RISE LabGriffith University

Open a layer for its keywords and topics, or scroll to each layer in detail. Every topic links to related members, publications and projects.

The principles, methods and evidence needed to trust digital and intelligent systems in adversarial environments. This layer defines security responsibilities, manages cyber risk and makes sure systems can withstand, recover from and adapt to incidents.

  • Governance
  • Risk controls
  • Resilience
  • Accountability
  • Assurance

Intelligent, data-driven and automated methods for understanding threats, finding vulnerabilities and defending systems. This layer brings together threat intelligence, security analytics, the security of emerging AI systems, and secure software engineering and testing.

  • Threat intelligence
  • Security analytics
  • LLM and agent security
  • Secure software
  • Automation

The cryptographic and systems foundations that protect sensitive data, computation and digital interactions. This layer connects foundational cryptography with practical privacy-enhancing technologies for health, finance, government, cyber intelligence, cloud services and AI-enabled systems.

  • Applied cryptography
  • Differential privacy
  • Secure computation
  • Verifiability
  • Provenance

The integration layer that turns the principles, defences and foundations of R, I and S into deployable outcomes: secure engineering practice, integrated toolchains, adversarial evaluation and continuous security operations that keep protections effective as software, data and threats change.

  • Demonstrators
  • Toolchains
  • Red teaming
  • Monitoring
  • Continuous assurance
  1. Risk and Requirements
  2. Design and Build
  3. Evaluate and Red Team
  4. Deploy and Operate
  5. Monitor and Improve

Responsible and Resilient Cybersecurity

Define responsibilities, manage cyber risks, and support prevention, recovery and adaptation.

The principles, methods and evidence needed to trust digital and intelligent systems in adversarial environments. This layer defines security responsibilities, manages cyber risk and makes sure systems can withstand, recover from and adapt to incidents.

PeopleLeo ZhangHe Zhang
ProjectsSecure and Auditable AI AgentsSecure Federated Battery Management System for...Digital Content ProtectorContinuous Assurance and Red-Team PipelinesResilient Security for Critical Infrastructure and...

Robustness, Safety and Resilience

Threats, malicious inputs, operational failures and cascading risks across software, cloud, critical infrastructure and AI-enabled applications, and the defences, resilient architectures and recovery strategies that contain them. For learning systems this includes poisoning, adversarial manipulation and distribution shift.

Adversarial robustness, Poisoning and backdoors, Resilient architectures, Incident containment, Recovery

Recent publications

All 100 publications on this topic

Projects

Transparency and Accountability

Methods that make the security properties and behaviour of systems traceable and auditable: provenance, supply-chain transparency, logging, responsibility allocation and human oversight. For AI systems this extends to explainability, uncertainty and accountability for automated decisions.

Provenance, Supply-chain transparency, Auditability, Explainability, Human oversight

Recent publications

All 27 publications on this topic

Projects

Evaluation, Verification and Continuous Assurance

Benchmarks, security metrics, testing methodologies, verification techniques and assurance cases that check whether systems meet their security, privacy and resilience requirements, and continuous monitoring that keeps protections effective as threats, software and models evolve.

Benchmarks, Security metrics, Formal verification, Assurance cases, Regression testing

Recent publications

All 2 publications on this topic

Projects

Intelligent Cyber Defence and Security Analytics

Understand threats, find vulnerabilities and support effective cyber defence with data-driven and automated methods.

Intelligent, data-driven and automated methods for understanding threats, finding vulnerabilities and defending systems. This layer brings together threat intelligence, security analytics, the security of emerging AI systems, and secure software engineering and testing.

PeopleLeo ZhangYanjun ZhangHe ZhangYi LiuWei Song
ProjectsPentestGPTSecure and Auditable AI AgentsPrivacy-Preserving Cyber-Threat Intelligence SharingTrustworthy, Energy-Aware Computing Load Balancing for...Verifiable Provenance for Software, Data and...Continuous Assurance and Red-Team Pipelines

Threat Intelligence and Security Analytics

Collecting, integrating and analysing security evidence from networks, software, identities and cloud platforms: threat modelling, malware and attack analysis, vulnerability prioritisation and decision support. AI-assisted techniques are adopted where they improve defence and evaluated for reliability and robustness.

Threat modelling, Malware analysis, Vulnerability prioritisation, Decision support, AI-assisted defence

Recent publications

All 12 publications on this topic

Projects

LLM, Foundation-Model and Agent Security

Threats to large language models, multimodal models and autonomous agents, from jailbreaks, prompt injection and poisoning to malicious skills, unsafe tool use and cross-agent attacks. We build automated red-teaming, secure architectures, permission models and runtime monitoring for AI systems that touch the real world.

Jailbreaks, Prompt injection, Agent skills, Permission control, Runtime monitoring, Red teaming

Recent publications

All 59 publications on this topic

Projects

Secure Software Engineering and Testing

Program analysis, fuzzing, automated test generation, vulnerability detection and supply-chain security for conventional software and software with AI components. We also study the security of AI-generated code and AI-assisted development, from credential leakage to malicious packages.

Program analysis, Fuzzing, Test generation, Supply-chain security, AI-generated code

Recent publications

All 16 publications on this topic

Projects

Secure and Privacy-Preserving Foundations

Protect data, computation and digital interactions with practical cryptography and privacy-enhancing technologies.

The cryptographic and systems foundations that protect sensitive data, computation and digital interactions. This layer connects foundational cryptography with practical privacy-enhancing technologies for health, finance, government, cyber intelligence, cloud services and AI-enabled systems.

PeopleLeo ZhangYanjun ZhangQinyi LiHe Zhang
ProjectsSecure Federated Battery Management System for...Digital Content ProtectorPrivacy-Preserving Cyber-Threat Intelligence SharingTrustworthy, Energy-Aware Computing Load Balancing for...Secure Collaborative AnalyticsVerifiable Provenance for Software, Data and...Resilient Security for Critical Infrastructure and...

Privacy-Preserving Data Processing

Differential privacy, secure aggregation, federated processing, privacy-preserving queries and anonymisation, so that data can be used and shared with minimal disclosure. We study inference, reconstruction and linkage attacks and balance privacy guarantees against utility and deployability.

Differential privacy, Federated learning, Secure aggregation, Inference attacks, Anonymisation

Recent publications

All 44 publications on this topic

Projects

Secure Computation and Collaborative Analytics

Secure multi-party computation, homomorphic encryption, trusted execution environments and hybrid designs that let organisations compute jointly without revealing private inputs, applied to cross-organisational threat analysis, health and financial analytics and protected cloud computation.

Multi-party computation, Homomorphic encryption, Trusted execution, Privacy-preserving ML, Collaborative analytics

Recent publications

All 5 publications on this topic

Projects

Cryptographic Protocols for Intelligent Systems

Protocols for confidentiality, authentication, integrity, access control, key management and provenance among users, services, devices and autonomous components, including secure communication, digital identity, lattice-based constructions and intellectual-property protection in adversarial environments.

Authentication, Key management, Digital identity, Lattice-based cryptography, IP protection

Recent publications

All 26 publications on this topic

Projects

Verifiable and Accountable Computation

Zero-knowledge proofs, verifiable computation, authenticated data structures and auditable protocols that let the correctness and provenance of computations be checked without revealing protected information, supporting secure outsourcing, supply-chain integrity and accountable automated decisions.

Zero-knowledge proofs, Verifiable computation, Authenticated data structures, Auditable protocols, Accountability

Recent publications

All 4 publications on this topic

Projects

Engineering, Evaluation and Continuous Operations

Turn principles, defences and foundations into deployable systems that stay secure after they leave the lab.

The integration layer that turns the principles, defences and foundations of R, I and S into deployable outcomes: secure engineering practice, integrated toolchains, adversarial evaluation and continuous security operations that keep protections effective as software, data and threats change.

PeopleYi LiuWei Song
ProjectsPentestGPTSecure and Auditable AI AgentsSecure Federated Battery Management System for...Digital Content ProtectorPrivacy-Preserving Cyber-Threat Intelligence SharingTrustworthy, Energy-Aware Computing Load Balancing for...Secure Collaborative AnalyticsVerifiable Provenance for Software, Data and...Continuous Assurance and Red-Team PipelinesResilient Security for Critical Infrastructure and...

Cross-Layer Demonstrators

Project-supported demonstrators that integrate all four layers: privacy-preserving threat-intelligence sharing, secure collaborative analytics, verifiable software and data provenance, resilient architectures for critical infrastructure, and secure, auditable AI agents.

Threat-intelligence sharing, Collaborative analytics, Provenance, Critical infrastructure, Auditable agents

Engineering Methods and Toolchains

Practical methods and toolchains for secure development: security by design, automated code and dependency analysis, credential protection and supply-chain assurance. Where AI is involved this includes secure MLOps, model and data provenance and agent permission controls.

Security by design, Dependency analysis, Secure MLOps, Credential protection, Reproducible evaluation

Recent publications

All 4 publications on this topic

Projects

Evaluation and Red-Team Pipelines

Reusable benchmarks, test harnesses, attack simulations and adversarial evaluation workflows for software, networks, cloud infrastructure and AI-enabled applications, combining penetration testing, fuzzing and red-team exercises into measurable, reproducible assurance evidence.

Benchmarks, Penetration testing, Attack simulation, Test harnesses, Assurance evidence

Recent publications

All 6 publications on this topic

Projects

Operational Monitoring and Assurance

Continuous monitoring, threat detection, configuration and dependency analysis, regression testing and incident investigation after deployment. For AI systems this includes watching for data or behavioural drift, adversarial manipulation and information leakage.

Continuous monitoring, Drift detection, Incident investigation, Regression testing, Post-deployment assurance

Projects