Studies and meetings on health emergency preparedness and response for HERA : scientific background document : leveraging AI tools for a strengthened biosecurity framework.
Date: 2025-09-25
Institutions: Bozzao, Beatrice, European Health and Digital Executive Agency, Lupiáñez-Villanuev, Francisco, Rodés Sánchez, Marina
Subjects: Health Emergency Preparedness and Response Authority, artificial intelligence, cross-border dimension, decision (EU), emergency aid, emergency medical treatment, health legislation, health risk, healthcare industry, report, risk management

SPECIFIC CONTRACT No 2022 P5 02 

based on framework Contract No HADEA/2022/OP/0012 

Studies and Meetings on Health 
Emergency Preparedness and 
Response for HERA 
 Scientific background document 
Leveraging AI tools for a strengthened biosecurity framework 

Written by: Ana Roca-Umbert, Beatrice Bozzao,  
Marina Rodés Sánchez and Francisco Lupiáñez-Villanueva 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Leveraging AI tools for a strengthened biosecurity framework 

Disclaimer 

“This report was produced under the EU4Health Programme under a service contract with the European 
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Leveraging AI tools for a strengthened biosecurity framework 

Manuscript completed in May 2025 

First edition 

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Leveraging AI tools for a strengthened biosecurity framework 

Executive Summary 

Technological developments in artificial intelligence (AI), digitalisation, computational biology, 
and synthetic biology have fuelled a “21st-century biotechnology revolution”. AI and innovative 
digital tools can play an important role in supporting the preparation and response to health 
emergencies.  However,  the  use  of  these  new  technologies  has  also  triggered  concerns 
regarding  their  potential  exploitation  by  individuals  with  ill  intent.  Therefore,  relevant 
discussions and actions are needed to prevent their misuse. 

This  scientific  background  document  explores  the  potential  applications  of  AI  in 
strengthening the EU biosecurity framework. The EU biosecurity framework comprises the 
policies, strategies, regulations, and operational measures designed to prevent, detect, and 
respond to biological risks.  

The study is organised in three main chapters as follows: 

The first chapter, Introduction, presents the study scope, an overview of the current state of 
the art and defines the three objectives of the study, which are: 

1.  Assess  AI’s  potential  to  enhance  the  screening  of  synthetic  nucleic  acid  orders,  a 
countermeasure  to  prevent  and  detect  access  to  synthetic  nucleic  acids  with  threat 
potential.  

2.  Identify and evaluate AI-based tools that enhance the detection and attribution of genetic 

engineering activities. 

3.  Assess  both  the  opportunities  and  risks  of  AI  in  relation  to  Biosecurity  in  the  EU  and 

globally.  

To meet these objectives, the study team conducted a literature review using the academic 
database  PubMed  complemented  by  additional  desk  research  of  relevant  reports  by 
experts  in  the  field. Additionally,  key  experts  from  academia,  government  and  industry 
were  interviewed  to  collect  further  insights  on  the  topic.  In-depth  interviews  allowed  the 
identification of the most critical research opportunities in the field while recognizing possible 
risks and obstacles to adopting AI for a reinforced EU biosecurity framework. The methodology 
is further detailed in the Annex. 

The second chapter, Results, summarises the main outcomes of the study, which are 
organised into five sections. 

Section  2.1  provides  an  initial  overview  of  the  opportunities  that  AI  offers  to  enhance 
biosecurity.  Some  key  highlights  are  AI’s  potential  to  process  large  datasets,  perform 
advanced pattern recognition, automate tasks, facilitate access to knowledge, and contribute 
to the faster and more precise design of biological components. Since AI is regarded as a dual-
use technology with both beneficial and potentially harmful applications, Section 2.3 discusses 
the risks associated with AI in biosecurity. The most significant risk identified is the potential 
for Large Language Models (LLMs) and Biological Design Tools (BDTs) to provide access to 
knowledge and capabilities for developing novel, more dangerous biological agents. This could 
aid malicious actors and complicate biosecurity efforts.  

Section 2.3 presents seven tools used for screening orders of synthetic nucleic acid. Among 
these, four tools have integrated AI techniques, while the others use classical computational 
methods, often in the form of alignment algorithms. Evidence suggests that, currently, the main 
potential of AI in this context is augmenting the reference data sets used for screening with 
sequences that do not exist in nature, thus increasing the possibilities for detecting sequences 
of novel AI-generated proteins and reducing the occurrence of false positives. 

Section  2.4 introduces  the  nine  tools  identified for  their  ability  to  detect genetic  engineering 
and attribute it to the original author. Most of these tools utilise deep learning algorithms, stem 
from research initiatives, and are not currently on the market. Key recommendations to improve 
these biosecurity safeguards include integrating high-quality nucleic acid sequence datasets 

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Leveraging AI tools for a strengthened biosecurity framework 

to train deep learning models and adopting a combined approach of AI and other computational 
methods  to  enhance  detection  rates.  The  study  highlights  the  potential  of  the  tools  but 
emphasises the need for further development to ensure real-world applicability. 

Section 2.5 provides a final overview of the applications of AI for enhancing the EU biosecurity 
framework,  from  biosurveillance  and  early  warning  systems  to  outbreak  monitoring  and 
biothreat response and mitigation. 

The  thirds  chapter,  Discussion  &  Conclusions,  reviews  the  key  findings  of  the  study 
and  alignment  with  the  three  study  objectives.  First,  it  assesses  AI’s  role  in  improving 
synthetic nucleic acid screening, highlighting its contribution to managing datasets, optimising 
sequence comparisons, and predicting biological entity structures. Second, the study identifies 
AI-based  tools  for  detecting  and  attributing  genetic  engineering  activity,  revealing  that  AI 
techniques  like  deep  learning  show  promise  but  require  further  development  for  real-world 
application.  Third,  the  study  evaluates  the  opportunities  of  AI  technologies  in  biosecurity, 
noting that AI could enhance surveillance, early warning, outbreak monitoring, and response. 
The  study  also  considers  the  risks,  particularly  concerning  dual-use  capabilities  and  the 
potential  for  malicious  applications.  Overall,  the  study  underscores  the  need  for  policies  to 
balance  the  benefits  of  AI-driven  innovation  with  the  management  of  emerging  biosecurity 
risks. 

Moreover,  this  chapter  proposes  recommendations  for  policymakers  to  guide  future 
actions  in  leveraging  AI  for  an  enhanced  biosecurity  framework  in  the  EU.  These 
recommendations include the following:  
•  Promote multidisciplinary and cross-border collaboration in the fields of AI and biosecurity, 
to ensure a good alignment between the technical, ethical, and security implications. 
•  Support Research, Innovation and Capacity building in AI for Biosecurity to promote the 
development and adoption of AI tools that enhance pandemic preparedness and response 
•  Develope Standards and Guidelines for the use of AI and biotechnology in Life Sciences 
to promote the responsible use of technology against the misuse of biological systems and 
products 

•  Establish rules and legislation for AI in the life sciences to improve EU’s Biosecurity. 

infrastructure.  However,  achieving 

The  study  highlights  the  potential  of  AI  in  enhancing  biosecurity,  enabling  the  EU  to 
detect and respond to emerging biological threats. By leveraging AI, the EU can strengthen 
its  biosecurity 
international  and 
multidisciplinary  collaboration,  robust  ethical  guidelines  and  regulatory  frameworks,  and 
investment  in  research  and  innovation  to  ensure  responsible  and  effective  AI  use.  As 
biosecurity challenges evolve and technological advancement progress, the EU’s focus 
on  innovation,  cooperation,  and  adaptive  governance  will  be  crucial  in  safeguarding 
public health and global security. 

requires 

this 

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Leveraging AI tools for a strengthened biosecurity framework 

Contents 

Executive Summary .................................................................................................................... 4 

1. 
Introduction ....................................................................................................................... 10 
7¡7¡  Study.Scope¿.context.and.background ..................................................................................... 76 

7¡8¡  Study.objectives ..................................................................................................................... 77 

7¡9¡  Structure.of.the.report............................................................................................................. 77 

7¡0¡  Deﬁning.AI¿.Context.and.Techniques........................................................................................ 77 

7¡❶¡  Overview.of.existing.safeguard.measures ................................................................................. 79 

2.  Results ............................................................................................................................... 14 
8¡7¡  AI"s.potential.in.the.overall.EU.biosecurity.framework ............................................................... 7❶ 
2.1.1.  Considerations from the experts ................................................................................... 16 

8¡8¡ 

The.potential.risks.posed.by.AI.to.biosecurity ........................................................................... 7❸ 
2.2.1.  Considerations from the experts ................................................................................... 18 

8¡9¡  AI‗enabled.tools.for.an.enhanced.screening.of.synthetic.nucleic.acid.orders ............................. 7❺ 
2.3.1.  Challenges of AI used for synthetic nucleic acid order screening .................................... 21 
2.3.2.  Considerations from the experts ................................................................................... 23 

8¡0¡  AI‗enabled.tools.to.improve.the.detection.and.attribution.of.genetic.engineering ........................ 8❶ 
2.4.1.  AI-based tools to enhance the detection of genetic engineering ...................................... 25 
2.4.2.  AI-based tools to enhance the attribution of genetic engineering .................................... 27 
2.4.3.  Considerations from the experts ................................................................................... 30 

8¡❶¡  Outlook.and.potential.future.applications.of.AI ......................................................................... 98 

3.  Discussion & Conclusion ................................................................................................... 34 
9¡7¡  Recommendations.to.leverage.AI.for.an.enhanced.EU.biosecurity.framework ............................ 9❶ 

9¡8¡ 

Final.conclusions.of.the.study ................................................................................................. 9❹ 

Annex: Methodology .................................................................................................................. 45 
Scoping.literature.review ................................................................................................................... 0❶ 

Consultation.activities.and.stakeholder.mapping ................................................................................ 0❷ 

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Leveraging AI tools for a strengthened biosecurity framework 

List of Boxes 
Box 1 Working definition of "Biosecurity” and “AI system” in this report .................................... 10 
Box 2 Considerations from the experts - AI in the overall EU biosecurity framework .............. 16 
Box 3 Considerations from the experts í  The potential risks posed by AI to biosecurity ........ 18 
Box 4 Considerations from the experts: AI-based tools for an enhanced screening of synthetic 
nucleic acid orders ............................................................................................................................. 23 
Box  5  Considerations  from  the  experts:  AI-enabled  tools  to  improve  the  detection  and 
attribution of genetic engineering ..................................................................................................... 30 
Box 6 Research Questions and Query string used ....................................................................... 45 

List of Figures 
Figure  1  Prisma Flow Diagram summarising the records of the literature review. ................... 46 

List of Tables 
Ta b le  1 Definition of the AI techniques considered in this study ................................................. 12 
Ta b le  3 Key risks posed by AI for biosecurity ................................................................................. 18 
Ta b le   4 Commercially available tools for the screening of synthetic nucleic acid orders. ....... 22 
Ta b le   5 AI-based tools identi˛ ed for the detection of genetic engineering. ............................... 26 
Ta b le   6 AI-based tools identi˛ ed for the attribution of genetic engineering. ............................. 29 
Ta b le  7 Opportunities of AI to synthetic biology and biosecurity ................................................ 32 
Table 8 Consultation activities of the study. ................................................................................... 47 

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Leveraging AI tools for a strengthened biosecurity framework 

Acronyms and abbreviations 

AI 

ANN 

BIS 

CBRN 

CBB 

DNA 

EBRF 

EU 

GEA 

GED 

HERA 

IBBIS 

IGSC 

ML 

OFAC 

RF 

RFID 

RNA 

RNN 

RPD  

SD 

SDN 

SOC 

WHO 

Artificial Intelligence 

Artificial Neural Networks 

Bureau of Industry and Security  

Chemical, Biological, Radiological and Nuclear materials and agents 

Danish Centre for Biosecurity and Biopreparedness 

deoxyribonucleic acid 

European Biosecurity Regulators Forum 

European Union 

Genetic Engineering Attribution 

Genetic Engineering Detection 

Health Emergency Preparedness and Response Authority 

International Biosecurity and Biosafety Initiative for Science 

International Gene Synthesis Consortium 

Machine Learning 

Office of Foreign Assets Control 

Random Forest 

Radio-frequency identification 

ribonucleic acid 

Recurrent Neural Networks 

Regulated Pathogen Database 

System dynamic (simulation) 

Specially Designated Nationals  

Sequence of concern 

World Health Organisation 

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Leveraging AI tools for a strengthened biosecurity framework 

Glossary 

Term 

Description 

Artificial Intelligence system 

Biological threat 

Biosecurity 

Cybersecurity 

Data integrity 

Dual-use items 

Gene synthesis 

Genetic engineering 

Machine Learning 

Neural network 

Nucleic Acid 

Sequencing 

Sequencing (Next-Generation 
sequencing) 

Synthetic biology 

Machine-based system that, for explicit or implicit objectives, infers, 
from the input it receives, how to generate outputs such as 
predictions, content, recommendations, or decisions that can 
influence physical or virtual environments. 

A biologic threat is an infectious disease with the potential to spread 
and cause an emergency. Infectious disease emergencies may occur 
naturally or be released unintentionally or intentionally to hurt people, 
create fear, and/or disrupt society. 

Policies, principles, technologies and practices implemented for the 
protection and control of and accountability for biological material, 
technology and information or the equipment, methods, skills and 
data related to their handling. Biosecurity aims to prevent intentional 
or accidental unauthorized access to, and loss, theft, misuse, 
diversion or release or even weaponization of such commodities. 

Cybersecurity is the practice of protecting systems, networks, and 
programs from digital attacks. 

Data integrity is the accuracy, completeness, and quality of data as 
it's maintained over time and across formats. 

Dual-use items are goods, software and technology that can be used 
for both civilian and military applications. 

Gene synthesis is the artificial construction or manufacturing of 
nucleic acid sequences. This process involves creating new nucleic 
acid sequences in the laboratory using chemical or biological 
methods. The primary objective of this technology is to design and 
build nucleic acid sequences with specific functions or properties. 

Genetic engineering (also called genetic modification) is a process 
that uses laboratory-based technologies to alter the DNA makeup of 
an organism. 

Branch of AI and computer science that focuses on using data and 
algorithms to enable AI to imitate the way that humans learn, 
gradually improving its accuracy.  

A neural network is a machine learning program, or model, that 
makes decisions in a manner similar to the human brain, by using 
processes that mimic the way biological neurons work together to 
identify phenomena, weigh options and arrive at conclusions. 

A chemical compound that serves as the main information-carrying 
molecule of the cell and that directs the process of protein synthesis, 
thereby determining the inherited characteristics of every living thing. 
The two main classes of nucleic acids are deoxyribonucleic acid 
(DNA) and ribonucleic acid (RNA). 

Sequencing nucleic acids means determining the order of the four 
chemical building blocks - called "bases" - that make up the nucleic 
acid molecule.  

Next-generation sequencing (NGS) is a massively parallel sequencing 
technology that offers ultra-high throughput, scalability, and speed. 
The technology is used to determine the order of nucleotides in entire 
genomes or targeted regions of DNA or RNA. 

Synthetic biology is a field of science that involves redesigning 
organisms for useful purposes by engineering them to have new 
abilities. 

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Leveraging AI tools for a strengthened biosecurity framework 

1. Introduction 

1.1.  Study Scope: context and background 

in  synthetic  biology,  Artificial 

Technological  developments 
(AI), 
digitalisation,  automation,  and  computational  biology  are  fuelling  a  “21stcentury 
biotechnology  revolution”  with  lower  costs  and  easier  access  to  the  synthesis  and 
modification of biological material (Carter et al., 2023a). The speed at which these technologies 
are  evolving  often  exceeds  the  capacity  of  government  regulatory  oversight,  leading  to 
possible gaps in policies and regulations (Mouton, Lucas, & Guest, 2023). While this revolution 
opens the doors to scientific discoveries and innovation (i.e., supporting the modernisation of 
research within the health sector), it also triggers concerns with respect to the possibility of 
individuals with ill intent to exploit them for destructive purposes.  

Intelligence 

In the aftermath of COVID-19, the Health Emergency Preparedness and Response Authority 
(HERA)  was  set  up to  anticipate  health threats and  crises. It  collaborates  with  EU  Member 
States  to  enhance  preparedness  and  coordinates  efforts  in  response  to  health  crises  
(Directorate-General  for  Health  Emergency  Preparedness  and  Response  Authority,  n.d.).  
HERA is bringing forward key initiatives to promote advanced research and development of 
technologies for tackling priority cross-border health threats (European Commission, 2023). In 
parallel, the European Commission is leading a coordinated effort to embrace AI solutions that 
aim to address public health and global crises (Fontes, Corrigan, & Lütge, 2023).  

When the connection between opportunities and potential risks arising from this biotechnology 
revolution became evident, the concept of biosecurity emerged to safeguard society from 
the unintended or malicious consequences of biotechnological advancements. Different 
definitions of biosecurity have been adopted over the years. In Denmark, the Danish Centre 
for  Biosecurity  and  Biopreparedness  (CBB)  defined  biosecurity  as  “Measures  designed  to 
prevent the malicious abuse of “dual use” biological materials, i.e. biological substances and 
related materials that are intended for legal research, but which could also be used to create 
and  disseminate  a  danical  agent”  (Centre  for  Biosecurity  and  Biopreparedness  (CBB) 
Denmark, 2015). In this definition, biosecurity is limited to the prevention of intentional misuse 
of “dual use” biological materials. This is different from biosafety definition which also covers 
the  prevention  of  accidents  or  unintentional  misuse.  This  report  adopts  the  definition  of 
biosecurity  outlined  in  the  World  Health  Organisation  (WHO)  2024  laboratory 
biosecurity guidance. This definition has a broader scope, covering both the intentional and 
accidental improper or harmful use of biological material and related resources (See Error! 
Reference source not found.). 

This  study  will  explore  the  use  of  AI  to  enhance  biosecurity  measures  supporting  HERA’s 
mission  of  preparing  and  responding  to  health  emergencies.  For  this,  this  report  uses  the 
updated  OECD  definition  of  'AI  systems'  to  ensure  technical  accuracy  and  international 
alignment (See Error! Reference source not found.).  

Box 1 Working definition of "Biosecurity” and “AI system” in this report 

Definition of Biosecurity by the WHO 2024 Laboratory biosecurity guidance.  

“Policies, principles, technologies and practices implemented for the protection and control of and 
acountability for biological material, technology and information or the equipment, methods, skills 
and data related to their handling. Biosecurity aims to prevent intentional or accidental unauthorized 
access to, and loss, theft, misuse, diversion or release or even weaponization of such commodities.” 
(World Health Organization, 2024) 

Definition of AI system by OECD. 

“An AI system is a machine-based system that, for explicit or implicit objectives, infers, from the 
input  it  receives,  how  to  generate  outputs  such  as  predictions,  content,  recommendations,  or 
decisions  that  can  influence  physical  or  virtual  environments.  Different  AI  systems  vary  in  their 
levels of autonomy and adaptiveness after deployment.” 

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Leveraging AI tools for a strengthened biosecurity framework 

1.2.  Study objectives 

This  scientific  background  evaluates  the  potential  applications  of  AI  in  enhancing 
biosecurity  in  the  EU.  Through  a  comprehensive  literature  review,  desk  research  and 
consultations with key experts, the study maps existing technologies and solutions aimed at 
improving biosecurity. It seeks to identify key research opportunities and findings in this field 
while also addressing potential risks and challenges associated with adopting AI to strengthen 
biosecurity in the EU.  

More specifically, the report has the following key objectives: 

1.  Assess  AI’s  potential  to  enhance  screening  of  synthetic  nucleic  acid  orders,  a 

countermeasure to prevent access to bioweapon components 

2.  Identify and evaluate AI-based tools and methodologies that enhance the detection of 
unauthorised genetic engineering activities, and improve attribution capabilities 
to trace the origin of genetic modifications 

3.  Evaluate the risks posed by AI technologies and the opportunities they offer to 

enhance biosecurity both within the EU and globally. 

The  report  will  support  HERA’s  efforts  in  investigating  how AI  tools  can  be  leveraged  for  a 
strengthened biosecurity in the European Union for an effective policy development. To do this, 
the report will outline the needed targeted actions to adopt AI-supported technologies in the 
field of biosecurity and further explain how EU crisis preparedness and response would benefit 
from the adoption of such AI-based tools. 

1.3.  Structure of the report 

The study is divided into three main chapters and and annex: 
Chapter 1 – Introduction presents the purpose of the study together with the background and 
overview of the state of the art concerning biosecurity in the EU. 

Chapter  2  –  Results  presents  the  key  findings  that  emerged  from  the  literature  review, 
according to the above-mentioned study objectives. These are organised into three main parts:  

•  AI’s potential for an enhanced screening of synthetic nucleic acid orders  
•  AI-enabled tools to improve the detection and attribution of genetic engineering 
•  AI in the overall EU biosecurity framework, including opportunities and risks posed by 

AI to biosecurity  

At  the  end  of  each  results  section,  key  insights  resulting  from the consultation  activities 
with experts in the field are presented to complement the outcomes of the literature review. 

Chapter  3  –  Discussion  and  Conclusions  includes  final  considerations  based  on  the 
analysis of the key findings of the study, including identified gaps and possible directions for 
future research and policy actions. 

Annex  –  Methodology  outlines  the  methodology  followed  in  this  report,  detailing  the  key 
research  questions,  the  literature  search  strategy,  and  the  data  extraction  methodology.  A 
PRISMA diagram is presented to give a clear snapshot of the information considered in the 
scientific review. 

1.4.  Defining AI: Context and Techniques 

AI is a vast field of study that includes a variety of methodologies and applications, such as 
computer vision, natural language processing, and machine learning for pattern recognition, 
prediction, and large-scale data analysis. In 2023, member countries of the Organisation for 
Economic  Co-operation  and  Development  (OECD)  approved  a  revised  version  of  the 

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Leveraging AI tools for a strengthened biosecurity framework 

organisation's definition of an AI system, which was also used as a relevant basis for the EU 
AI Act (OECD, 2023).  

This  study  uses  the  updated  OECD  definition  of  'AI  systems'  to  ensure  technical 
accuracy and international alignment. The definition states that “An AI system is a machine-
based system that, for explicit or implicit objectives, infers, from the input it receives, how to 
generate  outputs  such  as  predictions,  content,  recommendations,  or  decisions  that  can 
influence physical or virtual environments. Different AI systems vary in their levels of autonomy 
and adaptiveness after deployment.” 

Moreover, the OECD developed the Framework for the Classification of AI Systems to evaluate 
and characterise AI systems across key dimensions such as model type, data and input, main 
tasks, and examples (OECD, 2022). Using this framework, a classification model for this 
study  was  developed  to  assess  and  categorise  AI  systems  based  on  their 
characteristics. This model identifies three main types of AI Systems in this study: Symbolic 
AI models, Statistical AI models, and Hybrid AI models. Table 1 provides an overview of the 
three  categories  considered,  highlighting  their  main  characteristics  and  tasks.  Symbolic AI 
models (or knowledge-based models) are used for tasks such as planning and optimisation, 
allowing inferences and classification activities. Statistical AI models are used for trend and 
pattern prediction based on data (e.g., linear regression, logistical regression, decision trees, 
neural networks, naïve Bayes, deep learning). Lastly, Hybrid AI models combine the previous 
approaches  to  extract  complex  features,  enhance  pattern  recognition,  and  learning  abilities 
(e.g., natural language processing (NLP) algorithms).  

Ta b le  1 Definition of the AI techniques considered in this study 

Description 

Symbolic AI model 

Statistical AI model 

Hybrid AI model 

Symbolic or knowledge-based 
AI uses human-generated logical 
representations to infer a 
conclusion from a set of 
constraints (variables). These 
constraints include rules, 
ontologies and search 
algorithms that rely on explicit 
descriptions of variables and 
descriptions of the interrelations 
between these variables. 

Statistical AI models (e.g. 
genetic algorithms, neural 
networks and deep learning) 
identify patterns based on data 
rather than expert human 
knowledge. Primarily used for 
recognition, more recently, 
these models are also being 
used for tasks such as 
synthesising and generating 
images or audio. 

Many applied AI systems 
combine symbolic and 
statistical models into 
“hybrid” models. For 
example, NLP algorithms 
often combine statistical 
approaches building on 
large amounts of data and 
symbolic approaches that 
consider issues such as 
grammar rules. 

Examples 

Knowledge-based and expert 
systems 

Neural networks, Deep 
learning, Linear regression, 
Logistical regression, Decision 
trees 

Data 

Labeled and categorised 

Unlabeled and uncategorised 

Task 

Making inferences and 
performing classification 

Trend/ pattern prediction 

Source: author’s elaboration based on literature review findings. 

Natural Language 
Processing 

Can be labeled or 
unlabeled 

Complex feature extraction 
and pattern recognition/ 
learning abilities 

As  AI  systems  become  more  advanced  and  diverse,  their  application  extends  beyond 
traditional fields into areas like synthetic biology and biotechnology. These technologies rely 
on AI models to design, predict, and optimise biological processes, enhancing both research 
capabilities  and  practical  applications.  The  convergence  of  AI  and  biology  is  particularly 
significant at the 'digital-physical interface,' where an actor integrates digital design and outputs 
into  a  biological  reality  (Carter  et  al.,  2023a).  The  interface  between  physical  and  digital 
elements  is  considered  by  experts  as  a  crucial  juncture  for  enhancing  biosecurity 
(O'Brien & Nelson, 2022).  

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Leveraging AI tools for a strengthened biosecurity framework 

1.5.  Overview of existing safeguard measures  

Advancements in biotechnology and artificial intelligence, while offering new possibilities for 
medical and agricultural innovation, also raise concerns about potential misuse. The ability to 
design and manipulate biological systems digitally introduces new vulnerabilities, particularly 
as the tools for genetic editing and synthesis become increasingly accessible. Consequently, 
ensuring that these powerful technologies are used responsibly has become a priority 
for  policymakers  and  scientific  communities  alike.  Efforts  to  strengthen  biosecurity  and 
manage biorisks have increased globally and within the EU, leading authorities to implement 
measures to control sensitive biological materials. 

The growing demand in the gene synthesis market (Global Market Insights, Inc., 2022) makes 
the  establishment  of  safeguards  for  nucleic  acid  synthesis  technology  essential.  Robust 
safeguards and control can prevent the unintentional distribution of biological components that 
could be used to create harmful biological agents. Current biosecurity safeguard measures 
involve  screening  synthetic  nucleic  acid  orders  to  assess  the  safety  and  legality  of 
requested sequences and verifying customer information. The goal is to prevent misuse while 
allowing scientific and medical research to continue safely. 

Most  governments,  including  those  in  the  EU,  do  not  mandate  screening  practices  by  law 
(Hoffmann, et al., 2023). Various initiatives have been launched to address this regulatory gap. 
In 2009, the International Gene Synthesis Consortium (IGSC) created a screening protocol to 
set  standards  and  best  practices  aimed  at  preventing  the  misuse  of  nucleic  acid  synthesis 
(International Gene Synthesis Consortium, 2017). The WHO, in 2022, recognised the potential 
risks related to nucleic acid synthesis and recommended adherence to established screening 
protocols  (World  Health Organization,  2022). At the  national  level,  especially  in  the  US  and 
UK,  Governments  have  published  national  screening  guidance  for  users  and  providers  of 
synthetic nucleic acids (UK Government, 2024). In addition to voluntary efforts and adherence 
to common guidelines, some countries, such as the US, introduced mandatory measures for 
screening.  In  2024,  the  White  House’s  Office  of  Science  and Technology  Policy  introduced 
the Framework for Nucleic Acid Synthesis Screening, directing providers to screen orders for 
sequences  of  concern  (SOCs)  and  verify  customer  legitimacy,  making  it  mandatory  for 
federally funded research (The White House, 2024) (Diggans & Leproust, 2019).  

The screening of synthetic nucleic acid orders largely depends on the existence of repositories 
of  well-characterised  regulated  pathogens.  A  shift  towards  a  less  centralised  nucleic  acid 
synthesis market and the rapid developments in synthetic biology could lead to the evasion of 
current  screening  mechanisms.  Experts  emphasise  the  need  for  alternative  safeguard 
measures  that  go  beyond  list-based  sequence  screening  (Batalis,  Schuerger,  Gronvall,  & 
Walsh, 2024).  

A  biosecurity  safeguard  measure  gaining  attention  is  the  detection  of  genetic 
engineering  (GED). This  process  identifies  whether  an  organism’s  genetic  information  has 
been  artificially  modified.  It  helps  detect  unauthorized  or  harmful  genetic  modifications, 
allowing authorities to identify potential misuse, such as engineered pathogens. GED can be 
complemented  by  genetic  engineering  attribution  (GEA),  which  traces  the  source  of 
genetic modifications. GEA aids in investigations to identify the parties responsible for such 
modifications. These safeguard measures aim to directly identify genetic engineering activity  
and trace it back to its source, making biosecurity measures more independent of reference 
databases of well-known pathogens.  

Biosecurity safeguard measures demonstrate ongoing efforts to manage biosecurity risks at 
the operational level. However, their development and implementation are rooted in a broader 
international policy framework designed to prevent the misuse of biological materials 
and technologies. 

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Leveraging AI tools for a strengthened biosecurity framework 

The key regulatory instruments for stronger global biosecurity include the Biological Weapons 
Convention  (BWC)1,  the United  Nations  Security Council  Resolution  1540  (UNSCR  1540)2, 
and the International Health Regulations (IHR)3. These three instruments, adopted by the 27 
EU Member States, are the only three legally binding international instruments forming 
the backbone of the global effort to prevent the development of biological weapons and 
enhancing  global  biosecurity  (Vennis,  Boskovic,  Bleijs,  &  Rutjes,  2022)  (United  Nations 
Office  for  Disarmament Affairs.,  2024).  At  the  EU  level,  several  policies  have  evolved in 
parallel to improve biosecurity. To support the BWC and the implementation of the UNSCR 
1540,  the  European  Council  adopted  the  European  Security  Strategy4  and  the  European 
Strategy against the Proliferation of Weapons of Mass Destruction5 along with several Joint 
Actions and Council decisions (Wakefield, 2022). In 2009, the European Commission adopted 
the CBRN Action Plan to address threats from chemical, biological, radiological, and nuclear 
incidents  of  accidental,  natural  or  intentional  origin  (Bielecka  &  Mohammadi,  2014).  More 
recently,  the  COVID-19  pandemic  highlighted  vulnerabilities,  triggering  investment  in 
biosecurity  projects  that  focus  on  the  development  of  medical  countermeasures  for  CBRN 
threats.  

Technological innovations are transforming the threat landscape, potentially shifting attention 
from state-sponsored actions to micro-level risks posed by smaller entities or individuals (Lee, 
Cowan,  &  &  Tankard,  2022).  Despite  the  robust  regulatory  frameworks,  biosecurity 
remains  a  complex  concept  across  the  EU  Member  States.  Different  national  priorities, 
resources, capacities, and existing legal frameworks lead to varying approaches to managing 
biological risks. As a result, biosecurity in the EU cannot be viewed as a singular entity 
but  rather  as  a  heterogeneous  framework,  consisting  of  a  system  of  policies,  strategies, 
regulations and operational measures to prevent, detect, and respond to biological threats that 
reflect the different approaches and capacities of individual Member States.  

Moreover,  as  the  interface  between  digital  and  biological  systems  continues  to  grow,  it  is 
essential to evaluate the evolving role of AI and innovative digital tools in strengthening 
biosecurity measures, assess the new challenges, and adapt future biosecurity policies 
to mitigate biological risks effectively. 

2. Results 

This section summarises the main results of the scoping literature review. The first part focuses 
on how AI can contribute to the overall EU biosecurity framework and the associated risks it 
entails. This section is followed by a presentation of the identified tools for screening synthetic 
nucleic  acid  orders  and  how AI  has  been  incorporated  to  enhance  their  use,  along  with  a 
discussion on the  application of AI to improve genetic engineering detection and attribution. 
The  results  section  concludes  with  a  general  outlook  and  presentation  of  potential  future 
applications  beyond  the  screening  of  orders,  GED  and  GEA.  Additional  references  are 
available  at  the  end  of  each  subsection,  based  on  information  gathered  through  in-depth 
structured interviews with key experts in this field. 

1 (United Nations Of˛ ce for Disarmame nt Affairs., 1972) 
2 (United Nations Of˛ ce for Disarmame nt Affairs, 2004) 
3 (World Health Organization, 2016) 
4 (European Council, 2003) 
5  (Council of the Europe an Union, 2003) 

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Leveraging AI tools for a strengthened biosecurity framework 

2.1.  AI’s potential in the overall EU biosecurity framework 

AI’s capacity to integrate large datasets, quickly analyse complex patterns, and deliver real-
time risk assessments can be applied in the detection, analysis and response to both natural 
and  deliberate  scenarios  of  biological  threats.  These  advanced  capabilities  are  crucial  for 
strengthening  the  EU's  biosecurity  framework,  as  they  allow  for  earlier  detection,  quicker 
responses, and more effective threat mitigation strategies. 

One of the basic areas explored is the use of AI to automate and accelerate certain steps 
in  scientific  discovery  like  generating  hypotheses,  literature  screening,  assisting  in  the 
design  of  experiments  and  data  collection  exercises,  automating  code  writing  and 
programming, etc. Although promising, the limited knowledge of common users could lead to 
an overestimation of AI-based automation (Carter et al., 2023a) (InterAcademy Partnership, 
2024). 

The  literature  review  revealed  Machine  Learning  use  cases  for  enhancing  pandemic 
preparedness  and  response.  Syrowatka  et  al.  (2021)  identified  the  potential  of  AI  in 
forecasting infectious disease dynamics, surveillance and detection of outbreaks, monitoring 
adherence  to  public  health  recommendations,  detection  of  symptomatology  (e.g.,  body 
temperature),  timely  diagnosis  of  infection,  assistance  in  patient  triage,  and  prognosis 
assessment and patient's treatment response. Nevertheless, most of the identified uses were 
at a developmental stage, not ready to inform public health decisions. 

Other publications focus on how Large Language Models (LLM) and Generative AI (Gen 
AI)  could  be  applied  to  biosecurity.  Some  experts  discuss  that  current  general-purpose 
LLMs applied to the life sciences are unlikely to provide significant novel insights into biological 
systems  and  therefore  are  only  useful  for  rather  unexperienced  individuals  with  a  shallow 
knowledge on the topic of interest. However, experts argue that this trend could easily change, 
considering the rapid advancement of this technology (Carter et al., 2023a). Chen et al. (2024) 
reviewed the potential role of LLMs for automating biosecurity tasks, which included data 
gathering from public data sources (e.g., papers or news), report summarising, and reviewing 
safety forms. The authors concluded that while LLMs have great potential, they are limited by 
their provision of shallow or incorrect information. Nevertheless, through an in-depth analysis 
of 169 publications on AI-enabled synthetic biology, Undheim (2024) identified that AI-based 
tools are already being used for several standard and operational functions in synthetic 
biology like text classifiers, generic search engines, generation of ideas, helping with access 
to  scientific  knowledge,  proofreading,  summarizing  knowledge,  translation,  text  mining  of 
biomedical data, optimizing coding and workflow optimization among other similar functions.  

Other authors have focused on the use that AI-based biodesign tools (BDT) can have for 
biosecurity. AI-based  BDTs  are AI  systems trained  on  biological  data  that  can  help  design 
proteins or other biological agents. Different from the general-purpose LLMs, AI-based BDTs 
are  reserved  for  those  with  a  deeper  scientific  experience  and  knowledge.  BDTs,  such  as 
RFDiffusion and protein language models like ProGen2 and Ankh, can expand the ability of 
experienced  actors  to  use  available  information  to  design  relevant  biological  components 
(Sandbrink,  2023).  The  capabilities  of  BDTs  to  facilitate  the  design,  testing  and 
optimization  of  new  biological  components  with  increased  accuracy  has  many 
beneficial applications in fields like medicine, the pharmaceutical industry and agriculture. In 
these  areas  an  improved  design  of  vaccines  and  novel  therapeutics  could  be  extremely 
beneficial (Carter et al., 2023a). For instance, AlphaFold2 is an AI BDT currently used in the 
field. Although it doesn't directly generate biological designs, it features an AI-enhanced ability 
to predict protein 3D structure (Senior, et al., 2020). Despite the promising advances, there are 
still some challenges to overcome in this field like the transition of structure-based biodesign 
to a function-based one or the capacity to design those biological components which are less 
well characterised and for which little data is available.  

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Leveraging AI tools for a strengthened biosecurity framework 

2.1.1.  Considerations from the experts 

The  findings  above  present  the  information  gathered  from  reviewing  the  literature.  These 
results have been further discussed and validated with experts in the field, who provided an 
overview of the role of AI in biosecurity. Box 2 presents the main learnings from the interviews 
conducted  with  several  experts  from  Academia,  Industry,  Government,  and  International 
Agencies.  

Box 2 Conside rations from the e xpe rts - AI in the  ove rall EU biose curity framework 

Highlights resulting from Experts’ consultation 

Opportunities for the use of AI 

Several policy and industry experts pointed to the opportunities of AI to improve biosecurity 
while  promoting  innovation:  AI  for  medical  countermeasure  development;  AI  for  bio 
surveillance;  AI  for  broader  forensic  capabilities  (e.g.  monitoring  and  screening  cloud 
laboratories  to  avoid  cyber-attacks);  and  AI  for  collecting  data,  such  as  open-source 
information  to  better  understand  the  biological  threat  landscape.  Several  interviewees 
identified challenges to overcome to maximise the use of AI for biosecurity: 

•  AI experts not being interested in biosecurity matters and biosecurity experts lacking 
expertise on AI. Disaggregated knowledge in this field slows down concerted efforts 
among different actors.  

•  One academic expert underscored that, due to the novelty and misinformation around 
AI,  several  scientists  are  still  reticent  to  use  AI  in  their  research.  This  is  lost 
knowledge  and  potentially  innovation  because  the  tools  are  often  developed  by 
computational dry laboratories and don't necessarily get adopted into wet labs.  

•  In a similar way disaggregated data is a challenge for using AI to do any kind of bio 
surveillance. With respect to the opportunities of AI, one academic expert underlined 
that if data is stored in one spot, there is a lot of opportunity for monitoring human 
health. If it is disaggregated, then it would limit the opportunities that AI can provide, 
e.g.  from  electronic  health  records. Another  important  aspect  related  to  data  is  the 
integration of disparate data types to make sense of things in aggregate form, e.g. 
human  movement  or  migration,  animal  population  density  or  weather  conditions. 
Questions that rely on deriving insight from vast amount of data are where AI can help. 
•  Low commercial incentives in the biosecurity field. A synthetic nucleic acid screening 
provider started seeing decreasing similarity to naturally occurring sequences from the 
orders  received,  due  to  AI  design  tools.  In  this  context,  they  were  advocating  for 
government to invest in research programs to solve this community challenge. 

•  Most stakeholders underlined the need for increased collaboration among industry, 
academia and government. Several experts underlined that government is critical to 
enforce rules but does not have the capacity alone to set standards because it doesn't 
have the knowledge that industry and academia can bring to the table. Academia has 
a critical role to play in answering the question “What should we control? What is the 
actual risk level associated with different genes?” Academia is also a great place for 
thinking about potential types of evasion that we need to defend against, including AI 
based evasion. Industry has pragmatic experience as it must deal with false positive 
rates and the cost of decision making. Thus, it should be at the table concerning these 
aspects because it must deal with the actual implementation and solutions. 

•  Finally, several experts underlined that, taking the example of the US Executive Order, 
Regulations could push things ahead. Government experts highlighted that they are 
still waiting for an EU directive on Biosecurity to be implemented in Member States. If 
countries don't have a law, then it's also difficult to find a contact person, which in turn 
also affects collaboration with other stakeholders in this field.  

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Leveraging AI tools for a strengthened biosecurity framework 

2.2.  The potential risks posed by AI to biosecurity  

As AI  advancements  fuel  innovation  and  technological  developments,  they  can  also  enable 
their misuse as biological weapons (de Lima, et al., 2024). Because of the consideration of AI 
as  dual-use  technology,  it  is  relevant  to  consider  the  concerns  shared  by  the  scientific 
community related to the risks that AI might pose for global biosecurity.  

The  InterAcademy  Partnership  (IAP)  meeting  in  Trieste  brought  together  technical  and 
scientific  experts  from  different  countries  to  discuss  the  potential  impact  of  AI  on  global 
biosecurity and international cooperation within the framework of the BWC. Some participants 
warned  that  AI  could  negatively  impact  biosecurity  by  enabling  the  faster,  cheaper 
creation of biological weapons, including AI-designed toxins or contagious proteins like 
prions (InterAcademy Partnership, 2024). 

Sandbrink  (2023)  highlights  two  classes  of AI  tools  that  could  pose  biosecurity  risks:  Large 
Language Models (LLMs) and Biological Design Tools (BDTs). In a recent article, Batalis and 
colleagues (2024) also argue that LLM-based chatbots and BDTs could inﬂuence biosecurity 
differently. While LLM-based chatbots lower barriers for non-expert individuals to access 
information that could be misused for malicious intent, BDTs can provide new capabilities 
to users with ill intents.  

Empirical evaluations suggest that LLMs may aid in planning biological attacks to some extent 
(Soice  et  al.,  2023),  though  other  studies  report  no  effect  (Mouton,  Lucas,  &  Guest,  2023) 
(National Security Commission on Emerging Biotechnology (NSCEB), 2024). Some experts 
warn that LLMs could facilitate familiarising individuals with biological agents and resources 
needed to develop harmful agents (Carter et al., 2023a). However, no documented cases of 
AI-enabled  bioweapons  exist,  and  experts  note  that  significant  hurdles  remain,  including 
access to biological components, laboratory infrastructure, and specialised training to test and 
deploy  the  designed  agent.  Expert  opinions  differ  on  how  much  tacit  knowledge  LLMs  can 
effectively provide and their actual impact, given the potential inaccuracies of the model (Carter 
et al., 2023a). 

Regarding the risks associated to BDTs, Carter and colleagues (2023a) noted that their misuse 
often  stems  from  their  original  benign  purpose,  such  as  predicting  high-pandemic-potential 
pathogens,  which  could  also  identify  promising  candidates  for  biological  weapons.  Experts 
interviewed by Carter in their study believe that AI BDTs have a higher possibility than LLMs 
to generate biological designs for toxins, pathogens, or other biological agents that may 
be more harmful than those found in nature. Moreover, the design of new types of biological 
agents could raise the ceiling of potential harms and evade synthetic nucleic acid screening 
by redesigning known hazards.  

Sandbrink  (2023)  declares  that  the  combination of  both  tools  could  significantly  expand  the 
capability and accessibility to manipulate biological agents, increasing the risks of nefarious 
actions.  Nevertheless,  he  emphasises  the  need  for  evidence  to  confirm  whether  these 
tools actually lower barriers to biological weapons creation. Some research groups are 
actively researching the risks of AI-assisted bioterrorism including the use of LLMs, but model 
evaluations have limitations as a means for ensuring safety.  

Experts  highlight  that  AI  tools  can  also  be  used  to  spread targeted  disinformation  that 
may  erroneously  influence  decision-making  around  response  to  biological  threats  or 
compliance with the BWC obligations and international collaboration. In terms of biosecurity 
and collaboration, experts also noted that AI tools could also be used to identify gaps and 
vulnerabilities  in  healthcare  and  threat  response  systems  that  can  be  exploited  by 
adversaries.  

The literature also explores the mitigation measures that could be put in place to reduce the 
identified risks of AI for biosecurity, like the release of AI models tested for safety. However, 
some  authors  claim  that,  even  though  responsible AI  laboratories  may  release  biologically 

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Leveraging AI tools for a strengthened biosecurity framework 

capable AI models only when they have been trained to be safe, skilled actors could overcome 
these guardrails through jailbreaks or other means (Mazeika, et al., 2024) (Qi, et al., 2023). 
Moreover,  other  authors  underlined  that  the  availability  of  open-source  codes  could  render 
even capable AI models subject to malicious actors who can remove such safeguards (Gopal, 
et al., 2023). Finally, experts recall that AI tools produce digital outputs rather than physical 
ones. Creating a biological agent or substance (including biological weapons) requires access 
to laboratories, production equipment, and technically proficient personnel which is difficult to 
obtain. 

Table 2 presents some of the key insights from the literature on the risks that AI-enabled tools 
pose  to  the  advancement  of  biotechnology,  synthetic  biology  and  biosecurity,  especially 
considering a non-state actor perspective. 

Ta b le  2 Key risks pose d by AI for biose curity 

Risk  

Chatbots may facilitate 
knowledge access to create 
biological threats. 
Biological Design Tools 
(BDTs) may provide 
additional capabilities to 
users with malicious intents 

BDTs may help generate 
toxins or pathogens that are 
not well characterised in the 
scientific literature, making 
it more difficult for nucleic 
acid providers to conduct 
effective screening of 
synthesis orders. 

Current safeguards that 
prevent the risk from 
materialising 

Potential additional mitigation 
strategies 

Source 

•  Availability of the 

•  Mandatory synthetic nucleic 

appropriate infrastructure 
(laboratory, reagents, 
etc.) 

•  Practical knowledge of 

acid order screening  

•  AI model evaluation (pre and 

post release) with independent 
audits 

how to work in a 
laboratory 

•  Access to funding 
•  Resources required for 
testing BDT’s candidate 
designs (time, funding, 
expertise, etc.) 

•  Controlled access to AI 
models (authentication) 
and to the infrastructure 
needed to train them  

•  Improvement of screening 
tools to keep pace with 
biodesign development 
•  Include protein structure 

predictor in screening methods  

•  Train AI models to refuse to 

engage in specific topics (i.e. 
“red-teaming” to assess 
misuse potential) 

O’Brien and 
Nelson (2020) 

Carter et al 
(2023a)  

Batalis et al 
(2024) 

De Haro (2024) 

O’Brien and 
Nelson (2020) 

Carter et al 
(2023a) 

De Haro (2024) 

Source: author’s elaboration based on literature review findings. 

2.2.1.  Considerations from the experts 

The  findings  above  present  the  information  gathered  from  reviewing  the  literature.  These 
results have been further discussed and validated with experts in the field, who provided an 
overview of the risks of AI for biosecurity. Box 3 presents the main learnings from the interviews 
conducted  with  several  experts  belonging  to  Academia,  Industry,  Government,  and 
International Agencies.  

Box 3 Conside rations from the e xpe rts í  The  pote ntial risks pose d by AI to biose curity 

Highlights resulting from Experts’ consultation 

Experts coming from industry, academia and government also pointed to the inherent risks 
of using this technology within the biosecurity field. Indeed, AI-enabled biological misuse 
was  considered  by  several  experts  as  a  huge  risk,  i.e.  the  potential  of AI  models  being 
used to come up with new sequences of concerns that might cause harm. One academic 
and industry expert recognised the need to ensure that future models do not suggest ways 
of building nastier things than nature does. LLMs that are as good as any living biotech 
researcher at thinking about new designs and better at considering security matters can 
be a major concern.  

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Leveraging AI tools for a strengthened biosecurity framework 

In addition to this, several experts pointed to the risk of AI allowing to troubleshoot tasks. 
According to them, tacit knowledge barriers, i.e. having pragmatic knowledge of working in 
a laboratory, have contributed to making the development of deadly pathogens harder but 
this is changing with AI. Being able to evaluate the level of capabilities that AI models offer 
or how much uplift they're able to offer actors is an important part of being able to monitor 
for AI risks. According to one policy expert, in the short term, the risks may outweigh the 
benefits. Then in the longer term, AI could be the biggest asset to security. 

Another  major  risk  related to the  use  of AI  for  intelligence  gathering  is misinformation, 
both in terms of diluting important information, as well as creating false data.  

Academic and policy experts also mentioned relevant ethical considerations and risks of 
integrating AI  in  this  field,  especially  when  using  clinical  data.  There  are  important  data 
privacy  concerns  on  giving  access  to  automated  algorithms  to  databases  with  health 
information.  

2.3.  AI-enabled  tools  for  an  enhanced  screening  of  synthetic 

nucleic acid orders  

These are the main takeaways on the role of AI for enhancing the screening of synthetic 
nucleic acid orders. 

•  The study identified seven tools being used or under development for the screening 
of synthetic nucleic acid orders. Four of the identified tools have some AI techniques 
integrated within their operating methods or databases. The other three use classical 
computational methods often in the form of alignment algorithms.  

•  Given the voluntary nature of screening synthetic nucleic acid orders, providers are 
not incentivised to improve their current screening methods integrating AI. However, 
experts consider that this may rapidly change considering the new US legislations to 
strengthen biosecurity (see section Error! Reference source not found.).    

•  Looking  at  the  future, AI  has  the  potential  to  enhance  the  screening  of  synthetic 
nucleic acid orders in two ways:. i) augmenting reference data sets with sequences 
that  do  not  exist  in  nature  and  thus  increasing  the  possibilities  to  screen  also  for 
sequences  of  novel  AI-generated  proteins  and  ii)  enabling  real-time  structure 
prediction, which is currently computationally resource intensive. 

The general lack of specific requirements that mandate the screening of synthetic nucleic acid 
orders,  leaves  providers to  voluntarily  implement  screening  procedures  based  on  high-level 
protocols and recommendations. A recent study by Kane and Parker (2024), which interviewed 
providers and associated stakeholders from 18 organisations, highlights significant variability 
in screening practices and a general lack of regulation and effective governance.  

Many providers either do not screen synthetic oligonucleotides or lack procedures for reporting 
suspicious orders to law enforcement. The study found that the most common tool used for 
screening of potential orders against SOCs was the US National Centre for Biotechnology 
Information’s (NCBI) Basic Local Alignment Search Tool (BLAST) integrated into automated 
algorithms. BLAST searches for regions of significant similarity between nucleotide or peptide 
sequences  and  databases.  With  many  biological  applications,  it  can  also  aid  providers  in 
screening orders (Altschul, Gish, Miller, Myers, & Lipman, 1990).  

Besides this basic bioinformatic tool, the study team identified six advanced tools available on 
the  market  for  screening  synthetic  nucleic  acid  orders. The  tools  are  described  below. This 

19 

 
 
 
 
Leveraging AI tools for a strengthened biosecurity framework 

section presents the outcomes of the scoping literature review focused on the potential use of 
AI to improve synthetic nucleic acid orders’ screening tools or processess.  

• Aclid  Platform  performs  customer  screening  and  alignment-based  sequence  screening 
integrating automated and AI-driven data curation (Wheeler, et al., 2024a). The Aclid Platform 
uses  protein  language  models  like  ESM  to  generate  embeddings  to  search  for  similar  or 
homologous  proteins  in  vector  space  and  for  predicting  structures  to  perform  structural 
alignment.  The  outputs  from  the  searches  and  predictions are  used  to  annotate  their 
database, improving their false-positive and false-negative rates. 

• Common Mechanism for DNA synthesis screening (Beta version), an open-source, free, 
globally available tool for DNA synthesis screening to help providers of synthetic DNA and 
RNA effectively screen orders. It provides automated software for screening sequences of 
nucleic acids and resources like guidelines to facilitate customer screening. It is intended as 
a common standard and baseline for screening providers worldwide (Wheeler, et al., 2024b). 

• Fast-NA Scanner, a proprietary software designed to integrate with workflows for screening 
nucleic  acid  sequences  of  more  than  50  base  pairs,  delivering  results  about  1,000  times 
faster than BLAST. It uses Bloom filters, a probabilistic data structure allowing some false 
positive but no false negatives, to detect k-mer6 signatures unique to specific pathogen or 
toxins.  While  it  does  not  handle  customer  screening,  the  company  recommends  using 
established customer screening systems from the finance industry (Beal et al., 2019) (Beal 
et al., 2022) (Beal et al., 2023).  

• SecureDNA,  a  free,  non-profit  screening  platform  designed  to  safeguard  DNA  synthesis 
while protecting customer data. It uses a privacy-preserving cryptographic algorithm to check 
short  DNA  subsequences  from  the  orders  (20  aminoacids)  against  a  database  of  known 
hazards or predicted functional variants of controlled genes. Its database comprises hashed 
k-mers  from  conserved  regions  of  SOCs  and  mutated  variants  that  maintain  biological 
function,  enabling  SOC  inclusion  without  revealing  their  identity.  The  pricacy-preserving 
mechanism can protect trade secrets and shield research projects (Baum, et al., 2024). 

• SeqScreen  is  a  tool  that  beyond  the  taxonomic  identification  of  known  pathogens,  it  also 
reports functional threat assessments for both known and emerging pathogens. It feeds local 
alignments  into  a  machine  learning  classifier  trained  to  predict  functional  categories  (ie., 
“confers  antibiotic  resistance”)  providing  insights  into  why  a  particular  sequence  was 
identified as concerning (Alexanian & Langenkamp, 2024). Machine Learning techniques are 
being considered to further automate a portion of this work by predicting a suite of biological 
processes  of  interest  on  large  protein  databases  before  incorporation  into  the  SeqScreen 
framework (Hoffmann, et al., 2023).  

• UltraSEQ uses an information-driven alignment approach combined with machine learning 
to  identify taxonomic  best  matches  and  SOCs. Advanced  algorithms  are  used  to facilitate 
organism  and  function  identification.  The  associated  metadata  is  the  result  of  years  of 
government-funded curation efforts, reflecting the significant investment required to develop 
a comprehensive set of annotated sequence records (Gemler, et al., 2023). 

A  pilot  project  aiming  to  develop  a  test  set  for  evaluating  the  efficiency  of  screening  tools 
showed that results varied significantly for certain genetic sequences (i.e. bacterial and fungal). 
This  heterogenity  reflecting  the  variability  and  lack  of  uniformity  among  screening 
practices (Wheeler, et al., 2024a). 

6 k-me r lookup is a bioinformatics approach which bre aks up se que nce s into small sub-se que nce s of length k (èk-
me rsê), and the n flags exact matche s betwe e n the  k-me rs and se que nce s in a give n  database.  

20 

 
 
 
 
 
Leveraging AI tools for a strengthened biosecurity framework 

Table  3Error!  Reference  source  not  found.,  below,  provides  a  comparison  of  the  current 
available  tools  to  perform  screening  of  synthetic  nucleic  acid  orders.  Given  the  proprietary 
nature of such tools and the information hazards linked to exposing specific techniques applied 
to screening, in-depth information on the AI algorithms and systems integrated in such 
tools is  often  difficult to  obtain.  The table,  based  on the  publicly  available  data  enriched 
through  consultation  activities  with  experts  in  the  field,  provides  and  overview  of  how 
computational/AI techniques are currently integrated in these tools and processes.  

2.3.1.  Challenges of AI used for synthetic nucleic acid order screening  

Current screening solutions still require manual review of suspicious sequences, which can be 
complex  to  interpret  and  thus  increase  costs  for  providers  (Diggans  &  Leproust,  2019). 
However, synthetic nucleic acid screening companies have started integrating AI algorithms 
into  their  screening  methods  in  preparation  to  the  development  of  the  sector  and  new 
regulatory mechanisms to enhance biosecurity.  

Moreover, Lee and colleagues (2022) have developed a conceptual framework based on 
AI, the Biosecurity AI Network (BAIN), that proposes to enhance biosecurity by integrating 
AI  into  current  screening  procedures  of  synthetic  nucleic  acid  and  peptide  orders  against  a 
database with genomic and proteomic data of known pathogens, toxic peptides and proteins. 
The system could use AI and machine learning for predicting sequences’ biological function “in 
silico”  and  to  compile  customer  profiles,  orders  and  screening  outcomes to  build  a  network 
map  with  user-submitted  data  and  open  sources  to  help  detect  anomalies.  BAIN  aims  to 
leverage AI to provide a more robust approach to biosecurity, addressing the evolving 
challenges posed by advances in synthetic biology and genetic engineering. 

Besides the promise of AI to enhance synthetic nucleic acid orders’ screening, the literature 
identified  four  main  challenges  for  the  integration  of  AI  for  synthetic  nucleic  acid  order 
screening. The main challenges are described below: 

•  Scientific  uncertainties.  The  unknown  role  of  certain  sequences  in  pathogenicity, 
combined  with  the  evolving  nature  of  biological  systems,  means  that  any  set  of 
concerning  sequences  must  be  regularly  updated  and  therefore  AI  systems  might 
require re-training (Wheeler, et al., 2024b). 

•  Complex  sequence  categorisation.  Scientific  discoveries  about  pathogenicity 
sequences  are  often  poorly  integrated  into  databases.  This,  coupled  with  the 
heterogeneity on screening methods and various computational approaches leads to 
inconsistencies in identifying potentially hazardous sequences, making it challenging 
for  manufacturers  to  make  decisions  based  on  such  inconsistent  and  sometimes 
untraceable information (Kane & Parker, 2024). 

•  Quality control of training databases. The effectiveness of AI models relies on the 
quality of the training data. Predictions may be inaccurate or biased if the training data 
is skewed or of low quality. Given that data consistency and reliability can vary across 
sources,  ensuring  high-quality  training  data  is  essential  for  accurate  AI-enhanced 
screening (Diggans & Leproust, 2019). 

•  Technical  challenges  and  increasing  costs  for  providers.  The  newly  proposed 
HHS  guidance  has  expanded  recommendations  for  screening  to  shorter  sequences 
(20 base pairs for high-mass orders (>1 mM)). This poses a new technical challenge 
and increasing costs, as the probability of false similarity between orders and SOCs 
increases as sequence length decreases (Hoffmann, et al., 2023). 

21 

 
 
 
Ta b le  3 Comme rcially available  tools for the scre e ning of synthetic nucle ic acid orde rs. 

Name 

Computational/ AI techniques 

Data analysed 

Accuracy  

Target 

Availability 

Source 

Aclid platform 

AI-driven data curation. Use of 
protein language models (i.e., 
ESM)  

Nucleic acid & protein 
sequences and 
customer screening 

0.2-0.5% false-positive 
rates. Potential to go 
down as low as 0.05%. 

DNA screening 

On the market by 
Aclid  

(Wheeler, et al., 
2024a) 

BLAST 

Common 
Mechanism for 
DNA synthesis 
screening 

Fast-NA 
Scanner 

Heuristic algorithm estimating 
best matches for a query 
sequence against a sequence DB 

NCBI nucleic acid and 
protein sequences 

N.A. 

Sequence alignment 
(not designed for 
biosecurity 
purposesonly) 

For research 
[publicly available] 

(Altschul,  Gish, 
Miller,  Myers,  & 
Lipman, 1990) 

Compares sequences against a 
'biorisk' database equipped with 
ML models (HMM
profiles of SoC 

) capturing 

7

Publicly available data; 
standard protein and 
nucleotide DB 

<2% false positive flags 
of real synthetic biology 
designs 

DNA screening 

For research and 
industry providers 
[publicly available] 

(Wheeler, et al., 
2024b) 

Based on the Framework for 
Autogenerated Signature 
Technology originally developed 
for the detection of malware in 
network traffic  

Nucleic acid sequence 
data 

<2% false positive rates  DNA screening 

SecureDNA 

Shamir's secret sharing (SSS) 
algorithm  

nucleic acid and 
peptide subsequence 

Negligible false alarm 
rate (reverse screening 
system that removes 
known non-hazards 
from the database) 

Cryptographic DNA 
screening; compatible 
with benchtop 
synthesizers 

On the market by 
RTX BBN 
Technologies 

(Beal et al., 
2019) (Beal et 
al., 2022) (Beal 
et al., 2023) 

For research and 
industry providers 
[publicly available] 

(Baum, et al., 
2024) 

SeqScreen  

UltraSEQ 

ML based classification: 
techniques: L1-regularization 
(Support Vector) and dropout 
(Neural Networks) to balance 
weights and reduce overfitting 
Information-theory-based 
approach combined with ML 
algorithms  

Both nucleic and amino 
acid sequences 

N.A. 

nucleic acid screening 
and dataset analysis 
(metagenomics)  

N.A. 

Source: author’s elaboration based on literature review findings. 

Not explicitly designed 
for identifying 
biosecurity threats 

For research and 
industry providers 
[publicly available] 

(Balaji, et al., 
2022) 

DNA and metagenomic 
screening 

On the market by 
Battelle 

(Gemler, et al., 
2023) 

7 Hidden Markov Models, a statistical model that represents systems with unobserved states, where each state generates observable data based on specific probabilities. It is 
commonly used for sequence prediction, such as in bioinformatics. 

 
 
 
 
2.3.2.  Considerations from the experts 

The findings in Section 2.3 were significantly complemented through the consultation activities 
with key experts, which allowed to validate the identified AI solutions and tools for the screening 
of synthetic nucleic acid orders. The main input from interviewed experts is summarised in Box 
4.  

Box 4 Conside rations from the e xpe rts: AI-base d tools for an e nhance d scre e ning of synthetic nucle ic acid orde rs 

Highlights resulting from Experts’ consultation 

Several industry, academic and policy experts working in this field highlighted that there 
are opportunities for AI to make the screening of synthetic nucleic acid orders better 
but currently it’s not clear how challenging this is going to be.  

AI’s potential for enhanced screening 

One government expert in the EU underlined that AI is going to be both an enabler and 
the  only  way  that  we  can  screen  orders  in  the  future. Another  expert  coming  from 
academia  and  working  for  a  nonprofit  organisation  on  biosecurity  highlighted  that  the 
combination of order and customer screening to arrive at a decision is more likely to 
require machine learning techniques and AI because it is too complex for a human actor 
to  be  able  to  detect  sequences  of  concern,  especially  if  there  are  any  attempts  to 
obfuscate  the  sequences,  e.g.  slight  twitches  to  nucleotides  that  would  obfuscate  the 
order.  Indeed,  several  experts  coming  from  academia,  government  and  industry 
highlighted the potential for AI to go beyond list-matching when looking at sequences 
given  that  list-based  approaches  may  have  limitations  when  considering  new  threats. 
According  to  several  experts  from  academia  and  policy,  new  AI  designs  can  make 
proteins that have the same structure and therefore have the same function but have a 
very different primary DNA sequence. AI in synthetic nucleic acid orders screening can 
be useful to detect genetically engineered organisms that are not present in known lists 
or  whether  there  is  any  kind  of  potential  harm  in  a  particular  sequence  (better 
understanding of the function, identification of obfuscation, etc.). 

A DNA synthesis provider also highlighted how the screening of orders works for a 
company in practice: customers order to the company, they send the sequence over 
secure encrypted channels to the screening provider, which generates threat assessment 
data  and  sends  it  back  to  the  data  synthesis  company  that  reviews  the  information  to 
make  final  decisions.  The  automated  system  cannot  make  a  final  decision,  it  only 
generates data to support human decision making. At the end of the day, a human must 
decide whether something crosses a threshold in terms of having to follow up with the 
customer, asking them what they're doing with the order or if it requires an export license.  

Policy and academic experts shed further light into the functioning and development of 
the  common  mechanism  under  the  International  Biosecurity  and  Biosafety 
Initiative  for  Science  (IBBIS).  The  project  came  out  of  Nuclear  Threat  Initiative,  a 
nonprofit global security organization focused on reducing nuclear and biological threats 
imperilling humanity, which set up an international working group with people coming from 
the policy side, from the gene synthesis industry and from the bio security community. 
The  mechanism  is  not  doing  a  direct  sequence  comparison,  but  it  uses  a  probability 
matrix for any given amino acid being encoded within that genetic information. Instead of 
looking at the sequence of concern, it has developed Hidden Markov ModelsError! Bookmark n
ot defined. of specific sequences that tell you which amino acid residues are important for 
maintaining  the  structure  and  the function  of that  sequence. The  common  mechanism 
restricted its database to a smaller set of sequences that they focus on, i.e. only things 
that are found in regulated pathogens on already published and well-established lists of 
sequences of concern.  

 
 
Leveraging AI tools for a strengthened biosecurity framework 

Challenges 

Both academic, government and policy expert working on these topics underlined that it 
is inherently challenging to integrate AI and biology projects because there are very 
few people who are truly experts in both fields. Several of the interviewed experts working 
on  these  topics  struggled  to  find  European  experts  in  the  field  who  deal  with  AI, 
biotechnologies and security.  

With  regards  to  the  challenges  linked  to  screening,  several  experts  working  in  the 
industry  agreed  that  the  key  challenge  is  not  technically  doing  the  screening,  but 
agreeing  on  what  order  should  be  fulfilled  and  the  criteria  for  fulfilling  that  order. 
Currently there is no certification process yet and several experts reported that NIST is 
working on establishing it in the US. Few policy experts highlighted that a barrier to the 
use of screening tools is the fact that companies might be worried that it makes them 
liable for misuse. Several providers reported that it is extremely rare for them to reject an 
order, and this has become part of the problem with biosecurity screening. One industry 
expert mentioned that, within the IGSC, they have a mechanism for order reporting, but 
it's been used once since 2009, and the challenge resides in the fact that companies are 
reticent about disclosing customers due to competition. Internal discussions have started 
with respect to the possibility of having a neutral third party to report to or some blinding 
method where one submits that information to a mathematical process and then if another 
party submits the same information, then it's unblinded.  

One of the challenges of AI for synthetic nucleic acids screening detection mentioned 
by  both  industry  and  academic  experts  is  that  a  malicious  actor  could  order  nucleic 
acids in pieces and defeat all screening. This means that there is a trivial way by which 
any educated adversary could get around it and good enough de novo protein design will 
eventually  obviate  synthetic  nucleic  acid  screening.  One  industry  representative 
highlighted that synthesis companies are worried about integrating AI into their models 
as  they  may  run  into  the  same  unpredictability  seen  in  AI  models  within  other 
domains, i.e.  do not want to risk that 5% of the time the model may miss or forget some 
part of the embedding space. At the same time, experts mentioned that companies don't 
want  all  the  developments  in  this  field  to  be  fully  known  because  they  create 
vulnerabilities for companies. 

An academic expert mentioned that a greater focus amongst tool providers should be to 
make screening robust to AI circumvention as opposed to applying AI directly to 
build better tools. Afterwards, there will be a longer-term aim to figure out how to use AI 
tools inside the screening tools themselves to develop more sophisticated approaches to 
screening and have a better set of data against which to screen. The implementation of 
a good tool would be expensive as it would require in-depth testing. 

With  respect  to  benchtop  devices,  one  government  expert  underlined  that  one  option 
would be to have machines equipped with software and algorithms preventing them 
from synthesizing sequences that can have a dangerous function.  

New Regulations in place 

Academic  experts  and  industry  representatives  also  provided  their  view  on  the  United 
States executive actions to require that federally funded projects are from a company that 
screens orders. It was highlighted that different groups are covered by this, i.e. not only 
DNA  and  RNA  providers  but  also  contract  research  organizations  working  for  pharma 
companies,  university  core  facilities,  or  synthesizer  companies  that  make  desktop 
machines. According to both academia and industry, this regulation is a very low bar, but 
it's a first step towards the general acceptance of standards within the industry. Indeed, 
they underlined that it is a voluntary statement that the company must make saying that 
they  meet  the  criteria. According  to  several  experts,  AI  has  been  a  driving  threat  to 
allow people to become aware of the fact that it would not be possible to defend against 

24 

 
 
 
Leveraging AI tools for a strengthened biosecurity framework 

the  misuse  of AI  unless  these  other  things  are  implemented. Academic  and  industry 
experts  are  expecting  the  biggest  uptake  of  screening  tools  over  the  next  couple  of 
months with the change in the US rules. They also highlighted that two years from now 
there will be a certification process in place based on the activities by the IGSC Working 
group. Indeed, through IBBIS there has been broad collaboration across screening tool 
providers  on  benchmarking,  to  better  understand  the  individual  strengths  and 
weaknesses of the different tools.   

2.4.  AI-enabled  tools  to  improve  the  detection  and  attribution  of 

genetic engineering  

2.4.1.  AI-based tools to enhance the detection of genetic engineering 

These  are the  main takeaways  on  the role  of AI-based  tools to  enhance  the  detection  of 
genetic engineering. 

•  The study identified three tools, two of which are AI-based, currently being used or 
in development for detecting genetic engineering (Plaster, Synsor and ENDAR). 

• 

Interviews  with  experts  were  conducted  to  evaluate  the  current AI  tools  used  for 
genetic engineering detection.  

•  The  identified  tools  employ  deep  learning  techniques  (Synsor)  or  alignment-free 

methods (ENDAR). 

Our study highlights the need for integrating high-quality DNA sequence datasets to train 
deep learning models and suggests a combined approach using multiple detection methods 
to enhance detection rates. 

Current  developments  in  synthetic  biology  facilitate  the  generation  of  novel  biological 
components.  These  novel  biological  components  pose  a  risk  to  public  health  either  by  an 
accidental  introduction  into  the  environment  or  the  possible  misuse  or  abuse  by  malicious 
actors.  Therefore,  to  safeguard  security  and  public  health,  it  is  essential  to  monitor  the 
presence of genetic engineering in biological systems. This is a promising approach for 
an early detection of emerging biothreats. Different computational and experimental tools and 
protocols  have  been  developed  to  identify  signals  of  genetic  engineering.  However,  the 
characterisation of genetic engineering remains challenging (Tay, et al., 2024).  

New  methods  are  needed  to  enable  a  more  targeted  approach  to  identify  engineered 
DNA in biological systems. This study has identified three tools currently being used or under 
development for detecting genetic engineering. The tools are presented in detail below and 
summarised and compared in Table 4. 

Wang and colleagues (2019) developed an efficient and explainable method to detect 
signatures of genetic engineering in plasmids. Plasmids are small, circular, DNA molecules 
that are easy to modify and commonly used in biological research to transfer novel functions 
between species. Their method is based on the “pan-genome” concept, where a pan-genome 
sequence is a compact reference sequence capturing all the existing variation in a group. To 
achieve  this,  they  developed  a  new  pan-genome  construction  algorithm  called  Plaster.  The 
Plaster  algorithm  generated  two  reference  pan-genomes:  the  synthetic  one  capturing  the 
diversity from 51,047 synthetic plasmid sequences, and the natural one capturing the diversity 
from 6,642 natural plasmid sequences. By comparing unknown plasmid sequences to these 
reference  pan-genomes,  the  method  can  distinguish  whether  a  plasmid  is  natural  or 
engineered. Their research demonstrated that the Plaster algorithm generated pan-genome 
sequences  that  permitted  the  differentiation  of  synthetic  and  natural  plasmid  sequences 
(Wang,  Elworth,  Liu,  &  Treangen,  2019).  This  approach  did  not  integrate  any  ML 

25 

 
 
 
Leveraging AI tools for a strengthened biosecurity framework 

algorithms,  reinforcing  the  explainability  of the  model.  The  authors described that their 
initial approach to genetic engineering detection would lead to new techniques and approaches 
for rapid characterization of engineered plasmids.  

Tay and colleagues (2024) developed a tool for alignment-free detection of engineered 
DNA  sequences  using  an  artificial  neural  network.  The  tool  relies  on  the  differences  in 
oligonucleotide frequency (k-mer signature) between naturally occurring and engineered DNA 
sequences. Tay and colleagues developed Synsor based on the assumption by Allen et al. 
(2008) that engineered DNA sequences contain k-mer signatures notably different to natural 
occurring sequences. Synsor uses artificial neural networks that capture complex non-
linear  relationships  and  can  classify  whether  a  query  DNA  sequence  is  natural  or 
engineered through pattern recognition of k-mer signatures. Synsor is one of the first tools 
designed  for  genetic  engineering  detection  whose  capacity  to  detect  potential  genetically 
engineered organisms has been demonstrated with over 99% accuracy and which does not 
rely  on  sequence  alignment,  opening  the  way  for  new  methods  using  deep  learning  and 
artificial neural networks (Tay, et al., 2024). 

Indicators  project),  which  aims 

In  addition  to  academic  tools,  the  literature  highlights  IARPA’s  FELIX  Project  (Finding 
Engineering-Linked 
to  enhance  biodetection  and 
biosurveillance by developing new methods to identify indicators of genetic engineering (Lewis, 
et  al.,  2020).  One  solution  funded  under  this  initiative  is  ENDAR  (Engineered  Nucleotide 
Detection  and  Ranking),  a  software  tool  developed  by  Ginkgo  Bioworks,  a  Boston  biotech 
company, and Draper, a nonprofit engineering innovation firm. ENDAR uses deep learning 
to  help  trained  analysts  identify  genetic  engineering  in  next-generation  sequencing 
(NGS) data. It is designed to work across various biological organisms found in complex, multi-
species environments. According to its developers, ENDAR significantly improves the signal-
to-noise  ratio  compared  to  previous  methods,  which  could  allow  for  earlier  detection  of 
engineered organisms. The project’s outcomes were announced in a press release in 2022 
(Kelly, 2022), but no scientific publication has yet detailed their findings and technical aspects 
of the developed machine learning solution. 

Ta b le  4 AI-base d tools identi˛ ed for the dete ction of ge netic engineering. 

Accuracy  

Target 

Availability 

Source 

Name 

AI technique 

Plaster: pan-
genome 
construction 
algorithm 

Alternative to 
deep learning - 
Basic clustering 
methods 

Type of 
genetic 
material 
analysed 

Addgene 
database 
(plasmids) 

Information 
not available 

Detection 
of GE 

Synsor 

Deep learning - 
artificial neural 
network  

Addgene 
database 
(plasmids) 

>99% 
accuracy 

Detection 
of GE 

ENDAR 

Deep learning 

Information 
not available 

Information 
not available 

Detection 
of GE 

Source: author’s elaboration based on literature review findings. 

For research 
[source code 
publicly 
available] 
For research 
[source code 
available under 
GPL v3 license] 
On the market 
by Ginko 
bioworks 

Wang et al., 
2019 

Tay et al., 
2024 

Unpublished 

The literature also considers the key challenges and considerations for using AI models 
to detect genetic engineering. It emphasises the need for high-quality DNA sequences to 
train  deep  learning  models  and  the  trade-off  between  model  performance  and  required 
computational  resources.  Some  authors  propose  combining  multiple  detection  methods  to 
improve  detection  rates  (Tay,  et  al.,  2024)  (Crook,  et  al.,  2022).  Additionally,  concerns  are 
raised about open-source tools potentially aiding individuals in designing genetic elements that 

26 

 
 
 
Leveraging AI tools for a strengthened biosecurity framework 

could evade detection. Some authors advocate for developing more comprehensive, closed-
source versions of the tools (Tay, et al., 2024).  

2.4.2.  AI-based tools to enhance the attribution of genetic engineering 

These are the main takeaways on the role of AI-based tools to enhance the attribution of 
genetic engineering. 

• 

• 

Identification of six AI-based tools for genetic engineering attribution. The most used 
AI technique is Convolutional Neural Networks, with employing traditional clustering 
methods and Random Forest. 

Interviews  with  experts  in  the  field  validated  the  identified  challenges  such  as 
uncertainty  about  real-world  performance  and  the  tools’  lack  of  explainability,  that 
might hinder adoption and action upon their results. 

•  Experts interviewed agreed on the importance of considering these methods as only 
one piece of information that should be combined with other evidence or surveillance. 

•  All  the  identified  tools  were  mainly  developed  between  2018  and  2022,  but 
progress has stalled. Experts suggest that the commercial potential of the tools 
(i.e., as a solution for intellectual property rights in genetic engineering) could act 
as a catalyst for future developments. 

The previous section describes the different tools and methods developed for the detection of 
genetic engineering. Other approaches aim to improve biosecurity by the attribution of genetic 
engineering  to  the  source  laboratory  or  designer.  The  ability  to  determine  who  is  the 
responsible actor of a human-caused biological could be detrimental to the misuse of 
genetic engineering (Alley, 2021) (Lewis, et al., 2020) (Carter et al., 2023a).  

The attribution of genetic engineering relies on the idea that scientists who perform genetic 
modifications leave behind unique ‘methodological signatures’. These signatures come from 
specific design choices, such as a preference for certain genetic elements or specific codon 
optimization methods, along with evolutionary markers. These signatures can be used to infer 
attribution of the genetic construct (Lewis, et al., 2020). Because the data is vast and complex, 
advanced analysis is necessary. To meet this need, researchers have developed machine 
learning  techniques  (Alley,  2021).  The  tools  are  described  below  and  summarised  and 
compared in Table 5. 

In  late  2018,  Nielsen  and  Voigt  (2018)  demonstrated  that  deep  learning  could  be  used  to 
interpret engineering methodological signatures to predict the laboratory of origin of genetic 
engineered DNA sequences. In this first attempt, a convolutional neural network (CNN) was 
trained  on  the Addgene  dataset  that  at  the  time  contained  engineered  plasmids  from  2230 
laboratories.  The  trained  CNN  was  used  to  establish  the  origin  of  DNA  sequences.  The 
accuracy was of the 48% for of the laboratory-of-origin prediction but increased up to a 70% 
for the top 10 predicted laboratories. This method, on average, allowed for variations in ~9% 
of the sequence without a substantial reduction in the predictive accuracy (Lewis, et al., 2020) 
(Nielsen  &  Voigt,  2018).  This  work  showed  that  genetic  engineering  patterns  can  guide 
attribution without manual feature selection or sequence function data, opening the door 
for future research. 

Two  years  later, Alley  and  colleagues  (Alley,  2021)  proposed  the  use  of  recurrent  neural 
networks  (RNNs) over  CNNs  for  their  suitability  in  detecting  and  predicting  patterns  in 
sequential  data  and  handling  variable-length  sequences.  The  team  used  techniques  from 
natural language processing (NLP) to extract features from the sequence. More specifically, 
Byte-Pair  Encoding  (BPE)  algorithm  was  used  to  infer  1000  salient  motifs  directly  from  the 
Addgene primary DNA sequences originating from over 1400 labs. These motifs were used for 

27 

 
 
 
 
Leveraging AI tools for a strengthened biosecurity framework 

the training of the RNN together with some basic phenotypic information which improved the 
accuracy  of  the  model.  The  resulting  model,  deteRNNt,  reached  70.1%  accuracy  for  the 
prediction  of  the  laboratory  of  origin  and  84.7%  top  10  accuracy  (Alley,  2021). To  enhance 
usability,  the  team  developed  a  framework  to  weight  predictions  against  other  investigative 
evidence improving robustness and uncertainty estimation for integration with other attribution 
indicators. They also demonstrated that simple classification models can accurately predict the 
nation and region of origin and “genealogies” of laboratories. This is particularly relevant for 
the  Biological  Weapons  Convention  (BWC)  and  to  expand  the  identification  of  designer 
laboratories beyond those in the training dataset (Alley, 2021). These methods resulted in the 
development  of  an  integrated  attribution  toolkit  of  machine-learning  algorithms, 
designed to enhance biosecurity and support biotechnology stakeholders in promoting 
responsible innovation (Alley, 2021) (Lewis, et al., 2020). Despite its improved accuracy, the 
toolkit  relies  on  high-quality  training  data  and  while  Addgene  attribution  data  provide  a 
reasonable model scenario, it is biased because of the limitation of plasmid sequences and its 
geographical location in the US. Additionally, machine learning models can be costly and lack 
explainability, often obscuring the features and decision-making process generating a given 
classification or prediction (Holzinger, 2018). 

In parallel to the development of the deteRNNt model, Wang and colleagues (2021) developed 
PlasmidHawk,  an  alternative  alignment-based  approach  for 
laboratory-of-origin 
detection independent of machine learning techniques, overcoming some of the limitations 
presented above. PlasmidHawk builds a pan-genome from the synthetic plasmid training data 
(Addgene)  using  Plaster,  the  pan-genome  construction  algorithm  presented  above  (Wang, 
Elworth,  Liu,  &  Treangen,  2019).  The  generated  synthetic  pan-genome  is  annotated, 
connecting unique regions with information on laboratory of origin. PlasmidHawk can predict 
the  laboratory-of-origin  of  unknown  plasmids  by  performing  sequence  aligning  between  the 
query unknown sequences and the annotated synthetic pan-genome. This approach led to an 
accuracy of 76% for top 1 prediction and 85% accuracy in the top 10 predictions (Wang, Kille, 
Liu, Elworth, & Treangen, 2021). This study demonstrated that alignment of query sequences 
to specifically designed pan-genomes can effectively predict laboratory-of-origin of synthetic 
sequences while providing interpretable prediction results, a benefit compared to the currently 
limited  explainability  of  ML-based  methods.  The  authors  highlighted  the  importance  of 
continuing the study of traditional comparative genomics methods to complement the 
more recent advances in AI and ML methodologies. 

Based on the alternative approach to the laboratory-of-origin issue proposed by Wang et al. 
(2021),  researchers  that  worked  on  the  development  of  the  deteRNNt  collaborated  in  the 
organisation  of  the  first  Genetic  Engineering  Attribution  Challenge  (GEAC)  to  explore 
additional and improved solutions to the matter (Crook, et al., 2022). The citizen science 
initiative resulted in 40 teams outperforming the top 1 prediction accuracy, while over 70 teams 
improved the top 10 prediction accuracy of laboratory-of-origin and compared to the existing 
models at the time. Some of the most promising results included a nearly 95% accuracy for 
top  10  prediction  and  81.9% for the  top  1  prediction  using  a  CNN-based  approach,  and  a 
1000-fold increase in speed by a high accuracy method based on a naive Bayes classifier 
instead  of  CNN  (Crook,  et  al.,  2022).  Despite  the  encouraging  promising  results  of  the 
competition,  the  GEAC  did  not  focus  on  the  calibration  of  the  models,  and  therefore,  the 
resulting models were much less well-calibrated than the deteRNNt model developed by Alley 
et al. (2021). The study highlights the gap between the competition-based attribution challenge 
and real-world GEA applications. While the Addgene database offers a good testing ground, 
its simplicity limits real-world applicability, requiring further research to generalise attribution 
models (Crook, et al., 2022).  

In  parallel,  in  2022,  a  different  research  group  introduced  a  new  approach  to  GEA.  They 
developed a state-of-the-art classifier and a training methodology for interpretable GEA. Their 
method uses deep metric learning, which teaches the model to learn the similarity between 

28 

 
 
 
Leveraging AI tools for a strengthened biosecurity framework 

plasmid sequences and the information from the laboratories. They also improved the use of 
CNNs by employing circular shift augmentation. This technique helps the model extract 
DNA  embeddings  while  learning  the  embeddings  of  known  laboratories  from  the Addgene 
database. This training methodology  improves the  accuracy  of the model. The  embeddings 
can  be  used  as  features  in  other  ML  models  and  perform  downstream  tasks  like  clustering 
DNA  sequences  and  laboratories. The  authors  also  introduce  the  use  of  triplet  networks  to 
embed  the  DNA  sequences  from  laboratories  and  DNA  query  sequence.  The  use  of  triplet 
networks allows multiple comparisons beyond line ranking the possible laboratories-of-origin, 
opening the door for multiple clustering techniques and improving interpretability of the model. 
Increased robustness to sequence perturbations was achieved compared to previous attempts 
(Nielsen  &  Voigt,  2018),  allowing  as  far  as  nearly  1000  variations  in  the  sequence  while 
maintaining an acceptable accuracy in the top 10 prediction. Moreover, this method offers an 
efficient  approach  that  avoids  retraining  the  entire  model  when  adding  new  laboratories. 
Instead,  it  directly  analyses  the  distance  between  an  unknown  sequence  and  the  new 
laboratories embeddings (Soares, Camargo, Marques, & Crook, 2022). 

Spirgel  and  colleagues  (Spirgel,  Comolli,  &  Guido,  2023)  developed  a  methodology  that 
addressed attribution from the signatures left by the computational tools used to design 
the engineered modifications. The team developed a Random Forest (RF) classifier that 
distinguishes  between  the  design  tools  used  for  codon  optimisation.  This  methodology 
achieved a 97% accuracy in predicting the computational tools used, a piece of information 
useful  for the  attribution of GEA  and  groundwork  for  future  research  lines,  as  stated  by the 
authors of the study. 

Ta b le  5 AI-base d tools identi˛ ed for the attribution of ge netic e ngine e ring. 

Type of 
genetic 
material 
analysed 

Accuracy  

Target 

Availability  Source 

Name 

AI technique 

Deep learning to 
predict the 
laboratory-of-
origin of 
engineered DNA 
ML toolkit 
including 
deteRNNt model 
for laboratory-of-
origin attribution 

PlasmidHawk 
(integrating 
Plaster algorithm) 

Convolutional 
Neural Network 
(CNN)  

BPE for 
plasmids 
encode; 
Recurrent 
Neural Networks 
(RNNs)  
Alternative to 
deep learning - 
Basic clustering 
methods 

Addgene 
(plasmids) 

47% top1 
70% top10 

Laboratory-of-
origin 

For 
research 

Addgene 
(plasmids) 

70% top1 
85% top10 

Addgene 
(plasmids) 

76% top 1 
85% top 
10 

81.9% top 
1 
94.9% top 
10   

75.8% top 
1  
90.4% top 
10  

Laboratory-of-
origin; 
Nation-of-origin; 
Ancestor 
laboratory-of- 
origin 

For 
research 

Laboratory-of-
origin 

For 
research 

Laboratory-of-
origin 

For 
research 

Laboratory-of-
origin 

For 
research 

Results from the 
GEA challenge 

CNN and naïve 
Bayes classifier 

Addgene 
(plasmids) 

Deep metric 
learning and 
CNN with 
circular shift 
augmentation 

Addgene 
(plasmids) 

Deep metric 
learning to 
improve 
laboratory-of-
origin prediction 
ML for Genome 
Engineering 
Design Tool 
Attribution 

Source: author’s elaboration based on literature review findings. 

29 

Random Forest 
classifier 

Information 
not 
available 

97% top 1 

In-silico tools 
used to design 
codon 
optimization 

For 
research 

Nielsen 
and 
Voigt, 
2018 

Alley et 
al., 
2021 

Wang 
et al., 
2021 

Crook 
et al., 
2022 

Soares 
et al., 
2022 

Spirgel 
et al., 
2023 

 
 
 
Leveraging AI tools for a strengthened biosecurity framework 

Despite advances in the field, there are some considerations and limitations that need to 
be  considered  when  analysing  these  AI-based  techniques  to  enhance  GEA.  First, 
attribution techniques are not intended for the detection of genetic engineering and therefore 
GEA is a process that should follow the initial process of detecting engineering (Lewis, et al., 
2020).  Second,  these  approaches  do  not  provide  authenticity  guarantees,  as  laboratories 
could  vary  their  preferred  methods  or  third  parties  could  mimic  methodological  signatures, 
difficulting  the  origin  identification.  While  useful  for  initial  investigations,  genetic  analysis 
through Computational and AI-based tools should be seen as a supplement to GEA, always 
accompanied  by  non-technical  indicators  like  context,  intelligence  from  human  sources  or 
surveillance data (Nielsen & Voigt, 2018) (Lewis, et al., 2020) (Alley, 2021) . Third, most models 
are  trained  using  Addgene  which,  despite  its  large  representation  of  synthetic  genetic 
sequences, does not represent the real world, especially when considering GEA for intended 
misuse where actors will look for ways of challenging attribution indicators (Lewis, et al., 2020) 
(Alley, 2021). 

2.4.3.  Considerations from the experts 

The findings in previous sections were complemented through the consultation activities with 
key experts. Experts’ perspectives have contributed to a better overview of the state-of-the-art 
on genetic engineering detection and attribution. Box 5 presents the main learnings from the 
interviews conducted with several experts belonging to Academia, Industry, Government, and 
International Agencies.  

Box  5  Considerations  from  the  experts:  AI-enabled  tools  to  improve  the  detection  and  attribution  of  genetic 
engineering 

Highlights resulting from Experts’ consultation 

Several  industry,  academic  and  policy  experts  working  in  this  field  were  consulted  to 
gather which are the main opportunities and challenges for incorporating AI into genetic 
engineering  detection  (GED)  and  attribution  (GEA),  and  therefore,  enhance  the 
biosecurity framework. 

Challenges in the use of AI for enhancing GED and GEA 

One major challenge in the use of AI for GED and GEA lies in the explainability of AI 
systems,  particularly  when  attempting  to  prompt  actionable  responses  from 
authorities and governments. An academic expert emphasized that the necessary level 
of  confidence  in AI-driven  findings  varies  depending  on  the  intended  application.  For 
instance, if the goal is ongoing surveillance of an individual or entity, moderate confidence 
may be sufficient, whereas attributing a significant action to a nation-state may require a 
much higher confidence level before making a public statement.  

Another complication arises from the gap between AI models and their real-world 
use cases; as several academic experts noted, tools often depend on the presence of 
specific  data  in  training  sets,  making  it  difficult  to  anticipate  performance  in  practical 
applications without real-world testing. Several experts noted that AI-based tools for GED 
or GEA are strengthened when they are integrated with other types of evidence, such as 
location  data,  video  footage,  or  physical  traces  from  a  scene,  aligning  with  realistic 
investigative needs.  

An  academic  and  an  international  organization  expert  highlighted  that  existing  GEA 
methodologies,  largely  based  on  the  Addgene  dataset,  lack  relevance  to  real-world 
applications  like  identifying  bioweapons.  Current  attribution  datasets  involve  plasmids 
that bear little resemblance to actual bioweapons, and those synthesizing the plasmids 
in  these  test sets  are  not typically  seeking to  evade  detection. As  an  academic  noted, 
these factors create a gap between GEA’s current capabilities and its potential to identify 
those  responsible  for  creating  bioweapons.  Should  malicious  actors  aim  to  evade 
attribution,  they  might  mimic  other  engineering  styles  or  capitalize  on  discrepancies 

30 

 
 
 
Leveraging AI tools for a strengthened biosecurity framework 

between  training  data  and  bioweapon  characteristics,  complicating  attribution  efforts 
considerably.  Thus,  challenges  persist  due  to  the  unrealistic  nature  of  current 
datasets and the evolving tactics that could be employed by adversaries. 

An academic expert working in industry emphasized that GEA is a rapidly evolving field 
which  poses  a  formidable  challenge;  the  advancements  seen  today  will  likely  differ 
significantly within five to ten years as more tool-assisted engineering practices replace 
manual  laboratory  work.  Attribution  is  an  area  where  fundamental  research  from 
academia is essential, yet current research often serves only as a preliminary step toward 
actionable solutions.  

Industry perspectives also emphasize that the absence of commercial incentives and 
limited  public  funding  in  biosecurity  means  that  many  AI  and  machine  learning 
professionals are drawn to more profitable sectors.  

Opportunities in the use of AI for enhancing GED and GEA 

Academic and policy experts noted that GED and GEA tools have a strong deterrent 
effect.  The  possibility  that  AI-based  detection  and  attribution  tools  could  expose 
malicious actors, trace their actions, or help mitigate impacts swiftly may be sufficient to 
deter harmful intent. 

Some experts reflected in a couple of key outcomes from the GEA Challenge, proving 
that  natural  language  processing  (NLP)  techniques  can  be  valuable  for  genomic 
analysis and that AI experts with well framed problems and questions may have a crucial 
role in developing solutions to enhance biosecurity, despite their lack of knowledge in the 
biosecurity field. 

Another expert recalled some initiatives on developing guardrails for biodesign tools 
that incorporate metadata tags, potentially cryptographically signed, to document 
design  origins,  user  intentions,  and  optimization  steps.  This  could  allow  DNA 
providers  and  collaborators  to  trace  biodesigns  and  interpret  their  intended  functions, 
enhancing both security and transparency. Another expert further proposed the "digital 
barcoding" of synthetic DNA, embedding unique identifiers in sequences to trace 
their origin, which could aid forensic investigations and compliance. This concept may 
not require intensive AI-specific technology but could be accelerated by AI to handle 
larger volumes of data. 

Latest developments and next steps 

Industry experts further noted that GED and GEA serve as supplementary intelligence 
sources within a broader biosurveillance ecosystem, akin to the chemical weapons 
surveillance  landscape.  Expanded  biosurveillance  programs,  such  as  wastewater 
monitoring, offer granular environmental data that could support GED and GEA’s practical 
application,  yet  scaling  such  initiatives  would  require  substantial  investment. 
Despite their potential, such programs remain limited, hindering the broader integration 
of GED and GEA as mainstream biosurveillance tools.  

Several  experts  also  noted  that  in the  near  future,  GEA  might raise  some  emerging 
commercial  interest,  which  would  accelerate  its  development,  as  it  could  potentially 
offer  solutions  to  Intellectual  Property  concerns  and  the  potential  risks  related  to 
intellectual property theft and patent violations.  

From an industry perspective, having diverse, independent solutions is essential to 
GED  and  GEA  long-term  success,  as  consensus-driven  approaches  from  unbiased 
sources add credibility and enhance international acceptance. 

Consequently,  though  GED  and  GEA  are  a  promising  area  for  AI  application,  some 
technical, practical, and economic obstacles complicate its advancement. 

31 

 
 
 
 
Leveraging AI tools for a strengthened biosecurity framework 

2.5.  Outlook and potential future applications of AI 

These are the main takeaways on potential future applications of AI in biosecurity. 

•  Beyond its use for synthetic nucleic acid orders screening, GED and GEA, AI has 
potential in other areas to safeguard biosecurity such as biosurveillance and early 
warning systems, threat detection, response strategies, and establishing mitigation 
measures.  

•  Large  Language  Models  and  Generative  AI  are  considered  by  many  as 

underexplored tools that could help advance biosecurity. 

AI can enhance the screening of synthetic nucleic acid orders and can expedite the detection 
and  attribution  of  genetic  engineering.  While  these  advances  are  extremely  relevant,  the 
literature review revealed other areas that need to be considered when discussing the 
role of AI in biosecurity. 

Drawing on insights from the study of De Haro (2024) on the use of Artificial Intelligence in 
synthetic biology, the outcomes of the meeting “Exploring possible impact of AI on Biosecurity 
and International Cooperation in the BWC” (InterAcademy Partnership, 2024) and the report 
on “the convergence of AI and the life sciences” (Carter et al., 2023a), Table 6 Error! Reference 
source not found. provides and overview of AI-driven innovations applied to biosecurity.  

The table presents the potential contribution of AI systems and tools across three main steps 
in biosecurity: (1) AI for biosurveillance, data integration, and early warning systems; (2) AI for 
biothreat  detection  and  monitoring;  (3)  AI  for  biothreat  response  and  mitigation.  Practical 
examples of existing tools or use cases are highlighted when available. 

This  overview  of  the  potential  application  of AI  innovations  in  biosecurity,  showing  how AI 
supports efforts to safeguard public health and mitigate biological risks. 

Ta b le  6 Opportunitie s of AI to synthetic biology and biose curity 

AI for biosurveillance, data integration and early warning systems 

Area of application   Description of AI contribution 

Data analysis 

AI has been a significant change in the analysis of genomic data, accelerating the pace of 
discovery in synthetic biology. The volume of data produced by modern genomic technologies 
such as Next-Generation Sequencing is immense. AI algorithms, particularly machine learning 
and deep learning models, can learn from and make predictions on these rapidly, making the 
analysis possible on the scale of hours todays. 
Tool example: ART (Automated Recommendation Tool). ART leverages machine learning and 
probabilistic modelling techniques to systematically guide synthetic biology without the need 
for a full mechanistic understanding of the biological system. (Radivojević, Costello, Workman, 
& Martin, 2020) 

Biorisk 
management  

AI can predict the safety and security implications of certain research activities. For instance, 
it could help predict the likelihood of an engineered organism’s accidental release or identify 
research activities that could be misused. 

Monitoring of 
biotechnology 
activities 

AI  can  be  used  to  monitor  biotechnology  activities  and  to  verify  and  validate  information, 
ensuring safety and beneficial outcomes from biotechnology and pharmaceutical research and 
development. 
AI-based tools can facilitate the identification of patterns of unapproved acquisitions, new life-
science discoveries, and interesting and potentially concerning publications for further review. 

Biosurveillance 
and detection 

AI  can  support  early  detection  of  biological  threats  and  outbreaks  combined  with 
environmental or human surveillance systems by the analysis of patters and deviations from 
normality. An example of this is the use of AI to enhance metagenomic analysis for genomic 

32 

 
 
 
Leveraging AI tools for a strengthened biosecurity framework 

AI for biosurveillance, data integration and early warning systems 

Area of application   Description of AI contribution 

detection in wastewater surveillance. AI can also be used to analyse the raw data, flagging 
concerning inputs for human review. 

Modelling patterns 
of infectious 
diseases and 
Outbreak 
response 

AI  tools  can  be  used  for  monitoring  and  tracking  patterns  of  infectious  disease  outbreaks, 
predicting and preventing the spread of disease, and processing and using the information to 
reduce  disease  impact,  evaluate  risks  to  public  health,  and  inform  policy  decisions  and 
containment measures.  
AI  tools  can  test  response  capabilities,  robustness of  oversight  mechanisms,  and safety of 
biotechnology products and services. Together with other new technologies, AI can contribute 
to  control  and  securing  supply  chains  which  is  of  significant  relevance  for  pandemic 
preparedness and response. 

Reducing 
misinformation 
and disinformation 

AI tools might help in the detection and recognition of misinformation and disinformation, as 
well  as  in  the  summarisation  and  outreach  of  confidence  building  measures  created  at 
governmental level. 

AI for biothreat detection and monitoring 

Area of application   Description of AI contribution 

Synthetic nucleic 
acid screening  

AI can be used for nucleic acid synthesis screening to help prevent the unauthorized creation 
of harmful organisms. 
Tool example: Tools reviewed in Section 2.1 of this report. 

GED and GEA 

AI can enhance the process of genetic engineering detection and attribution. 
Tool example: Tools reviewed in Sections 2.4.1 and 2.4.2 of this report. 

Protein structure 
prediction 

Protein  structure  prediction  is  one  of  the  key  applications  of  AI  in  synthetic  biology. 
Understanding how a protein’s function is determined by its 3Dstructure is one of the central 
challenges in biology. This is because the number of configurations a protein can fold into is 
astronomically  large,  making  it  practically  impossible  to  predict  a  protein’s  structure  based 
solely  on  its  sequence  of  amino  acids  using  conventional  methods. AI  can  help  accurately 
predict protein folding based on sequences 
Tool example: BDTs like RoseTTAFold, AlphaFold-2, ProteinMPNN, ESMFold (Carter et al., 
2023a) 

Library screening 

AI can help screen libraries of various biological or chemical entities, significantly improving 
the speed, cost-effectiveness, and outcomes of library screening. 

Forensic 
investigations 

AI and especially ML can contribute to genetic engineering detection and attribution in forensic 
investigations, as described in the previous sections of the report. 
AI  tools  can  monitor  attempts  to  create  a  new  pathogen,  or  to  build  a  known  high-risk 
pathogen, allowing controls to be put in place for biotechnology oversight.  

AI for biothreat response and mitigation 

Area of application   Description of AI contribution 

Vaccine and 
medicine 
development  

AI has been pivotal in accelerating various stages of vaccine development, including antigen 
identification, vaccine design, production, and distribution. Moreover, AI can also speed up the 
process of medicine screening by analysing large databases of compounds and predict their 
likely effects, reducing the need for extensive laboratory testing 

33 

 
 
 
 
 
Leveraging AI tools for a strengthened biosecurity framework 

AI for biothreat response and mitigation 

Area of application   Description of AI contribution 

Tool example: SAM (Search Algorithm for Ligands). SAM is an AI program that shifts through 
trillions  of  molecules  to  determine  which  one  could  be  a  good  vaccine  adjuvant  candidate 
(Masige, 2019) (Lima, Cha, Cha, & Hwang, 2021). 

AI  can  be  used  to  predict  the  outcomes  of  genetic  modifications  using  specific  tools  (e.g. 
CRISPR). By analysing large datasets, AI can predict off-target effects and help optimize the 
design of guide RNAs, leading to more efficient and accurate gene editing. 
Tool example: Combination of AI and CRISPR/Cas9 (Maserat, 2022) 

AI can be used in designing and creating new genes from scratch to perform a speciﬁc function 
in various applications, e.g. medical treatments.  
Tool  example:  A  generative  model  that  bridges  systems  biology  and  molecular  design, 
conditioning a generative adversarial network with transcriptomic data (Méndez-Lucio, Baillif, 
Clevert, Rouquié, & Wichard, 2020) 

Designing  genetic  circuits,  which  are  sequences  of  DNA  that  enable  cells  to  perform  new 
functions,  is  a  complex  task. AI  can  help  to  design  these circuits and predict how  they will 
behave in living cells 
Tool  example:  machine  learning  and  neural  networks  for  understanding  the  relationship 
between motif choice, combination and position to tailor synthetic receptors for desired cell 
phenotypes (Daniels, et al., 2022). 

Gene editing 

De novo gene 
design 

Genetic circuit 
design  

Protein design 

AI can help to predict how changes in a protein’s sequence will affect its structure and function. 
This can speed up designing new proteins or modifying existing ones 
Tool example: RFDiffusion 

Automated 
laboratory 
experiments 

AI can guide the automation of laboratory experiments. This can greatly speed up the research 
process and make it more efficient. 
Tool example: Emerald Cloud Lab, Opentrons 

Risk assessment 
on biothreats 

AI can enhance a more robust and informative risk assessment on biothreats, aiding in real-
time those in charge of decision-making.  

Supply chain 
management 
(Outbreak response) 

AI controls supply chains relevant to pandemic preparedness and response. 

Timely diagnosis 
and patient 
management 

The detection of specific symptomatology, timely diagnosis of infection, assistance in patient 
triage,  assessment  of  the  illness  prognosis  and  patient's  response  to  treatment,  and  the 
monitoring of population’s adherence to public health recommendations. 

Source: Adapted from De Haro (2024),  the meeting Exploring possible impact of AI on Biosecurity and International Cooperation 
in the BWC (2024) and the report on the convergence of AI and the life sciences (2023a). 

3. Discussion & Conclusion 

AI  offers  significant  potential  to  strengthen  the  EU  biosecurity  framework,  ranging  from 
improved  biosurveillance  and  early  warning  systems  to  enhancing  biothreat  response  and 
mitigation. However, these benefits require coordinated action at multiple levels. Building on 
existing  EU  and  international  biosecurity  measures,  a  series  of  targeted  actions  can  help 
translate  AI’s  capabilities  into  concrete  safeguards.  The  following  section  outlines  key 
measures  the  EU  can  adopt  to  harness  AI  for  enhanced  biosecurity  while  ensuring 
ethical use, regulatory alignment, and cross-border cooperation. This chapter concludes 
by summarising the key conclusions and insights of the study and providing a comprehensive 
overview of their alignment with the original objectives. 

34 

 
 
 
 
Leveraging AI tools for a strengthened biosecurity framework 

3.1.  Recommendations  to  leverage  AI  for  an  enhanced  EU 

biosecurity framework 

The uncertainty surrounding the new developments in both AI and synthetic biology demands 
that all stakeholders take a proactive approach to mitigate risks and leverage the opportunities 
these two emerging fields present for public health (Carter et al., 2023a). Experts interviewed 
in  this  study  emphasized  the  urgency  of  the  moment.  As  the  intersection  of  AI,  synthetic 
biology, and biosecurity is relatively new, there is a unique opportunity to shape its future by 
integrating  safety  by  design  into  policies  and  tools.  However,  this  window  of  opportunity  is 
closing rapidly, prompting calls for swift international action.  

The following globally scoped recommendations can serve as a practical roadmap to guide the 
EU's  direction  in  implementing AI’s  potential  to  enhance  the  EU  biosecurity  framework,  as 
outlined in the previous section. 

Promote  multidisciplinary  and  cross-border  collaboration  in  the  fields  of  AI  and 
biosecurity  

To  maximise  the  application  of  AI  in  biosecurity,  experts  emphasise  the  need  for  a 
multidisciplinary approach involving microbiology, synthetic biology, computational sciences, 
public  health,  and  bioethics  professionals  (Carter  et  al.,  2023a)  (InterAcademy  Partnership, 
2024)  (Undheim,  2024).  Sustainable  growth  of  computational  technologies  applied  to  life 
sciences like Biodesign tools and LLM requires collaboration among multidisciplinary teams to 
ensure  a  good  alignment  between  the  technical,  ethical,  and  security  implications  of  these 
advancements  (Sandbrink,  2023). The  EU  can  foster  dialogue  between  industry,  academia 
and  policymakers  by  supporting  scientific  associations.  Such  multidisciplinary  groups  are 
essential to ensure diverse perspectives when discussing the implications of new technologies’ 
development (Urbina, Lentzos, Invernizzi, & Ekins, 2022). Proposed measures include: 

•  Participate in Global AI-Biosecurity Initiatives. Take a leading role in international 
discussions  on AI  and  biosecurity,  ensuring that EU  actions  align  with  and  influence 
global governance frameworks. 

•  Reinforce  EU  multidisciplinary  bodies  to  advise  governments  on AI  applied  to 
the  life  sciences  and  biosecurity.  Proposed  initiatives  include  a  BWC  scientific 
advisory body to engage with the latest developments and identify potential areas of 
concern or interest for the BWC (InterAcademy Partnership, 2024) and an international 
“AI-Bio  Forum”  for  sharing  best  practices  and  recent  developments  in  AI  and 
biosecurity.  

•  Promote EU programmes and discussion forums to unite key experts to shape 
policies  and  guidelines  for  an  enhanced  and  secure  interplay  between  AI, 
synthetic  biology,  and  biosecurity.  The  European  Biosecurity  Regulators  Forum 
(EBRF), comprising Belgium, Denmark, France, Germany, The Netherlands, Sweden, 
Switzerland, and the UK, serves as a  solid foundation for establishing a European 
network of biosecurity contact points, with could expand to other Member States 
with proper EU support.  

Support Research, Innovation and Capacity building in AI for Biosecurity 

Both  the  literature  reviewed,  and  the  experts  interviewed  highlighted  the  importance  of  the 
allocation of resources for research and innovation applied to AI in the context of enhancing 
the biosecurity framework. Specific measures proposed include: 

•  Strengthen  EU’s  research  agenda  by 

for  pandemic 
preparedness  and  response  through  programmes  like  Horizon  Europe  and 
EU4Health. Suggested focus areas for these programmes include enhanced screening 

funding  AI 

tools 

35 

 
 
 
Leveraging AI tools for a strengthened biosecurity framework 

of  synthetic  nucleic  acid  orders,  early  outbreak  detection,  accelerated  vaccine 
development, and improved AI control mechanisms to safeguard security.  

• 

Increase  government  funding  to  develop  robust  biosecurity  infrastructure, 
ensuring  the  capacity  to  identify  the  origin,  danger  level,  and  spread  of  biological 
threats, while addressing the lack of private sector incentives due to limited profitability. 

•  Support  the  creation  of  data-sharing  protocols  for  AI  model  development  and 
testing. Controlled access to the data required for training, auditing and evaluating AI 
models  would  enhance  responsible  use  and  minimisation  of  risks  while  enabling 
innovation and continuous progress (Sandbrink, 2023) (Urbina, Lentzos, Invernizzi, & 
Ekins, 2022).  

•  Enforce an assessment by scientific publishers of the data and information made 
publicly  available  in  scientific  publications  for  adequacy  and  potential  risks 
assessment  (National  Security  Commission  on  Emerging  Biotechnology  (NSCEB), 
2024). 

Moreover,  it  was  highlighted  the  importance  of  enhancing  understanding  of  biosecurity 
implications  among  life  sciences  and  computer  science  professionals  (Urbina,  Lentzos, 
Invernizzi,  &  Ekins,  2022).  Interdisciplinary  training  that  integrates  ethical,  social, 
computing, and life sciences can improve awareness of both the benefits and potential 
misuse  of  new  advancements,  fostering  greater  understanding  and  trust.  While 
education policies are mainly handled by Member States, the EU could support and facilitate 
access  to  biosecurity  education  initiatives  by  developing  a  code  of  conduct  for  biosecurity 
similar to the ones used for biosafety and  promoting the common EU education frameworks 
and mobility programmes that expand cross-border training opportunities for students across 
Member States. 

Develope  Standards  and  Guidelines  for  the  use  of  AI  and  biotechnology  in  Life 
Sciences 

In  order  to  prevent  misuse,  it  is  essential  to  establish  robust  standards  and  guidelines  for 
ethical  and  practical  challenges  posed  by  emerging  technologies.  The  literature  and 
interviewed experts proposed several roles that the EU could take. 

•  Collaborate with Member States to adopt a biotechnology and AI code of conduct, 
similar  to  The  Hague  Ethical  Guidelines  for  chemistry  (Organisation  for  the 
Prohibition  of  Chemical  Weapons  (OPCW),  2015),  to  promote  responsible  use  and 
reduce risks of misuse (Urbina, Lentzos, Invernizzi, & Ekins, 2022).  

•  Establish  international  agreements  on  regulated  pathogen  lists  and  develop 
guidelines and protocols for AI-enhanced screening synthesis orders tools for 
other  biological  threats,  such  peptide  toxins,  that  can  be  synthesized  in  low-
biosecurity environments (Lee, Cowan, & & Tankard, 2022).  

•  Promote,  in  collaboration  with  other  international  regulators,  the  development 
and implementation of standards specific to the use of AI models in life sciences. 
The  development  of  standards  for  the  use AI  models  is  a  global  matter,  extending 
beyond their application in life sciences. Initiatives like the European AI Act, OECD AI 
principles, and ISO standards aim to enhance transparency, data quality and system 
reliability and are the starting point of any further initiatives. 
Establish oversight and internationally recognized standards to tackle visibility 
and traceability issues arising from decentralised nucleic acid synthesis using 
benchtop  devices  (Carter  &  Yassif,  2023b)  (Kane  &  Parker,  2024).  Existing 
frameworks,  such  as  ISO  35001:2019  on  “biorisk  management  for  laboratories”  and 
ISO 20688-2:2024 on “Biotechnology - Nucleic acid synthesis - requirements for the 

36 

 
 
 
Leveraging AI tools for a strengthened biosecurity framework 

production and quality control of synthesized gene fragments, genes, and genomes,” 
serve as a basis for developing targeted standards in the evolving biotechnology sector. 

Establish rules and legislation for AI in the life sciences to improve EU’s Biosecurity  

Legislative frameworks are being put in place worldwide for the secure development and use 
of AI technologies. Two examples are the Artificial Intelligence (AI) Act in the EU and previous 
US Executive Orders on AI, including EO 14110 on Safe, Secure and Trustworthy Development 
and Use of Artificial Intelligence. While the 2023 US Executive Order on AI marked an initial 
move towards enhanced biosecurity, these regulations are generally unspecific concerning the 
application of AI to the life sciences and its implications for biosecurity (Sandbrink, 2023). 

Moreover, as AI governance becomes more important, there is growing concern that excessive 
regulation  could  put  on  hold  beneficial  advancements  and  innovation,  potentially  making 
difficult  international  collaboration  and  limiting  access  to  AI  tools  for  positive  applications 
(InterAcademy  Partnership,  2024).  The  EU  has  a  heterogeneous  biosecurity  landscape, 
shaped by varying national understandings, priorities, capabilities, and resources. This means 
the EU can play a supporting and guiding role to ensure consistent and effective action across 
all Member States. Under this context, proposed actions include: 

•  Adopt  an  adaptive  EU  governance  approach,  where  AI  developments  and 
biological risks can be monitored and polices adapted accordingly (Carter et al., 
2023a). 

•  Establish  a  structure  for  reporting  to  competent  authorities  about  accidents  or 
awareness on potential misuse of technology (Urbina, Lentzos, Invernizzi, & Ekins, 
2022). Given biosecurity’s global nature, the EU could play a key role in developing 
a  common  reporting  framework  to  facilitate  information  exchange  among 
Member States and encourage broader international adoption.  

The four recommended areas detailed above establish a basis for advanced AI-driven 
monitoring and surveillance of biothreats as well as enforce compliance of biosecurity 
measures. For example, to effectively deploy AI technologies for biosecurity monitoring and 
surveillance,  there  must  be  parallel  investments  in  collaborative  networks  and  knowledge-
sharing  mechanisms  across  disciplines  and  borders.  A  targeted  research  agenda  on  AI  for 
biosecurity can drive innovation in genetic sequence screening and real-time biological threat 
detection. Moreover, developing common standards and guidelines will ensure that AI-based 
detection  systems  operate  reliably  and  consistently  across  Member  States.  Moreover,  to 
implement  advanced  compliance  measures,  such  as  AI-supported  supply  chain  tracking  or 
automated  regulatory  audits,  requires  a  regulatory  framework  that  reflects  the  diverse 
resources  and  capacities  across  EU  Member  States.  This  highlights  the  need  for  tailored 
legislative approaches that account for national differences while maintaining alignment with 
broader EU regulations like the AI Act. Education and training programmes on AI ethics and 
biosecurity  will  also  be  essential  to  support  regulatory  bodies  and  research  institutions  in 
adopting AI tools for compliance monitoring. 

Summarising, the EU can leverage AI to enhance biosecurity by promoting cross-border 
cooperation,  fostering  research  and 
innovation,  bridging  expertise  gaps,  and 
strengthening regulatory frameworks. These actions in turn could contribute to improving 
monitoring capabilities and enforcing compliance, which would help the EU proactively address 
emerging biosecurity challenges while maintaining ethical and legal safeguards. 

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Leveraging AI tools for a strengthened biosecurity framework 

3.2.  Final conclusions of the study 

Addressing cross-border health threats is a top priority for Europe, as outlined in the European 
Health  Union,  the  EU’s  legal  framework  aimed  at  enhancing  prevention,  preparedness, 
surveillance, risk assessment, early warning, and response. In this context, HERA is driving 
key  initiatives  to  promote  advanced  research  and  development  of  technologies  to  improve 
preparedness and response to novel pandemics. The integration of AI in biosecurity offers 
a  unique  opportunity to strengthen  the  EU's defences  against  both natural  and  man-
made biological risks.  

This study has examined the literature and interviewed experts in the field to address three 
main objectives: 

1.  Assess AI’s potential to enhance screening of synthetic nucleic acid orders;  
2.  Identify and evaluate AI-based tools and methodologies that enhance the detection and 

attribution of genetic engineering activities; 

3.  Evaluate the opportunities and risks posed by AI technologies toward biosecurity.  

The main findings from the study are described below. 

First, the study team identified the seven main tools used for screening synthetic nucleic 
acid  orders.  The  underlying  methodologies  of  most  of  these  tools  are  not  always  publicly 
available. However, the available information pointed to four of them being enhanced by the 
use of AI techniques. The role of AI in these tools is mainly the management and refinement 
of  reference  datasets,  optimisation  of  sequence  comparison  algorithms  through  Machine 
Learning, or the prediction of structure and functionality of the biological entities encoded by 
the  sequences  analysed.  It  is  expected  for  AI  to  further  contribute  to  the  development  of 
screening  methodologies,  gaining  independence  from  traditional  alignment-based  methods 
that rely on limited repositories of well-known pathogens.  

Second, the study identified two AI-based tools for detecting genetic engineering and 
six for genetic engineering attribution. The most employed AI technique in these tools is 
Deep learning, with some tools employing traditional clustering methods and Random Forest 
techniques.  The  literature  points  to  an  emerging  field  where,  developing  further  these 
capabilities  will  be  crucial  for  both  preventing  and  early  detecting  bioweapon  attacks  and 
attributing responsibility in the event of a biosecurity breach. However, in comparison to the 
screening of synthetic nucleic acid orders, this field is still under development with most of the 
solutions developed for research purposes and not yet suited for real-world wide application.  

Third, the study team evaluated the key benefits and risks of integrating AI-enabled tools into 
EU biosecurity measures. AI offers key benefits for biosecurity with an impact in the areas 
of biosurveillance, early warning systems, outbreak monitoring and biothreat response 
and mitigation. Large Language Models (LLMs) can automate biosecurity tasks, improving 
efficiency in tasks like data gathering or report reviewing and summarising. AI can also improve 
the  monitoring  and  surveillance  of  outbreaks  through  enhanced  pattern  recognition  in 
surveillance data. AI-based Biodesign Tools (BDTs) can expedite responses to biothreats by 
improving  the  design  of  medical  countermeasures;  Tools  like  AlphaFold2  facilitate  protein 
structure prediction, advancing drug and vaccine development. While AI has the potential to 
bolster  biosecurity,  many  applications  are  still  evolving  and  face  challenges,  particularly 
concerning information quality and resource utilisation. The integration of AI also brings risks 
and dual-use concerns. The two primary risks identified are the increased public access 
to  and  understanding of  technical  knowledge  via  LLM,  which  could  empower malicious 
actors,  and the  heightened  ability  to  design  harmful  biological  agents  using  BDTs  that may 
evade standard screening and detection protocols. As most of the risks are closely linked to 
the opportunities AI offers to biosecurity, it is crucial to implement policy actions to carefully 
manage the current and emerging risks stemming from the advancement of rapidly evolving 
fields like synthetic biology and AI. These policies are essential to safeguard biosecurity while 
ensuring that innovation and growth in synthetic biology and AI continue to flourish. 

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Leveraging AI tools for a strengthened biosecurity framework 

Based on the literature findings and the targeted interviews with experts in the field, this report 
drafts  several  recommendations  on  the  role  that  the  EU  can  take  in  supporting  and 
guiding Member States in the implementation of a biosecurity framework enhanced by 
AI. The main areas of recommendations are: 

1.  Promote multidisciplinary and cross-border collaboration in the fields of AI and biosecurity.  
2.  Support Research, Innovation and Capacity building in AI for Biosecurity. 
3.  Develope Standards and Guidelines for the use of AI and biotechnology in Life Sciences. 
4.  Establish rules and legislation for AI in the life sciences to improve EU’s Biosecurity. 

The  EU  can  leverage  AI  to  enhance  biosecurity  by  fostering  cross-border  collaboration, 
promoting research and innovation, and strengthening regulatory frameworks. These initiatives 
can enhance the monitoring abilities, ensure compliance, and assist the EU  in tackling new 
biosecurity threats while upholding ethical and legal standards. By adopting these actions, the 
EU could increase their support to Member States, enabling them to establish or reinforce a 
proactive and robust biosecurity framework that responds to existing risks and prepares for 
future challenges.  

An  important  learning  is  the  importance  of  accompanying  technical  developments  by 
political  and  legislative  actions  to  support  the  implementation  and  adoption  of  these 
screening practices globally. An example is the US Executive Order on the Safe, Secure, 
and  Trustworthy  Development  and  Use  of  Artificial  Intelligence  (The  White  House,  2023), 
which mandated the screening of synthetic nucleic acid orders for those projects receiving US 
national  funds.  Although  the  Executive Order  only  covered  activities funded  by  US  national 
funds, it is a good example of how regulations can reinforce the adoption of existing guidelines 
and  best  practices  to  reinforce  biosecurity  and  make  it  a  subject  of  current  interest  in 
international discussions and among a variety of different stakeholders.   

By  embracing  innovation  and  fostering  collaboration,  the  EU  can  lead  the  way  in 
leveraging  AI  to  strengthen  biosecurity,  protect  public  health,  and  enhance  global 
resilience  against  emerging  biological  threats.  The  integration  of  AI  into  the  biosecurity 
framework presents a transformative opportunity to enhance the EU’s capacity to anticipate, 
detect,  and  respond  to  emerging  biological  threats.  By  leveraging  AI’s  potential  for  data 
analysis, early warning systems, and risk assessment, the EU can build a more proactive and 
resilient  biosecurity  infrastructure.  However,  this  must  be  accompanied  by  robust  ethical 
guidelines,  regulatory  frameworks,  and  international  and  multidisciplinary  cooperation  to 
ensure that AI is used responsibly and effectively. As biosecurity challenges continue to evolve, 
the  EU’s  commitment  to  innovation,  collaboration,  and  adaptive  governance  will  be  key  to 
safeguarding public health and reinforcing global security. The future of biosecurity depends 
on the ability to balance technological advancement with careful oversight, ensuring that AI 
remains a powerful tool in the fight against biological risks. 

39 

 
 
 
 
Leveraging AI tools for a strengthened biosecurity framework 

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Annex: Methodology 

This section outlines the methodology followed in this report for the literature review on the use 
of AI-based  tools  for  enhancing  the  EU  biosecurity  framework.  Moreover,  this  section  also 
describes the methodology used to gather additional inputs on the topic from key stakeholders, 
covering from the selection of stakeholders up to the analysis of the inputs gathered. 

Scoping literature review 

The focus of the scoping literature review was to map and analyse literature reporting on AI’s 
potential  to  enhance  DNA  synthesis  screening  in  Europe  to  prevent  access  to  bioweapon 
components  and  the  creation  of  biological  weapons,  the  role  of  AI  in  advancing  genetic 
engineering detection and attribution, and how AI-enabled tools can contribute to the overall 
EU biosecurity framework.  

A literature search was conducted following the PRISMA guidelines, which were designed to 
help reviewers transparently report why the review was done, what the authors did, and what 
they found (Page et al., 2021). The literature search was performed on the academic database 
PubMed. Other databases with a specific focus on AI research were also considered (i.e., IEEE 
and ACM Digital Library) but were discarded as they only contributed with publications already 
identified  in the  PubMed  database.  Box  6  presents  the  search  string used  for  this  literature 
review. The search strings were developed based on the key research questions of the study 
(see Box 6) and refined through discussions among the study team. The strings were tested 
to ensure they produced valid and reliable outcomes.  

The  study  team  established  the  inclusion  criteria:  studies  published  in  the  last  10  years 
(January  2015- August  2024)  that  were  open  source,  with  full  text  available  and  written  in 
English.  The  exclusion  criteria  were  also  defined,  with  studies  excluded  when  1)  not  peer-
reviewed, 2) outside the research scope, or 3) irrelevant to the research questions. 

Box 6 Research Questions and Query string used   

Research Question #1 

How can AI-enabled screening techniques enhance the detection of cases of suspicious orders 
of synthetic DNA, possibly aimed at the creation of biological weapons?   

Research Question #2 

How can ML algorithms be used to improve genetic engineering detection and attribution?  

Research Question #3 

How  can  AI  tools  contribute  to  the  improvement  of  the  European  and  global  biosecurity 
framework?  

Query in PubMed: 

("artificial inte llige nce "[Title /Abstract] OR "AI"[Title /Abstract] OR "ML"[Title /Abstract] OR "Machine  
Le arning"[Title /Abstract] OR "De e p Le arning"[Title /Abstract] OR "LLM"[Title /Abstract] OR 
"computational biology"[Title /Abstract] OR "he alth te chnolog*"[Title /Abstract] OR "big 
data*"[Title /Abstract]) AND ("biological we apon*"[Title /Abstract] OR "biowe apon*"[Title /Abstract] OR 
"ge ne tic modif*"[Title /Abstract] OR "ge ne tic e ngine e r*"[Title /Abstract] OR "ge ne tic 
synthe s*"[Title /Abstract] OR "harmful ge ne tic*"[Title /Abstract] ) AND ("biose curity"[Title /Abstract] OR 
"se curity"[Title/Abstract] OR "he alth se curity"[Title /Abstract] OR "de te ction"[Title /Abstract] OR 
"attribution"[Title /Abstract] OR "monitoring"[Title /Abstract] OR "unauthorise d"[Title /Abstract] OR 
"scre e ning"[Title /Abstract] OR "orde r"[Title /Abstract])  

Hits : 72 

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Leveraging AI tools for a strengthened biosecurity framework 

Search results (n=72) were compiled into a single file for screening. Titles and abstracts were 
reviewed to determine if they addressed:  1) AI-based solutions for the screening of synthetic 
nucleic  acid  orders,  2) AI-based  solutions  for  advancing  genetic  engineering  detection  and 
attribution, and 3) the impact of AI-based tools on biosecurity. Publications not excluded during 
the title and abstract screening (n=11) underwent a rapid full-text preliminary review. Studies 
were included or excluded based on the exclusion criteria defined above. Nine studies were 
included in the review for data extraction.  

Additionally,  38  key  references  were  identified  through  a  manual  search  of  relevant 
publications and citations in included studies (snowballing approach).  

Data  from  each  47  included  studies  were  extracted  by  a  single  reviewer  and  verified  for 
accuracy  and  completeness  by  a  second  independent  reviewer. A  narrative  synthesis  was 
performed.  

The total number of records after each screening round was documented using the PRISMA 
flow  diagram  template  (see  Figure  1). The  literature  review  assisted  the  study  team  in 
identifying  authors  that  explored  the  contribution  of  AI-based  tools  to  the  EU  biosecurity 
framework.  

Figure  1 Prisma Flow Diagram summarising the re cords of the  lite rature  review. 

Identification of studies via databases and registers 

Identification of studies via other methods 

Records identified from 
PubMed (n = 72) 

No records were remove d 
before screening 

Records identified from citation 
searching, snowballing approach and 
grey literature (n = 52) 

Records excluded** 
(n = 61) 

Reports excluded (n=2) 

Reports assessed for eligibility 
(n = 38) 

Records screened (title 
and abstract) 
Tot (n=72) 

Reports assessed for 
eligibility 
(n = 11) 

Reports of included studies 
(n = 47) 

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i
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Source:  Page  MJ,  McKenzie  JE,  Bossuyt  PM,  Boutron  I,  Hoffmann  TC,  Mulrow  CD,  et  al.  The  PRISMA  2020  statement:  an 
updated  guideline  for  reporting  systematic  reviews.  BMJ  2021;372:n71.  doi:  10.1136/bmj.n71.  For  more  information,  visit: 
http://www.prisma-statement.org/     

Consultation activities and stakeholder mapping 

The consultation activities and stakeholder mapping were carried out for six months and aimed 
to achieve three primary objectives: 

• 

Identify key experts in AI, biosecurity and associated fields from Academia, Industry, 
and Government. 

•  Validate  and  bridge  research  gaps:  To  corroborate  and  fill  the  research  gaps 
identified  in  existing  literature  by  integrating  insights  from  experts  across  academia, 
public health laboratories, industry, and government. 

46 

 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Leveraging AI tools for a strengthened biosecurity framework 

•  Validate  AI  methodologies  and  simulation  methods:  To  assess  the  validity  and 
feasibility  of  AI  supported  methodologies  identified  in  the  literature  for  demand 
forecasting and supply chain monitoring as well as simulation methods for supply chain 
optimisation. 

A  comprehensive  stakeholder  mapping  exercise  was  conducted  at  the  outset  of  the  study, 
identifying key experts across the three stakeholder categories. This mapping was validated 
by HERA and informed the subsequent engagement activities, which included targeted emails 
to request participation in specific consultation activities. The consultation activities consisted 
on in-depth one-on-one interviews (60 minutes long).  

Prior to the consultations, three distinct interview guidelines were developed, each tailored to 
a specific stakeholder category (academia, industry, and government). These guidelines were 
reviewed  and  validated  by  HERA,  ensuring  comprehensive  coverage  of  key  topics  while 
allowing flexibility for individual expertise. 

Each guideline included the following sections: 

Introduction and consent for recording and minute-taking. 

• 
•  Background questions on the interviewee's profile and experience in the field. 
•  Questions regarding the use of AI methodologies and simulation methods for demand 
forecasting and supply chain monitoring, considering applicability to MCM supply chain. 
•  Questions  exploring  implementation  challenges  (with  a  focus  on  public  authorities’ 

perspective), research bottlenecks and priorities. 

•  Open-ended questions for additional insights, uncovered areas, or recommendations 

for further consultation. 

The consultation strategy envisioned a target number of participants for each category: 

•  Academia (N = 5) 
• 
Industry (N = 5) 
•  Government (N =5) 

Due  to  the  ongoing  stakeholder  mapping  exercise,  additional  stakeholders  beyond  the 
originally  selected  group  were  invited  to  participate  in  the  consultation  activities  following  a 
comprehensive review of their expertise. This expansion of the initially selected pool of experts 
enhanced the quality of input and feedback collected. Table 7 summarises the number of in-
depth interviews conducted, the number of participants and their geographical regions. 

Table 7 Consultation activities of the study. 

AI 

Academia 

Industry 

Government 

Number of interviews 

7 

6 

3 

Geographical areas represented 
(number of participants) 
EU (2); UK (1); Switzerland (1); US 
(3) 
US industry (5); International industry 
organisation (1) 

EU (2); International organisation (2) 

Interviews  followed  a  semi-structured  format,  allowing  for  adaptation  based  on  the 
interviewee's  specific  expertise.  Preliminary  materials,  including  interview  guidelines  and 
tables of AI methodologies and simulation methods identified in the literature, were shared with 
interviewees in advance. Following each interview, detailed minutes were shared for review 
and approval before incorporating the feedback into the study. 

The valuable insights gathered from the consultation activities are presented at the end of each 
relevant results section (Box 2-6). These findings collectively contributed to the final discussion 
in Section 3 of this report. 

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Leveraging AI tools for a strengthened biosecurity framework 

GETTING IN TOUCH WITH THE EU 

In person 

All over the European Union there are hundreds of Europe Direct information centres. You can find the 
address of the centre nearest you at: https://europa.eu/european-union/contact_en 

On the phone or by email 

Europe Direct is a service that answers your questions about the European Union. You can contact 
this service: 

– by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls), 

– at the following standard number: +32 22999696, or  

– by email via: https://europa.eu/european-union/contact_en 

FINDING INFORMATION ABOUT THE EU 

Online 

Information about the European Union in all the official languages of the EU is available on the Europa 
website at: https://europa.eu/european-union/index_en 

EU publications 

You can download or order free and priced EU publications from: https://op.europa.eu/en/publications. 
Multiple copies of free publications may be obtained by contacting Europe Direct or your local 
information centre (see https://europa.eu/european-union/contact_en). 

EU law and related documents 

For access to legal information from the EU, including all EU law since 1952 in all the official language 
versions, go to EUR-Lex at: http://eur-lex.europa.eu 

Open data from the EU 

The EU Open Data Portal (http://data.europa.eu/euodp/en) provides access to datasets from the EU. 
Data can be downloaded and reused for free, for both commercial and non-commercial purposes. 

48