Self-driving laboratories (SDLs) are rapidly transforming the scientific enterprise by integrating artificial intelligence (AI), robotics, and automated experimentation to accelerate discovery with greater speed, autonomy, and precision. These intelligent platforms enable researchers to efficiently explore vast experimental spaces, reducing human error and increasing reproducibility. However, they introduce new and complex safety and security risks that demand proactive attention.
Methodology:
Here we discuss SDLs and how their utility in biotechnology can be harvested to drive scientific change. We also assess the security risks associated with integrating AI into SDL operations in research environments and provide mitigation strategies to address them.
Results:
We present an assessment of the vulnerabilities related to AI-driven SDLs and provide targeted, actionable mitigation strategies. These strategies are designed to help researchers and institutions address emerging risks related to system autonomy, data governance, and automation in experimental workflows.
Conclusion:
We argue that safeguarding SDLs cannot fall to any single actor; instead, it requires coordinated action among researchers, institutions, and policymakers. By grounding SDL development in principles of security, ethics, and collaborative governance, the scientific community can ensure that these powerful platforms advance research in a responsible manner. This work lays a critical foundation for secure and ethical SDL integration, supporting innovation while protecting against misuse and unintended harm.
RappJT, BremerBJ, RomeroPA. Self-Driving laboratories to autonomously navigate the protein fitness landscape. Nat Chem Eng, 2024; 1(1):97–107; doi: 10.1038/s44286-023-00002-4
2.
BennettJA, AbolhasaniM. A self-driving lab for accelerated catalyst development. Nat Chem Eng, 2024; 1(3):206–207; doi: 10.1038/s44286-024-00043-3
3.
TomG, SchmidSP, BairdSG, et al.Self-driving laboratories for chemistry and materials science. Chem Rev, 2024; 124(16):9633–9732; doi: 10.1021/acs.chemrev.4c00055
4.
AbolhasaniM, KumachevaE. The rise of self-driving labs in chemical and materials sciences. Nat Synth, 2023; 2(6):483–492; doi: 10.1038/s44160-022-00231-0
5.
O’DonoghueO, ShtedritskiA, GingerJ, et al.BioPlanner: Automatic evaluation of LLMs on protocol planning in biology. ArXiv, 2023; doi: 10.48550/arXiv.2310.10632
6.
GaoS, FangA, HuangY, et al.Empowering biomedical discovery with AI agents. Cell, 2024; 187(22):6125–6151; doi: 10.1016/j.cell.2024.09.022
7.
JinQ, YangY, ChenQ, LuZ. GeneGPT: Augmenting large language models with domain tools for improved access to biomedical information. Bioinformatics, 2024; 40(2):btae075; doi: 10.1093/bioinformatics/btae075
8.
HickmanRJ, SimM, Pablo-GarcíaS, et al.Atlas: A brain for self-driving laboratories. Digit Discov, 2025; 4(4):1006–1029; doi: 10.1039/D4DD00115J
9.
SzymanskiNJ, RendyB, FeiY, et al.An autonomous laboratory for the accelerated synthesis of novel materials. Nature, 2023; 624(7990):86–91; doi: 10.1038/s41586-023-06734-w
10.
BranAM, CoxS, SchilterO, et al.Augmenting large language models with chemistry tools. Nat Mach Intell, 2024; 6(5):525–535; doi: 10.1038/s42256-024-00832-8
11.
DarvishK, SkretaM, ZhaoY, et al.ORGANA: A robotic assistant for automated chemistry experimentation and characterization. Matter, 2025; 8(2):101897; doi: 10.1016/j.matt.2024.10.015
12.
YoshikawaN, SkretaM, DarvishK, et al.Large language models for chemistry robotics. Auton Robot, 2023; 47(8):1057–1086; doi: 10.1007/s10514-023-10136-2
13.
BoikoDA, MacKnightR, KlineB, GomesG. Autonomous chemical research with large language models. Nature, 2023; 624(7992):570–578; doi: 10.1038/s41586-023-06792-0
14.
MacLeodBP, ParlaneFGL, BerlinguetteCP. How to build an effective self-driving laboratory. MRS Bull, 2023; 48(2):173–178; doi: 10.1557/s43577-023-00476-w
15.
ScheurerC, ReuterK. Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysis. Nat Catal, 2025; 8(1):13–19; doi: 10.1038/s41929-024-01275-5
16.
De HaroLP. Using embodied artificial intelligence agents to automate biorisk management tasks in high-containment laboratories. Appl Biosaf, 2025; doi: 10.1089/apb.2024.0068
17.
BurgerB, MaffettonePM, GusevVV, et al.A mobile robotic chemist. Nature, 2020; 583(7815):237–241; doi: 10.1038/s41586-020-2442-2
18.
LuntAM, FakhruldeenH, PizzutoG, et al.Modular, multi-robot integration of laboratories: An autonomous workflow for solid-state chemistry. Chem Sci, 2024; 15(7):2456–2463; doi: 10.1039/D3SC06206F
19.
ZhuQ, HuangY, ZhouD, et al.Automated synthesis of oxygen-producing catalysts from martian meteorites by a robotic AI chemist. Nat Synth, 2023; 3(3):319–328; doi: 10.1038/s44160-023-00424-1
20.
Strieth-KalthoffF, HaoH, RathoreV, et al.Delocalized, asynchronous, closed-loop discovery of organic laser emitters. Science, 2024; 384(6697):eadk9227; doi: 10.1126/science.adk9227
21.
Delgado-LiconaF, AbolhasaniM. Research acceleration in self-driving labs: Technological roadmap toward accelerated materials and molecular discovery. Adv Intell Syst, 2023; 5(4):2200331; doi: 10.1002/aisy.202200331
22.
SadeghiS, CantyRB, MukhinN, et al.Engineering a sustainable future: Harnessing automation, robotics, and artificial intelligence with self-driving laboratories. ACS Sustainable Chem Eng, 2024; 12(34):12695–12707; doi: 10.1021/acssuschemeng.4c02177
23.
BaiJ, MosbachS, TaylorCJ, et al.A dynamic knowledge graph approach to distributed self-driving laboratories. Nat Commun, 2024; 15(1):462; doi: 10.1038/s41467-023-44599-9
24.
ReedJC, DunawayN. Cyberbiosecurity implications for the laboratory of the future. Front Bioeng Biotechnol, 2019; 7:182; doi: 10.3389/fbioe.2019.00182
25.
SeifridM, PolliceR, Aguilar-GrandaA, et al.Autonomous chemical experiments: Challenges and perspectives on establishing a self-driving lab. Acc Chem Res, 2022; 55(17):2454–2466; doi: 10.1021/acs.accounts.2c00220
26.
MassonJ-F. Opportunities and challenges of self-driving laboratories for sensing. ACS Sens, 2025; 10(7):4765–4768; doi: 10.1021/acssensors.5c02277
27.
von SteinM, WangH, ElbaumS. Automated generation of transformations to mitigate sensor hardware migration in ADS. IEEE Robot Autom Lett, 2024; 9(7):6480–6487; doi: 10.1109/LRA.2024.3405810
28.
MartinHG, RadivojevicT, ZuckerJ, et al.Perspectives for self-driving labs in synthetic biology. Curr Opin Biotechnol, 2023; 79:102881; doi: 10.1016/j.copbio.2022.102881
29.
CarbonellP, RadivojevicT, García MartínH. Opportunities at the intersection of synthetic biology, machine learning, and automation. ACS Synth Biol, 2019; 8(7):1474–1477; doi: 10.1021/acssynbio.8b00540
30.
ChaoR, MishraS, SiT, ZhaoH. Engineering biological systems using automated Biofoundries. Metab Eng, 2017; 42:98–108; doi: 10.1016/j.ymben.2017.06.003
31.
HillsonN, CaddickM, CaiY, et al.Building a global alliance of Biofoundries. Nat Commun, 2019; 10(1):2040; doi: 10.1038/s41467-019-10079-2
32.
Jessop-FabreMM, SonnenscheinN. Improving reproducibility in synthetic biology. Front Bioeng Biotechnol, 2019; 7:18; doi: 10.3389/fbioe.2019.00018
Soleimani SasaniM. The importance of biosecurity in emerging biotechnologies and synthetic biology. Avicenna J Med Biotechnol, 2024; 16(4):223–232; doi: 10.18502/ajmb.v16i4.16738
40.
De HaroLP. Biosecurity risk assessment for the use of artificial intelligence in synthetic biology. Appl Biosaf, 2024; 29(2):96–107; doi: 10.1089/apb.2023.0031
LekadirK, FeragenA, FofanahAJ, et al.FUTURE-AI: International Consensus Guideline for Trustworthy and Deployable Artificial Intelligence in Healthcare. ArXiv, 2024; doi: 10.48550/arXiv.2309.12325
EU Artificial Intelligence Act. Up-to-date developments and analyses of the EU AI Act. Available from: https://artificialintelligenceact.eu/ [Last accessed: September27, 2025].
47.
RadanlievP. Privacy, ethics, transparency, and accountability in AI systems for wearable devices. Front Digit Health, 2025; 7:1431246; doi: 10.3389/fdgth.2025.1431246
48.
RadanlievP. AI Ethics: Integrating transparency, fairness, and privacy in AI development. Appl Artif Intell, 2025; 39(1):2463722; doi: 10.1080/08839514.2025.2463722
