PreprintCited by 1Digitalization, Law, and RegulationInformation and Cyber SecurityEthics and Social Impacts of AI

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

AI in Control: Rethinking Cybersecurity Compliance and Auditing

Open access

Overview

Integrity signals

Registry

Crossref · 10.2139/ssrn.5731707

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Cybersecurity environments impose distinct and high reliability, auditability, and reproducibility requirements because incorrect or unverifiable automatic actions are commonly found.

    “This approach is justified by the recent findings showing that cybersecurity environments impose distinct and high reliability, auditability, and reproducibility requirements because incorrect or unverifiable automatic actions are commonly found [9], [10], [11].”

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 140

  1. D Itani, 2024, Enhancing cybersecurity through compliance and auditing: A strategic approach to resilience, in: 2024 2nd International Conference on Cyber Resilience, ICCR, 1
  2. J Henriques, 2023, A forensics and compliance auditing framework for critical infrastructure protection, Int. J. Crit. Infrastruct. Prot, 42, 10.1016/j.ijcip.2023.100613 · DOI
  3. 2024, PCI DSS v4.0.1, Payment card industry data security standard
  4. Iso/Iec, 2022, Information technology -Security techniques -Information security management systems -requirements
  5. 2025, NIST special publication 800-53 revision 5 update 1: Security and privacy controls for information systems and organizations
  6. 2018, International Organization for Standardization, ISO 19011:2018 -Guidelines for auditing management systems
  7. Isa, 2004, Effective for audits of financial statements for periods beginning on or after December 15
  8. R Amor, 2021, The promise of automated compliance checking, Dev. Built Environ, 5
  9. B Hutchinson, 2024, Audit masquerade: How audits provide comfort rather than treatment for serious safety problems, Saf. Sci, 169, 10.1016/j.ssci.2023.106348 · DOI
  10. 2016, Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data (General Data Protection Regulation
  11. A Marotta, 2020, Analyzing the Interplay Between Regulatory Compliance and Cybersecurity
  12. N Mohamed, 2025, Artificial intelligence and machine learning in cybersecurity: a deep dive into state-of-the-art techniques and future paradigms, Knowl. Inf. Syst, 67, 10.1007/s10115-025-02429-y · DOI
  13. G Deng, 2024, PentestGPT: Evaluating and harnessing large language models for automated penetration testing, rd USENIX Security Symposium (USENIX Security 24), USENIX Association, 847
  14. C Zhang, 2019, A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data, Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), 1409, 10.1609/aaai.v33i01.33011409 · DOI
  15. E Papagiannidis, 2025, Responsible artificial intelligence governance: A review and research framework, J. Strateg. Inf. Syst, 34, 10.1016/j.jsis.2024.101885 · DOI · Paper page
  16. H A Haveman, 2019, Organizational theory: From classical sociology to the 1970s, Sociol. Compass, 13, 10.1111/soc4.12627 · DOI
  17. O Management, 2025, M-25-21: Accelerating federal use of AI through innovation, governance, and public trust
  18. S H Cen, 2024, From transparency to accountability and back: A discussion of access and evidence in AI auditing, Proceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, EAAMO'24, 1
  19. High-Level, 2019, Expert Group on Artificial Intelligence, Ethics Guidelines for Trustworthy AI
  20. M M Saeed, 2024, Alsharidah, Security, privacy, and robustness for trustworthy AI systems: A review, Comput. Electr. Eng, 119, 10.1016/j.compeleceng.2024.109643 · DOI
  21. G L Thanasas, 2024, Enhancing transparency and efficiency in auditing and regulatory compliance with disruptive technologies, Theor. Econ. Lett, 15, 214, 10.4236/tel.2025.151013 · DOI
  22. W Wang, 2024, A survey of major cybersecurity compliance frameworks, 2024 IEEE 10th Conference on Big Data Security on Cloud (BigDataSecurity), 23, 10.1109/BigDataSecurity62737.2024.00013 · DOI
  23. A Marotta, 2021, Convergence and divergence of regulatory compliance and cybersecurity, Issues Inf. Syst, 22
  24. 2025, Condition that deviates from expectations based on requirements specifications, design documents, user documents, or standards, or from someone's perceptions or experiences, NIST Computer Security Resource Center, Anomaly, NIST Computer Security Resource Center (Glossary)
  25. 2025, Definition: A mechanism providing a multifaceted approach to detecting cybersecurity attacks, Behavioral Anomaly Detection, NIST Computer Security Resource Center (Glossary)
  26. P I Bhat, 2020, Doctrinal legal research as a means of synthesizing facts, thoughts, and legal principles, Idea and Methods of Legal Research, 143, 10.1093/oso/9780199493098.003.0005 · DOI
  27. M A Ferrag, 2025, Generative AI in cybersecurity: A comprehensive review of LLM applications and vulnerabilities, 5, 1
  28. H Kheddar, 2025, Transformers and large language models for efficient intrusion detection systems: A comprehensive survey, Inf. Fusion, 124, 10.1016/j.inffus.2025.103347 · DOI
  29. H Kheddar, 2024, Reinforcement-Learning-Based intrusion detection in communication networks: A review, IEEE Commun. Surv. Tutor
  30. P Maniriho, 2024, A survey of recent advances in deep learning models for detecting malware in desktop and mobile platforms, ACM Comput. Surv, 56, 10.1145/3638240 · DOI
  31. L F Sikos, 2023, Cybersecurity knowledge graphs, Knowl. Inf. Syst, 65, 3511, 10.1007/s10115-023-01860-3 · DOI
  32. G Rjoub, 2023, A survey on explainable artificial intelligence for cybersecurity, IEEE Trans. Netw. Serv. Manag, 20, 5115, 10.1109/TNSM.2023.3282740 · DOI · Paper page
  33. M Macas, 2022, A survey on deep learning for cybersecurity: Progress, challenges, and opportunities, Comput. Netw, 212, 10.1016/j.comnet.2022.109032 · DOI
  34. 2025, AI in Control: Rethinking Cybersecurity Compliance and Auditing, GitHub repository
  35. F Shakerin, 2020, White-box induction from SVM models: Explainable AI with logic programming, Theory Pract, Log. Program, 20, 656
  36. A B Arrieta, 2020, Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI, 58, 82
  37. O Anava, 2016, From global to local, 29, 4917
  38. J Haug, 2022, Dynamic model tree for interpretable data stream learning, Proceedings of the 2022 IEEE 38th International Conference on Data Engineering, ICDE, 2562
  39. Z C Lipton, 2018, The mythos of model interpretability, Commun. ACM, 61, 36, 10.1145/3233231 · DOI · Paper page
  40. J Gama, 2002, Advances in Artificial Intelligence -IBERAMIA 2002, 765
  41. J Rosaler, 2024, Enhanced local explainability and trust scores with random forest proximities, Proceedings of the 5th ACM International Conference on AI in Finance, ICAIF'24, 521
  42. M Garnelo, 2016, Towards deep symbolic reinforcement learning
  43. M Landajuela, Discovering symbolic policies with deep reinforcement learning, Proceedings of the 38th International Conference on Machine Learning (ICML 2021), 139, 5979
  44. J Guo, 2017, Semantically enhanced software traceability using deep learning techniques, 3
  45. H Turb�, 2023, Evaluation of Post-hoc interpretability methods in Time-Series classification, Nat. Mach. Intell, 5, 250, 10.1038/s42256-023-00620-w · DOI
  46. J Zhang, 2024, Provenance-Enabled explainable AI, Proc. ACM Manag. Data, 2, 10.1145/3698826 · DOI
  47. Q Zhang, 2018, A survey on deep learning for big data, Inf. Fusion, 42, 146, 10.1016/j.inffus.2017.10.006 · DOI · Paper page
  48. G Marcus, 2018, Deep learning: A critical appraisal
  49. Y Bengio, 2019, The consciousness prior
  50. J S Ribeiro, 2019, Belief change and Non-Monotonic reasoning sans compactness, Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), 3019, 10.1609/aaai.v33i01.33013019 · DOI
  51. H Dibowski, 2024, Full traceability and provenance for knowledge graphs, Formal Ontology in Information Systems (FOIS 2024), 394, 223
  52. B C Grau, 2012, Completeness guarantees for incomplete ontology reasoners: theory and practice, J. Artif. Int. Res, 43, 419
  53. A M Ileri, 2024, VEL: A formally verified reasoner for OWL2 EL profile
  54. W J Clancey, 1983, The epistemology of a rule-based expert system -a framework for explanation, Artificial Intelligence, 20, 215, 10.1016/0004-3702(83)90008-5 · DOI · Paper page
  55. W R Swartout, 1993, Explanation in second generation expert systems, Second Generation Expert Systems, 543, 10.1007/978-3-642-77927-5_24 · DOI · Paper page
  56. J Mccarthy, 1969, Some philosophical problems from the standpoint of artificial intelligence, Machine Intelligence, 4, 463
  57. M Peters, 2015, Large scale Rule-Based reasoning using a Laptop, The Semantic Web. Latest Advances and New Domains, 104, 10.1007/978-3-319-18818-8_7 · DOI
  58. M Richardson, 2006, Markov logic networks, Mach. Learn, 62, 107, 10.1007/s10994-006-5833-1 · DOI · Paper page
  59. L Getoor, 2013, Probabilistic soft logic: A scalable approach for Markov random fields over Continuous-Valued variables, Theory, Practice, and Applications of Rules on the Web, 10.1007/978-3-642-39617-5_1 · DOI
  60. V Verreet, 2022, Inference and learning with model uncertainty in probabilistic logic programs, Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI 2022), 36, 10060, 10.1609/aaai.v36i9.21245 · DOI
  61. B Teixeira, 2025, Explainable AI framework for reliable and transparent automated energy management in buildings, Energy Build, 347, 10.1016/j.enbuild.2025.116246 · DOI
  62. M Zhou, 2020, Progress in neural NLP: Modeling, learning, and reasoning, Engineering, 6, 275
  63. T Mikolov, 2013, Distributed representations of words and phrases and their compositionality, Advances in Neural Information Processing Systems, 26, 3111
  64. J Devlin, 2019, BERT: Pre-training of deep bidirectional transformers for language understanding, Proceedings of the 2019 Conference of the North American Chapter, 1, 4171, 10.18653/v1/N19-1423 · DOI · Paper page
  65. A Vaswani, 2017, Attention is all you need, Advances in Neural Information Processing Systems, 30, 5998
  66. Y Liu, 2022, Rethinking Attention-Model explainability through faithfulness violation test, Proceedings of the 39th International Conference on Machine Learning (ICML 2022), 162, 7281
  67. S L Blodgett, 2020, Language (technology) is power: A critical survey of ''Bias'' in NLP, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 5454, 10.18653/v1/2020.acl-main.485 · DOI · Paper page
  68. S Gururangan, 2020, Don't stop pretraining: Adapt language models to domains and tasks, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 8342, 10.18653/v1/2020.acl-main.740 · DOI · Paper page
  69. T Rockt�schel, 2017, End-to-end differentiable proving, Advances in Neural Information Processing Systems, 30, 3788
  70. J Xu, 2018, Van den Broeck, A semantic loss function for deep learning with symbolic knowledge, Proceedings of the 35th International Conference on Machine Learning, 80, 5502
  71. Z Hu, 2020, Harnessing deep neural networks with logic rules, Information and Software Technology, 195
  72. G Spillo, 2024, Recommender systems based on neuro-symbolic knowledge graph embeddings encoding first-order logic rules, User Model. User-Adapt. Interact, 34, 2039, 10.1007/s11257-024-09417-x · DOI
  73. S Bhardwaj, 2023, Integrating a Rule-Based approach to malware detection with an LSTM-Based feature selection technique, SN Comput. Sci, 4, 10.1007/s42979-023-02177-2 · DOI
  74. Z Liu, 2022, A Neural-Symbolic approach to natural language understanding, Findings of the Association for Computational Linguistics: EMNLP 2022, 2159
  75. P Barbiero, 2023, Proceedings of the 40th International Conference on Machine Learning, 202, 1801
  76. X Zhang, 2024, Neuro-symbolic AI: Explainability, challenges, and future trends
  77. A Baheri, 2025, Hierarchical Neuro-Symbolic decision transformer
  78. S Badreddine, 2022, Logic tensor networks, Artificial Intelligence, 303, 10.1016/j.artint.2021.103649 · DOI
  79. S De Giorgis, 2025, Neurosymbolic graph enrichment for grounded world models, Inf. Process. Manage, 62, 10.1016/j.ipm.2025.104127 · DOI
  80. K Yi, 2018, Neural-Symbolic VQA: Disentangling reasoning from vision and language understanding, Advances in Neural Information Processing Systems, 31, 1031
  81. T Eiter, 2022, Symbolic ASP pipeline for visual question answering, Theory Pract, Log. Program, 22, 739
  82. R Riaz, 2025, A novel ensemble wasserstein GAN framework for effective anomaly detection in industrial internet of things environments, Sci. Rep, 15, 10.1038/s41598-025-07533-1 · DOI
  83. Z C Lipton, 2017, Precise recovery of latent vectors from generative adversarial networks
  84. I J Goodfellow, 2014, Generative adversarial nets, Advances in Neural Information Processing Systems, 27, 2672
  85. S Wiegreffe, 2019, Attention is not not explanation, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 11, 10.18653/v1/D19-1002 · DOI · Paper page
  86. K Mai, 2025, LLM-Based Fine-Grained ABAC policy generation, Proceedings of the 11th International Conference on Information Systems Security and Privacy, 2, 204
  87. C Chen, 2025, CLEAR: Towards contextual LLM-Empowered privacy policy analysis and risk generation for large language model applications, Proceedings of the 30th International Conference on Intelligent User Interfaces, IUI'25, 277, 10.1145/3708359.3712156 · DOI
  88. F Piccialli, 2025, A comprehensive survey on autonomous agents in distributed AI for industry 4.0, 291
  89. M A Ferrag, 2025, From LLM reasoning to autonomous AI agents: A comprehensive review
  90. N Kumar, 2025, Blockchain technology in supply chain management: Innovations, applications, and challenges, Telemat. Inform. Rep, 18
  91. F Bassan, 2024, From smart legal contracts to contracts on blockchain: An empirical investigation, Comput. Law Secur. Rev, 55
  92. S Gorbunov, 2022, Predicate encryption for circuits from LWE, Advances in Cryptology -CRYPTO 2015, 503
  93. J Wang, 2019, 16th USENIX Symposium on Networked Systems Design and Implementation, NSDI'19, USENIX Association, 95
  94. A K Jain, 2025, A survey on scalable consensus algorithms for blockchain technology, Cyber Secur. Appl, 3
  95. M Bartoletti, 2019, A minimal core calculus for solidity contracts, Data Privacy Management, Cryptocurrencies and Blockchain Technology, 233, 10.1007/978-3-030-31500-9_15 · DOI
  96. J A Kroll, 2021, Outlining traceability: A principle for operationalizing accountability in computing systems, Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21), 758, 10.1145/3442188.3445937 · DOI · Paper page
  97. R Guidotti, 2018, A survey of methods for explaining Black Box models, ACM Comput. Surv, 51, 1, 10.1145/3236009 · DOI · Paper page
  98. C Rudin, 2019, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nat. Mach. Intell, 1, 206, 10.1038/s42256-019-0048-x · DOI · Paper page
  99. L H Gilpin, 2018, Explaining explanations: An overview of interpretability of machine learning, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics, 80
  100. P W Grimm, 2021, Artificial intelligence as evidence, Northwest. J. Technol. Intellect. Prop, 19, 9
  101. J Gama, 2014, A survey on concept drift adaptation, ACM Comput. Surv, 46, 1, 10.1145/2523813 · DOI · Paper page
  102. 2023, Artificial intelligence risk management framework (AI RMF) 1.0, 2023, Voluntary framework to manage AI risks across design, development, use, and evaluation
  103. 2023, International Organization for Standardization, ISO/IEC 42001:2023 -information technology -Artificial intelligence -Management system
  104. Z Cheng, 2025, A survey on explainable deep reinforcement learning
  105. M Zolanvari, 2019, Machine Learning-Based network vulnerability analysis of industrial internet of things, IEEE Internet Things J, 6, 6822, 10.1109/JIOT.2019.2912022 · DOI
  106. V.-H Pham, 2024, A Coverage-Guided fuzzing method for automatic software vulnerability detection using reinforcement Learning-Enabled Multi-Level input mutation, IEEE Access, 12, 129064, 10.1109/ACCESS.2024.3421989 · DOI
  107. G Muriithi, 2025, Vulnerability assessment and detection of stealthy sequential cyberattacks in hybrid tracked vehicles, IEEE Trans. Transp. Electrif, 11, 6472, 10.1109/TTE.2024.3510458 · DOI
  108. G Lin, 2021, Software vulnerability discovery via learning Multi-Domain knowledge bases, IEEE Trans. Dependable Secur. Comput, 18, 2469, 10.1109/TDSC.2019.2954088 · DOI
  109. X Zhang, 2025, Effectively detecting software vulnerabilities via leveraging features on program slices, IEEE Internet Things J, 12, 8033, 10.1109/JIOT.2025.3541090 · DOI
  110. A Bahaa, 2024, DB-CBIL: A DistilBert-based transformer hybrid model using CNN and BiLSTM for software vulnerability detection, IEEE Access, 12, 64446, 10.1109/ACCESS.2024.3396410 · DOI
  111. L Pandolfo, 2025, A Knowledge-Driven approach to threat validation and security reasoning in modular systems, IEEE Access, 13, 149817, 10.1109/ACCESS.2025.3602292 · DOI
  112. F Sikder, 2025, Efficient adaptation of large language models for smart contract vulnerability detection, Proceedings of the 21st International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE'25, 65
  113. P Radanliev, 2020, Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments 2013 cyber risk in the colonisation of Mars, Saf. Extrem. Environ, 2, 219, 10.1007/s42797-021-00025-1 · DOI
  114. S.-H Park, 2021, Multi-perspective APT attack risk assessment framework using Risk-Aware problem domain ontology, 2021 IEEE 29th International Requirements Engineering Conference Workshops, 400
  115. V Vassilev, 2022, Risk assessment in transactions under threat as partially observable Markov decision process, Optimization in Artificial Intelligence and Data Sciences, 8, 199
  116. D Yao, 2024, Cyber risk assessment framework for the construction industry using machine learning techniques, Information and Software Technology, 14
  117. S U Lee, 2025, Software security mapping framework: Operationalization of security requirements
  118. A Adebayo, 2022, Automated compliance blueprint optimization with artificial intelligence
  119. L Elluri, 2018, An integrated knowledge graph to automate GDPR and PCI DSS compliance, 2018 IEEE International Conference on Big Data (Big Data), 1266, 10.1109/BigData.2018.8622236 · DOI
  120. R Bar-Haim, 2023, Proceedings of the 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD), in: CODS-COMAD '23, 167
  121. V Barbara, 2023, Neuro-Symbolic AI for compliance checking of electrical control panels
  122. K Jain, 2025, Using machine learning to analyze and detect anomalies in SELinux security policies, The 16th International Conference on Ambient Systems, Networks and Technologies Networks (ANT)/ the 8th International Conference on Emerging Data, 257, 880
  123. L, 2024, Automated cybersecurity compliance and threat response using AI, blockchain and smart contracts, Int. J. Inf. Technol
  124. M C Ghanem, 2022, Hierarchical reinforcement learning for efficient and effective automated penetration testing of large networks, J. Intell. Inf. Syst
  125. H P T Nguyen, 2025, Realistic training environment for reinforcement learning pentesting agents, Comput. Secur, 148
  126. A Chowdhary, 2023, Generative adversarial network (GAN)-Based autonomous penetration testing for web applications, Sensors, 23, 10.3390/s23188014 · DOI
  127. F Y Loumachi, 2025, Advancing cyber incident timeline analysis through Retrieval-Augmented generation and large language models, Computers, 14, 10.3390/computers14020067 · DOI
  128. M Pourvahab, 2019, Digital forensics architecture for evidence collection and provenance preservation in IaaS cloud environment using SDN and blockchain technology, IEEE Access, 7, 153349, 10.1109/ACCESS.2019.2946978 · DOI
  129. S Brotsis, 2019, Blockchain solutions for forensic evidence preservation in IoT environments, 2019 IEEE Conference on Network Softwarization (NetSoft), 110, 10.1109/NETSOFT.2019.8806675 · DOI
  130. O P Olawale, 2024, Cybersecurity anomaly detection: AI and ethereum blockchain for a secure and tamperproof IoHT data management, IEEE Access, 12, 131605, 10.1109/ACCESS.2024.3460428 · DOI
  131. M Arafah, 2025, An enhanced BiGAN architecture for network intrusion detection, Knowl.-Based Syst, 314, 10.1016/j.knosys.2025.113178 · DOI
  132. K Alam, 2025, Adaptive defense: Zero-Day attack detection in NIDS with deep reinforcement learning, IEEE Access, 13, 116345, 10.1109/ACCESS.2025.3585445 · DOI
  133. A A Hammad, 2024, Deep reinforcement learning for adaptive cyber defense in network security, Proceedings of the Cognitive Models and Artificial Intelligence Conference, AICCONF '24, 292, 10.1145/3660853.3660930 · DOI
  134. Z Liu, 2025, AutoBnB: Multi-Agent incident response with large language models, 2025 13th International Symposium on Digital Forensics and Security, 1
  135. S R Castro, 2025, Large language models are autonomous cyber defenders
  136. H Gu, 2024, Artificial intelligence co-piloted auditing, Int. J. Account. Inf. Syst, 54
  137. J H Chin, 2025, Automating security audit using large language model based agent: An exploration experiment
  138. Iso/Iec, 2017, ISO/IEC 17025:2017 -General requirements for the competence of testing and calibration laboratories
  139. C Donalds, 2020, Cybersecurity compliance behavior: Exploring the influences of individual decision style and other antecedents, Int. J. Inf. Manage, 51
  140. S Theil, 2025, Carefully tailored: Doctrinal methods and empirical contributions, Oxf. J. Leg. Stud, 45, 1047, 10.1093/ojls/gqaf029 · DOI

Cited by

Sorted by citations

Metadata

Authors
Loumachi, F. Y., Lacerda, M., Ouazzane, K., Adnane, A., & Adamyk, O.
Affiliations
  • London Metropolitan University (GB)
  • Loughborough University (GB)
Corresponding author
Loumachi, Fatma Yasmine
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2025
ISSN
1556-5068 (ISSN-L)
OpenAlex
W4416308259
Cited by
0
References
1
Type
posted content
Language
en
Open access
Locations
  • SSRN Electronic Journal (acceptedVersion)
  • London Met Repository (London Metropolitan University) (submittedVersion)
Topics
  • Social Sciences › Law › Digitalization, Law, and Regulation
  • Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Keywords
artificial intelligence, cybersecurity compliance, cybersecurity auditing, AI governance

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review