active, but coverage is not yet universal.

Recommended Next Steps
  • Extend least-privilege access to all production workloads
  • Implement continuous authentication signals across the estate
  • Deploy SASE for remote access and branch connectivity