Concept
At the heart of SECASSURED lies the principle of assurance-driven security engineering—embedding security and compliance as continuous, adaptive processes rather than one-time verifications.
The framework integrates AI-driven assurance services, federated digital twins, and SecDevOps/MLOps pipelines, allowing organizations to model, test, and validate systems in secure, virtual environments before deployment.
This concept transforms security operations from reactive defence into proactive, predictive assurance, where automated compliance management, vulnerability discovery, and real-time adaptation continuously improve system resilience.
Key innovation pillars include:
- Continuous assurance through adaptive security models.
- Digital twin-based secure environments for testing and validation.
- AI-powered services for detection, repair, and orchestration.
Together, they position SECASSURED as a foundational enabler for secure and compliant digital transformation.
Architecture
SECASSURED introduces a Blueprint Architecture uniting development and operational security through two interconnected digital twins:
- SecDevTwin – the Security Development Twin that virtualizes the development phase, enabling secure simulation, vulnerability testing, and compliance evaluation for both in-house and third-party components.
- SecOpsTwin – the Security Operations Twin that continuously monitors live systems, detects anomalies, and orchestrates AI-driven responses to emerging threats.
These twins form part of Security Engineering Workspaces, isolated environments hosting assurance-driven workflows. Within these workspaces, AI services interact with stakeholders via natural language interfaces and knowledge graphs, bridging human expertise with automated security intelligence.
The architecture’s modular design integrates:
- The Compliance & Security Assurance Model (CSAM) for continuous regulatory alignment.
- Assurance-Driven Interpretation Services (secInterp) to automate the parsing of regulations and standards.
- Security Assurance Case Tools (secSAC) and AI Trustworthiness Evaluation (secAssure4AI) modules for explainable, certifiable assurance.
This architecture supports scalable, federated deployments, enabling secure collaboration across supply chains without exposing sensitive operational data.
Modules
The SECASSURED platform comprises a catalogue of AI-based security modules designed for continuous assurance across IoT-edge-cloud environments.
Each module can operate standalone or be orchestrated within the federated twins, forming a coherent assurance ecosystem that automates security and compliance across the system lifecycle.
secInterp
AI-driven interpretation of regulations and standards, producing contextualized compliance requirements.
secSAC
Automated construction of security assurance cases using standardized Open Security Controls Assessment Language (OSCAL) representations.
secNCD
Non-Compliance Detector leveraging LLM-based reasoning to identify and explain regulatory or technical deviations.
secVDR
Vulnerability Discovery and Repair suite using fine-tuned models like CodeBERT and CodeT5 for detection and automatic remediation.
secAssure4AI
Trustworthiness assessment for AI components, addressing explainability, fairness, robustness, and adversarial resilience.
secAnoD
AI-based anomaly detection powered by LLMs and real-time threat intelligence.
secAttSIM
Attack and incident simulator supporting both white-box and black-box adversarial AI scenarios.
secAISOAR
Security Control Orchestration and Automation layer enhancing real-time decision-making through adaptive playbooks.
secSIM
Symbiotic simulator for design-phase security validation.
Compliance & Security Assurance Model (CSAM)
CSAM is the core assurance framework in SECASSURED that structures security and trustworthiness claims and links them to evidence generated by SECASSURED tools. It enables continuous, risk-based cybersecurity and AI assurance across the full system life cycle, from development to operation, supporting compliance and informed decision-making.
