AISS
Artificial Intelligence in Safety and Security
Assessing the impact of artificial intelligence on safety and security
This ESReDA Project Group will explore how AI and emerging technologies can strengthen safety and security, and how these technologies impact risk prevention, safety assurance, and protection frameworks in our societies. The focus is not on developing new AI tools, but on understanding their added value and potential risks.
ESReDA Project Group on Artificial Intelligence in Safety and Security
The ESReDA Project Group on Artificial Intelligence in Safety and Security (AISS) focuses on the growing intersection between artificial intelligence (AI) and safety and security, addressing both protection against malicious acts (security) and industrial and occupational safety (safety). The group adopts a unified perspective, recognising that safety and security can no longer be treated as separate domains when analysing the risks associated with advanced digital and AI-based technologies.
This Project Group explores how AI and other emerging technologies can strengthen safety and security systems, while also examining how these technologies impact risk prevention, safety assurance, and protection frameworks in modern societies. The focus is explicitly technical and industrial, rather than addressing speculative or existential risks of AI.
Scope of the Project Group
The AISS Project Group does not aim to develop new AI tools or algorithms. Instead, its primary goal is to create a structured “toolbox” of principles, guidelines, and best practices to support authorities, regulators, and organisations in the responsible and informed use of AI in safety- and security-critical contexts.
The PG adopts a pragmatic and application-oriented approach, concentrating on:
- Understanding the added value of AI in safety and security applications
- Identifying and characterising the new risks and vulnerabilities introduced by
AI-based systems - Supporting decision-makers in balancing innovation with robustness,
transparency, and trust
Key Research Areas
The work of the AISS Project Group is structured around five critical thematic areas:
- Integrity in System Design
Analysis of how AI-based tools may encourage engineers to move prematurely towards detailed models (“design paradox”), potentially limiting error detection, reducing transparency, and constraining the exploration of alternative design
options. - Accident and Incident Investigation
Assessment of the potential of AI for analysing Big Data, supporting advanced simulations and 3D reconstructions, while addressing risks related to model hallucinations, biased datasets, and misleading conclusions in accident investigations. - Human Competencies and “Informacy”
Examination of the skills required for professionals to critically interpret AI outputs, moving beyond digital literacy towards a deeper competence that preserves human judgement, intuition, and responsibility in safety-related decision-making. - AI as a “Whistleblower”
Exploration of AI systems as impartial and anonymous mechanisms for detecting non-compliance with procedures or unsafe practices, including the ethical, organisational, and privacy-related implications of such applications. - Regulation, Certification, and Assurance
Analysis of how regulatory frameworks must evolve towards continuous certification and adaptive auditing, recognising that AI systems learn, evolve, and change their behaviour over time.
Identified Challenges
The Project Group explicitly acknowledges a dual narrative associated with AI in safety and security contexts:
- On one hand, AI enables real-time hazard detection, enhanced monitoring, and optimised responses.
- On the other hand, AI introduces black-box effects, unpredictable behaviours, data dependency risks, and new cyber-attack vectors that may propagate into physical safety crises.
Understanding and managing this duality is a central challenge of the AISS PG.
Project Duration and Milestones
- Working period: 2025–2027
- Key milestones:
- ESReDA Seminar organised by the PG in Autumn 2027
- Publication of the final Project Group report in 2028
Coordination Team
- Chair:
- Sever Paul – AGIFER (Romania)
- Secretary:
- John Kingston – JKL Limited (United Kingdom)
- Core Members:
- Eric Marsden – FonCSI (France)
- Tuuli Tulonen – Tukes (Finland)
Project Group Members
Join our Project Groups and collaborate with ESReDA members on real, ongoing initiatives with meaningful impact. Connect with experts from different countries, share knowledge, develop new ideas, and take part in community-driven projects built through teamwork.
Leaders
Members
Collaborators
Member Publications
“Fatigue delamination shape prognostics in composites using numerical simulation-assisted transfer learning”
Type: Journal Article
DOI: 10.1016/J.AEI.2025.104025 EID: WOS:001607520400002
“Data augmentation-aided fatigue delamination shape prognostics in composites”
Type: Journal Article
DOI: 10.1016/J.COMPSTRUCT.2025.119748 EID: WOS:001600332400001
“An asset management modelling framework for wind turbine blades considering monitoring system reliability”
Type: Journal Article
“A computer-based simulation methodology of the predetermined maintenance scheme of an irradiation facility”
Type: Journal Article
DOI: 10.1016/J.CIE.2024.110671 EID: WOS:001349909300001
“A general approach to assessing SHM reliability considering sensor failures based on information theory”
Type: Journal Article
“Description of the female of Labium walkeri Turner & Waterson, 1920 from Australia with new distribution data of L. montivagum (Hymenoptera, Ichneumonidae, Labeninae)”
Type: Journal Article
DOI: 10.11646/ZOOTAXA.5471.2.5 EID: WOS:001281047500001
“A novel semi-empirical approach to non-destructively evaluate the effect of infills on frame buildings”
Type: Journal Article
“Reliability-based leading edge erosion maintenance strategy selection framework”
Type: Journal Article
DOI: 10.1016/J.APENERGY.2023.122612 EID: WOS:001163518100001
“A semi-empirical method for shear response modelling of masonry infilled frame structures”
Type: Journal Article
“Physics-guided recurrent neural network trained with approximate Bayesian computation: A case study on structural response prognostics”
Type: Journal Article
“Training of physics-informed Bayesian neural networks with ABC-SS for prognostic of Li-ion batteries”
Type: Journal Article
“Intelligent and adaptive asset management model for railway sections using the iPN method”
Type: Journal Article
“Generative Adversarial Networks for Improved Model Training in the Context of the Digital Twin”
Type: Journal Article
DOI: 10.1155/STC/9997872 EID: WOS:001381050100001
“Particle filter-based delamination shape prediction in composites subjected to fatigue loading”
Type: Journal Article
DOI: 10.1177/14759217221116041 EID: WOS:000837361600001
“Damage Quantification and Identification in Structural Joints through Ultrasonic Guided Wave-Based Features and an Inverse Bayesian Scheme”
Type: Journal Article
DOI: 10.3390/S23084160 EID: WOS:000979333000001
“Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets”
Type: Journal Article
“Physics-guided Bayesian neural networks by ABC-SS: Application to reinforced concrete columns”
Type: Journal Article
DOI: 10.1016/J.ENGAPPAI.2022.105790 EID: WOS:000919379100001
“A wind turbine blade leading edge rain erosion computational framework”
Type: Journal Article
“Particle Filter-Based Delamination Shape Prediction in Composites”
Type: Journal Article
“Robust optimal sensor configuration using the value of information”
Type: Journal Article
DOI: 10.1002/STC.3143 EID: WOS:000880500400001
“Structural digital twin framework: Formulation and technology integration”
Type: Journal Article
“A Bayesian approach for damage assessment in welded structures using Lamb-wave surrogate models and minimal sensing”
Type: Journal Article
“Reduction of Petri net maintenance modeling complexity via Approximate Bayesian Computation”
Type: Journal Article
“A cross-sectoral review of the current and potential maintenance strategies for composite structures”
Type: Journal Article
“Adaptive approximate Bayesian computation by subset simulation for structural model calibration”
Type: Journal Article
DOI: 10.1111/MICE.12762 EID: WOS:000692778100001
“Uncertainty quantification in Neural Networks by Approximate Bayesian Computation: Application to fatigue in composite materials”
Type: Journal Article
DOI: 10.1016/J.ENGAPPAI.2021.104511 EID: WOS:000747080100005
“A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects”
Type: Journal Article
“Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys”
Type: Journal Article
DOI: 10.3390/SU13116038 EID: WOS:000660739500001
“Structural Health Monitoring Using Ultrasonic Guided-Waves and the Degree of Health Index”
Type: Journal Article
DOI: 10.3390/S21030993 EID: WOS:000615491400001
“OptiSens-Convex optimization of sensor and actuator placement for ultrasonic guided-wave based structural health monitoring”
Type: Journal Article
“Optimal sensor and actuator placement for structural health monitoring via an efficient convex cost-benefit optimization”
Type: Journal Article
“A Markov chains prognostics framework for complex degradation processes”
Type: Journal Article
“Optimal sensor configuration for ultrasonic guided-wave inspection based on value of information”
Type: Journal Article
“A robust Bayesian methodology for damage localization in plate-like structures using ultrasonic guided-waves”
Type: Journal Article
“Plausible Petri nets as self-adaptive expert systems: A tool for infrastructure asset monitoring”
Type: Journal Article
DOI: 10.1111/MICE.12427 EID: WOS:000460470900001
“A Bayesian Assessment of an Approximate Model for Unconfined Water Flow in Sloping Layered Porous Media”
Type: Journal Article
DOI: 10.1007/S11242-018-1094-2 EID: WOS:000455397000010
“A knowledge-based prognostics framework for railway track geometry degradation”
Type: Journal Article
“A new paradigm for uncertain knowledge representation by Plausible Petri nets”
Type: Journal Article
DOI: 10.1016/J.INS.2018.04.029 EID: WOS:000434742700020
“A new algorithm for prognostics using Subset Simulation”
Type: Journal Article
“A reliability-based prognostics framework for railway track management”
Type: Journal Article
“A multilevel Bayesian method for ultrasound-based damage identification in composite laminates”
Type: Journal Article
“An Information Theoretic Approach for Knowledge Representation using Petri Nets”
Type: Journal Article
DOI: 10.1109/FTC.2016.7821606 EID: WOS:000399455300021
“Condition-based prediction of time-dependent reliability in composites”
Type: Journal Article
“Model-Based Damage Evaluation of Layered CFRP Structures”
Type: Journal Article
DOI: 10.1063/1.4914727 EID: WOS:000354938100138
“Predicting fatigue damage in composites: A Bayesian framework”
Type: Journal Article
DOI: 10.1016/J.STRUSAFE.2014.06.002 EID: WOS:000340324800007
“Information-theory approach to model class assessment for tissue-engineered cultures consistence evolution”
Type: Journal Article
EID: WOS:000346381500012
“APPROXIMATE BAYESIAN COMPUTATION BY SUBSET SIMULATION”
Type: Journal Article
DOI: 10.1137/130932831 EID: WOS:000338783300020
“Model-based probability of detection of pathologies in soft tissue”
Type: Journal Article
EID: WOS:000346381500011
“FDTD simulations for ultrasound propagation in a 2-D cervical tissue model”
Type: Journal Article
EID: WOS:000346381500010
“Ultrasonic monitoring of artificial tissue mechanical properties in biorreactor”
Type: Journal Article
EID: WOS:000346381500008
“Device for monitoring samples e.g. tissue cultures, in medicine field, has analysis part provided with programmable unit for comparing and analyzing wave signals that are emitted and received by transmitter and receiver transducers”
Type: Patent
EID: DIIDW:2013F17033
“Fatigue Damage Prognosis in FRP Composites by Combining Multi-Scale Degradation Fault Modes in an Uncertainty Bayesian Framework”
Type: Journal Article
EID: WOS:000329292700167
“A MULTISCALE MECHANICAL MODEL FOR THE CERVICAL TISSUE”
Type: Journal Article
“Reliability in composites - A selective review and survey of current development”
Type: Journal Article
DOI: 10.1016/J.COMPOSITESB.2011.10.007 EID: WOS:000303284200008
“Device for monitoring samples such as tissue cultures or cellular cultures, has processing and analyzing unit that processes and analyzes signals that are transmitted through culture plate”
Type: Patent
EID: DIIDW:2012L94789
“Self-tensioning structure for composite-material bridges, has three-dimensionally hyperstatic structure formed by joining several elements to each other and to supports”
Type: Patent
EID: DIIDW:2010A66622
“Effects of sodium on mineral nutrition in rose plants”
Type: Journal Article
DOI: 10.1111/J.1744-7348.2000.TB00058.X EID: WOS:000168120700008
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