CoChat
Sign Up
Cited by 34Network Security and Intrusion DetectionInformation and Cyber SecurityAdversarial Robustness in Machine Learning

A Review of Agentic AI in Cybersecurity: Cognitive Autonomy, Ethical Governance, and Quantum-Resilient Defense

Open access
ADABARA, IBRAHIM, Olaniyi Sadiq, Bashir, Nuhu Shuaibu, Aliyu, Ibarahim Danjuma, Yale, Venkateswarlu, Maninti·2025·F1000Research
DOI 10.12688/f1000research.169337.1PMC PMC12569510
Open this paper in CoChat — free accountOpen PDF

Abstract

Agentic Artificial Intelligence (AAI) refers to autonomous, adaptable, and goal-directed systems capable of proactive decision-making in dynamic environments. These agentic systems extend beyond reactive AI by leveraging cognitive architectures and reinforcement learning to enhance adaptability, resilience, and self-sufficiency in cybersecurity contexts. As cyber threats grow in sophistication and unpredictability, Agentic AI is rapidly becoming a foundational technology for intelligent cyber defense, enabling capabilities such as real-time anomaly detection, predictive threat response, and quantum-resilient protocols. This narrative review synthesizes literature from 2005 to 2025, integrating academic, industry, and policy sources across three thematic pillars: cognitive autonomy, ethical governance, and quantum-resilient defense. The review identifies key advancements in neuromorphic architectures, cross-jurisdictional governance models, and hybrid defense systems that adapt to evolving threat landscapes. It also exposes critical challenges, including dual-use risks, governance interoperability, and preparedness for post-quantum security. This work contributes a multi-dimensional conceptual framework linking governance mechanisms to operational practice, maps resilience strategies across conventional and quantum vectors, and outlines a forward-looking roadmap for secure, ethical, and adaptive deployment of Agentic AI in cybersecurity. The synthesis aims to support policymakers, developers, and security practitioners in navigating the accelerating convergence of autonomy, security, and AI ethics.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.12688/f1000research.169337.1

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • The fourth generation of agentic cybersecurity systems shows a notable shift towards greater stability, predictability and regulatory control, driven by the adoption of standardized tool schemas.

    “We observe a notable shift towards greater stability, predictability and regulatory control in the fourth generation of agentic cybersecurity systems. This shift is driven by the adoption of standardized tool schemas [35].”

    From The Evolution of Agentic AI in Cybersecurity: From Single LLM Reasoners to Multi-Agent Systems and Autonomous Pipelines (2026) · cites this paper as [35]

    Section 3.1 describes a fourth phase, the agentic AI era, characterized by autonomous agents with embedded governance. Section 5.1 also describes a shift toward interoperable tool orchestration and compliance frameworks. Neither passage says that standardized tool schemas drive greater stability, predictability, or regulatory control. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • This is in stark contrast to the Gen-1 and Gen-2 systems, for which small prompt changes or LLM variability could alter system behavior.

    “This is in stark contrast to the Gen-1 and Gen-2 systems, for which small prompt changes or LLM variability could alter system behavior [35].”

    From The Evolution of Agentic AI in Cybersecurity: From Single LLM Reasoners to Multi-Agent Systems and Autonomous Pipelines (2026) · cites this paper as [35]

    Section 3.1 describes an evolution from rule-based systems through generative AI to agentic AI, but does not identify Gen-1 or Gen-2 systems or discuss their sensitivity to small prompt changes. Searches for prompt sensitivity and LLM variability found no passage in our copy addressing the claimed behavior. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

  • Gen-4 systems are highly circumscribed: agents interact with enterprise security solutions like SIEM platforms, EDR agents, threat-intelligence systems, and SOAR pipelines using a set of predefined schemas that explicitly specify the structure and semantics of each interaction.

    “Whereas in earlier generations, agents would spontaneously make API calls or create ad hoc command structures, the Gen-4 systems are highly circumscribed. Agents interact with other enterprise security solutions like SIEM platforms, EDR agents, threat-intelligence systems, and SOAR pipelines, using a set of predefined schemas that explicitly specify the structure and semantics”

    From The Evolution of Agentic AI in Cybersecurity: From Single LLM Reasoners to Multi-Agent Systems and Autonomous Pipelines (2026) · cites this paper as [35]

    Section 3.1 describes broad phases in the evolution of cybersecurity AI, and section 5.1 mentions interoperable tool orchestration, but neither addresses the claimed Gen-4 interaction rules. Searches for Gen-4, predefined schemas, SIEM, EDR, and SOAR found no passage specifying schema-governed interactions with those systems. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 124

  1. [3]Kott A: Autonomous Intelligent Cyber-defense Agent: Introduction and Overview. ArXiv. 2023; abs/2304.1. 10.48550/ARXIV.2304.12408 · DOI
  2. [4]Sengupta A: Securing the Autonomous Future A Comprehensive Analysis of Security Challenges and Mitigation Strategies for AI Agents. Int. J. Sci. Res. Eng. Manag. Dec. 2024; 08(12): 1– 2. 10.55041/IJSREM40091 · DOI
  3. [5]Hauptman AI Schelble BG McNeese NJ: Adapt and overcome: Perceptions of adaptive autonomous agents for human-AI teaming. Comput. Hum. Behav. Jan. 2023; 138: 107451. 10.1016/J.CHB.2022.107451 · DOI · Paper page
  4. [6]Laitinen A Sahlgren O: AI Systems and Respect for Human Autonomy. Front. Artif. Intell. Oct. 2021; 4. 10.3389/FRAI.2021.705164/PDF 34765969 PMC8576577 · DOI
  5. [7]Bondhala S: Cybersecurity in AI-Driven Data Centers: Reinventing Threat Detection. Int. J. Adv. Res. Sci. Commun. Technol. Mar. 2025; 510– 519. 10.48175/IJARSCT-24464 · DOI
  6. [8]Mohammed DF: Adaptive Cyber Defense: Leveraging AI for Real Time Threat Detection. Int. J. Sci. Res. Eng. Manag. May 2025; 09(05): 1– 9. 10.55041/IJSREM47095 · DOI
  7. [9]Jimmy F: Emerging Threats: The Latest Cybersecurity Risks and the Role of Artificial Intelligence in Enhancing Cybersecurity Defenses. Int. J. Sci. Res. Manag. Feb. 2021; 9(02): 564– 574. 10.18535/IJSRM/V9I2.EC01 · DOI
  8. [10]Ajayi R Masunda M: Integrating edge computing, data science and advanced cyber defense for autonomous threat mitigation. Int. J. Sci. Res. Arch. May 2025; 15(2): 063– 080. 10.30574/IJSRA.2025.15.2.1292 · DOI
  9. [11]Sindiramutty SR: Autonomous Threat Hunting: A Future Paradigm for AI-Driven Threat Intelligence. ArXiv. 2023; abs/2401.0. 10.48550/ARXIV.2401.00286 · DOI
  10. [12]Balasubramani R Biradar VG: Empowering Autonomous Decision-Making Through Quantum Reinforcement Learning and Cognitive Neuromorphic Frameworks. 2024 4th Int. Conf. Mob. Networks Wirel. Commun. 2024; pp. 1– 7. 10.1109/ICMNWC63764.2024.10872223 · DOI
  11. [13]Tallam K: Transforming Cyber Defense: Harnessing Agentic and Frontier AI for Proactive, Ethical Threat Intelligence. ArXiv. 2025; abs/2503.0. 10.48550/ARXIV.2503.00164 · DOI
  12. [14]Ratnawita R: Cybersecurity in the AI Era Measures Deepfake Threats and Artificial Intelligence-Based Attacks. J. Am. Inst. Feb. 2025; 2(2): 180– 189. 10.71364/S3EMXX77 · DOI
  13. [15]Chimamiwa G: Managing cyber risks in the face of AI- and ML - Driven Adversarial Attacks. Integr. AI Technol. Mod. Bus. Pract. Oct. 2024; 71– 79. 10.70301/CONF.SBS-JABR.2024.1/1.6 · DOI
  14. [16]Elmisery AM Sertovic M Zayin A: Cyber Threats in Financial Transactions - Addressing the Dual Challenge of AI and Quantum Computing. ArXiv. 2025; abs/2503.1. 10.48550/ARXIV.2503.15678 · DOI
  15. [17]Dubey V Shende P Kumbhare B: Exploring AI Techniques for Quantum Threat Detection and Prevention. Indian J. Comput. Sci. Technol. Jan. 2025; 08– 12. 10.59256/INDJCST.20250401002 · DOI
  16. [18]Lekota NF: Governance Considerations of Adversarial Attacks on AI Systems. Int. Conf. AI Res. 2024; 4: 227– 233. 10.34190/ICAIR.4.1.3194 · DOI
  17. [19]Birkstedt T Minkkinen M Tandon A: AI governance: themes, knowledge gaps and future agendas. Internet Res. 2023; 33(7): 133– 167. 10.1108/INTR-01-2022-0042/FULL/PDF · DOI
  18. [20]Polemi N Praça I Kioskli K: Challenges and efforts in managing AI trustworthiness risks: a state of knowledge. Front. Big Data. 2024; 7. 10.3389/FDATA.2024.1381163/PDF 38798307 PMC11119750 · DOI
  19. [21]Ogunmolu AM: Enhancing Data Security in Artificial Intelligence Systems: A Cybersecurity and Information Governance Approach. J. Eng. Res. Reports. May 2025; 27(5): 154– 172. 10.9734/JERR/2025/V27I51500 · DOI
  20. [23]Perrella A Bernardi FF Bisogno M: Bridging the gap in AI integration: enhancing clinician education and establishing pharmaceutical-level regulation for ethical healthcare. Front. Med. 2024; 11. 10.3389/FMED.2024.1514741/FULL Reference Source PMC11693746 39748923 · DOI
  21. [24]Tun HM Naing L Malik OA: Navigating ASEAN Region Artificial Intelligence (AI) Governance Readiness in Healthcare. Heal. Policy Technol. Mar. 2025; 14(2): 100981. 10.1016/J.HLPT.2025.100981 · DOI
  22. [25]Bellini V: Artificial intelligence and anesthesia: a narrative review. Ann. Transl. Med. May 2022; 10(9): 528– 528. 10.21037/ATM-21-7031 35928743 PMC9347047 · DOI
  23. [26]Paez A: Grey literature: An important resource in systematic reviews. J. Evid. Based Med. Dec. 2017. 10.1111/JEBM.12265 29266844 · DOI
  24. [27]Papageorgiou G Skamnia E Economou P: AI-Assisted Literature Review: Integrating Visualization and Geometric Features for Insightful Analysis. WIREs Data Min. Knowl. Discov. Jun. 2025; 15(2). 10.1002/WIDM.70016 · DOI
  25. [28]Stevens T: Knowledge in the grey zone: AI and cybersecurity. Digit. War. Dec. 2020; 1(1–3): 164– 170. 10.1057/S42984-020-00007-W · DOI
  26. [29]Thórisson K Helgasson H: Cognitive Architectures and Autonomy: A Comparative Review. J. Artif. Gen. Intell. May 2012; 3(2): 1– 30. 10.2478/V10229-011-0015-3 · DOI
  27. [30]Sharma D: Innovations and Future Directions in Securing Digital Environments. Int. J. Sci. Res. Eng. Manag. Apr. 2025; 09(04): 1– 9. 10.55041/IJSREM45950 · DOI
  28. [31]Huber-Liebl M Römer R Wirsching G: Quantum-inspired cognitive agents. Front. Appl. Math. Stat. Sep. 2022; 8. 10.3389/FAMS.2022.909873/PDF · DOI
  29. [32]Yilmaz L: A quantum cognition model for simulating ethical dilemmas among multi-perspective agents. J. Simul. Apr. 2020; 14(2): 98– 106. 10.1080/17477778.2019.1603090 · DOI
  30. [33]Chaffer TJ Goldston J Okusanya B: Decentralized Governance of Autonomous AI Agents. Probl. Polit. Auth. 2024; 81– 100. 10.1057/9781137281661_5 · DOI
  31. [34]Ranjan R Gupta S Singh SN: LOKA Protocol: A Decentralized Framework for Trustworthy and Ethical AI Agent Ecosystems. ArXiv. 2025; abs/2504.1. 10.48550/ARXIV.2504.10915 · DOI
  32. [35]Byrne JA: Improving the peer review of narrative literature reviews. Res. Integr. Peer Rev. Dec. 2016; 1(1): 12. 10.1186/S41073-016-0019-2 29451529 PMC5803579 · DOI
  33. [36]Edmonds B: A Context- and Scope-Sensitive Analysis of Narrative Data to Aid the Specification of Agent Behaviour. J. Artif. Soc. Soc. Simul. Jan. 2015; 18(1). 10.18564/JASSS.2715 · DOI
  34. [37]Shah Z: ETHICAL CONSIDERATIONS IN THE USE OF AI FOR ACADEMIC RESEARCH AND SCIENTIFIC DISCOVERY: A NARRATIVE REVIEW. Insights-Journal Life Soc. Sci. Apr. 2025; 3(2): 183– 189. 10.71000/JFESGV69 · DOI
  35. [38]D’Elia A: Artificial intelligence and health inequities in primary care: a systematic scoping review and framework. Fam. Med. Community Heal. Nov. 2022; 10: e001670. 10.1136/FMCH-2022-001670 36450391 PMC9716837 · DOI
  36. [39]Pautasso M: The Structure and Conduct of a Narrative Literature Review. A Guid. to Sci. Career. Jan. 2019; 299– 310. 10.1002/9781118907283.CH31 · DOI · Paper page
  37. [40]Faisal A Cupiadi H: Cybersecurity in Digital Supply Chains: A Narrative Review of Threats and Strategic Frameworks for Sustainable Logistics. Sinergi Int. J. Logist. Aug. 2024; 2(3): 174– 186. 10.61194/SIJL.V2I3.728 · DOI
  38. [42]Cooper C: Revisiting the need for a literature search narrative: A brief methodological note. Res. Synth. Methods. Sep. 2018; 9(3): 361– 365. 10.1002/JRSM.1315 30031371 · DOI
  39. [43]Ferenhof HA Fernandes RF: Demystifying literature review in the AI Era. Biblios J. Librariansh. Inf. Sci. 2025; 88. 10.5195/BIBLIOS.2025.1317 · DOI
  40. [44]Adhikari D Thapaliya S: An Overview of AI Applications in Cybersecurity for IT Management. NPRC J. Multidiscip. Res. Oct. 2024; 1(4): 121– 133. 10.3126/NPRCJMR.V1I4.70951 · DOI
  41. [46]Ahmady E Mojadadi AR Hakimi M: A Comprehensive Review of Cybersecurity Measures in the IoT Era. J. Soc. Sci. Util. Technol. Feb. 2024; 2(1): 288– 298. 10.70177/JSSUT.V2I1.722 · DOI
  42. [47]Veritti D Rubinato L Sarao V: Behind the mask: a critical perspective on the ethical, moral, and legal implications of AI in ophthalmology. Graefes Arch. Clin. Exp. Ophthalmol. Mar. 2024; 262(3): 975– 982. 10.1007/S00417-023-06245-4 37747539 PMC10907411 · DOI
  43. [48]Petticrew M Roberts H: What Sorts of Studies do I Include in the Review? Deciding on the Review’s Inclusion/Exclusion Criteria. Syst. Rev. Soc. Sci. Jan. 2006; 57– 78. 10.1002/9780470754887.CH3 · DOI
  44. [49]Swift JK Wampold BE: Inclusion and exclusion strategies for conducting meta-analyses. Psychother. Res. May 2018; 28(3): 356– 366. 10.1080/10503307.2017.1405169 29169303 · DOI
  45. [51]Sarkar S Bhatia G: Writing and appraising narrative reviews. J. Clin. Sci. Res. Jul. 2021; 10(3): 169– 172. 10.4103/JCSR.JCSR_1_21 · DOI
  46. [52]Christou PA: Thematic Analysis through Artificial Intelligence (AI). Qual. Rep. Feb. 2024; 29(2): 560– 576. 10.46743/2160-3715/2024.7046 · DOI
  47. [53]Rawat DB Bajracharya C: The Intersection of Quantum Computing, AI, and Cybersecurity: Challenges and Opportunities. 2024 IEEE 6th Int. Conf. Trust. Priv. Secur. Intell. Syst. Appl. 2024; pp. 176– 181. 10.1109/TPS-ISA62245.2024.00029 · DOI
  48. [54]Young DL Bigham M Bradbury M: SMU-DDI Cyber Autonomy Range. 2022 IEEE Appl. Imag. Pattern Recognit. Work. 2022; vol. 2022-October: pp. 1– 5. 10.1109/AIPR57179.2022.10092228 · DOI
  49. [55]Ali S Wang J Leung VCM: AI-driven fusion with cybersecurity: Exploring current trends, advanced techniques, future directions, and policy implications for evolving paradigms- A comprehensive review. Inf. Fusion. Jun. 2025; 118: 102922. 10.1016/J.INFFUS.2024.102922 · DOI
  50. [56]Çakır AM: AI Driven Cybersecurity. Hum. Comput. Interact. Dec. 2024; 8(1): 119. 10.62802/JG7GGE06 · DOI
  51. [57]Vemuri N Thaneeru N Tatikonda VM: Adaptive generative AI for dynamic cybersecurity threat detection in enterprises. Int. J. Sci. Res. Arch. Feb. 2024; 11(1): 2259– 2265. 10.30574/IJSRA.2024.11.1.0313 · DOI
  52. [58]Nour SM Said SA: Harnessing the Power of AI for Effective Cybersecurity Defense. 2024 6th Int. Conf. Comput. Informatics. 2024; pp. 98– 102. 10.1109/ICCI61671.2024.10485059 · DOI
  53. [59]Samtani S Kantarcioglu M Chen H: Trailblazing the Artificial Intelligence for Cybersecurity Discipline. ACM Trans. Manag. Inf. Syst. Dec. 2020; 11(4): 1– 19. 10.1145/3430360 · DOI
  54. [60]Schneider J: Generative to Agentic AI: Survey, Conceptualization, and Challenges. ArXiv. 2025; abs/2504.1. 10.48550/ARXIV.2504.18875 · DOI · Paper page
  55. [63]Tallam K: Transforming Cyber Defense: Harnessing Agentic and Frontier AI for Proactive, Ethical Threat Intelligence. ArXiv. 2025; abs/2503.0. 10.48550/ARXIV.2503.00164 · DOI
  56. [64]Oesch S Hutchins J Austria P: Agentic AI and the Cyber Arms Race. Computer (Long. Beach. Calif). 2025; 58(5): 82– 85. 10.1109/MC.2025.3544116 · DOI
  57. [65]Wu J You H Du J: AI Generations: From AI 1.0 to AI 4.0. ArXiv. 2025; abs/2502.1. 10.48550/ARXIV.2502.11312 PMC12241030 40642660 · DOI
  58. [66]Bako NZ Ozioko CN Sanni IO: The Integration of AI and blockchain technologies for secure data management in cybersecurity. World J. Adv. Res. Rev. Mar. 2025; 25(3): 1666– 1697. 10.30574/WJARR.2025.25.3.0784 · DOI
  59. [67]Murugesan S: The Rise of Agentic AI: Implications, Concerns, and the Path Forward. IEEE Intell. Syst. 2025; 40(2): 8– 14. 10.1109/MIS.2025.3544940 · DOI
  60. [68]Girish Savadatti S Srinivasan K Hu YC: A Bibliometric Analysis of Agent-Based Systems in Cybersecurity and Broader Security Domains: Trends and Insights. IEEE Access. 2025; 13: 90– 119. 10.1109/ACCESS.2024.3520583 · DOI
  61. [69]Albahri O Alamoodi A: Cybersecurity and Artificial Intelligence Applications: A Bibliometric Analysis Based on Scopus Database. Mesopotamian J. Cyber Secur. 2023; 2023: 158– 169. 10.58496/MJCSC/2023/018 · DOI
  62. [70]Ali S Wang J Leung VCM: AI-driven fusion with cybersecurity: Exploring current trends, advanced techniques, future directions, and policy implications for evolving paradigms- A comprehensive review. Inf. Fusion. Jun. 2025; 118: 102922. 10.1016/J.INFFUS.2024.102922 · DOI
  63. [71]Achuthan K Ramanathan S Srinivas S: Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directions. Front. Big Data. 2024; 7. 10.3389/FDATA.2024.1497535 39703783 PMC11656524 · DOI
  64. [72]Mintoo AA Saimon ASM Bakhsh MM: NATIONAL RESILIENCE THROUGH AI-DRIVEN DATA ANALYTICS AND CYBERSECURITY FOR REAL-TIME CRISIS RESPONSE AND INFRASTRUCTURE PROTECTION. Am. J. Sch. Res. Innov. Mar. 2022; 1(1): 137– 169. 10.63125/SDZ8KM60 · DOI
  65. [73]Alneyadi MRMAH Normalini MK: Intelligent Protection: A Study of the Key Drivers of Intention to Adopt Artificial Intelligence (AI) Cybersecurity Systems in the UAE. Interdiscip. J. Inf. Knowl. Manag. 2025; 20: 003. 10.28945/5430 · DOI
  66. [74]Arefin S Zannat NT Global Health Institute Research Team United states: Securing AI in Global Health Research: A Framework for Cross-Border Data Collaboration. Clin. Med. Heal. Res. J. Mar. 2025; 5(02): 1187– 1193. 10.18535/CMHRJ.V5I02.457 · DOI
  67. [75]Händler T: Balancing Autonomy and Alignment: A Multi-Dimensional Taxonomy for Autonomous LLM-powered Multi-Agent Architectures. ArXiv. 2023; abs/2310.0. 10.48550/ARXIV.2310.03659 · DOI
  68. [76]Cihon P Stein M Bansal G: Measuring AI agent autonomy: Towards a scalable approach with code inspection. ArXiv. 2025; abs/2502.1. 10.48550/ARXIV.2502.15212 · DOI
  69. [77]Karim MM Van DH Khan S: AI Agents Meet Blockchain: A Survey on Secure and Scalable Collaboration for Multi-Agents. Futur. Internet. Feb. 2025; 17(2). 10.3390/FI17020057 · DOI
  70. [78]Frenette J: Systems and Methods for Decentralized AI Governance Networks (DAGN) with Tokenized Power Control (TPC) for Enforcing Human-Centric AI. Int. J. Res. Appl. Sci. Eng. Technol. Jan. 2025; 13(1): 609– 613. 10.22214/IJRASET.2025.66304 · DOI
  71. [79]Ranjan R Gupta S Singh SN: LOKA Protocol: A Decentralized Framework for Trustworthy and Ethical AI Agent Ecosystems. ArXiv. 2025; abs/2504.1. 10.48550/ARXIV.2504.10915 · DOI
  72. [80]Loevenich JF Adler E Mercier R: Design of an Autonomous Cyber Defence Agent using Hybrid AI models. 2024 Int. Conf. Mil. Commun. Inf. Syst. 2024; pp. 1– 10. 10.1109/ICMCIS61231.2024.10540988 · DOI
  73. [81]Meyer-Vitali A Mulder W De Boer MHT: Modular design patterns for neural-symbolic integration: refinement and combination CEUR Workshop Proc. 2022; vol. 3212. Accessed: Aug. 13, 2025. Reference Source
  74. [83]Bekkum M van Boer M de Harmelen F van: Modular design patterns for hybrid learning and reasoning systems: a taxonomy, patterns and use cases. Spring. Sep. 2021; 51(9): 6528– 6546. 10.1007/S10489-021-02394-3 · DOI
  75. [84]Rodriguez S Thangarajah J Davey A: Design Patterns for Explainable Agents (XAg). 2024; 1621– 1629. 10.5555/3635637.3663023 · DOI
  76. [85]Schulte A Donath D Lange DS: Design Patterns for Human-Cognitive Agent Teaming. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 2016; 9736: 231– 243. 10.1007/978-3-319-40030-3_24 · DOI
  77. [86]Benraouane SA: AI Management System Certification According to the ISO/IEC 42001 Standard. AI Manag. Syst. Certif. Accord. to ISO/IEC 42001 Stand. How to Audit. Certify, Build Responsible AI Syst. Jan. 2024; pp. 1– 190. 10.4324/9781003463979 · DOI
  78. [89]Ricciardi Celsi L Zomaya AY: Perspectives on Managing AI Ethics in the Digital Age. Inform. Apr. 2025; 16(4). 10.3390/INFO16040318 · DOI
  79. [90]Sankaran S: Enhancing Trust Through Standards: A Comparative Risk-Impact Framework for Aligning ISO AI Standards with Global Ethical and Regulatory Contexts. ArXiv. 2025; abs/2504.1. 10.48550/ARXIV.2504.16139 · DOI
  80. [91]Hogan L Lasek-Markey M: Towards a Human Rights-Based Approach to Ethical AI Governance in Europe. Philosophies. Dec. 2024; 9(6). 10.3390/PHILOSOPHIES9060181 · DOI
  81. [92]Sharma D: Innovations and Future Directions in Securing Digital Environments. Int. J. Sci. Res. Eng. Manag. Apr. 2025; 09(04): 1– 9. 10.55041/IJSREM45950 · DOI
  82. [93]Cherukupalle NS: Quantum-Resilient Cloud Systems: Preemptive Shielding Against Post-Quantum Cryptographic Threats. J. Inf. Syst. Eng. Manag. Feb. 2025; 10(38s): 1234– 1246. 10.52783/JISEM.V10I38S.8781 · DOI
  83. [94]Aydeger A Zeydan E Yadav AK: Towards a Quantum-Resilient Future: Strategies for Transitioning to Post-Quantum Cryptography. 2024 15th Int. Conf. Netw. Futur. 2024; pp. 195– 203. 10.1109/NOF62948.2024.10741441 · DOI
  84. [95]Polu OR: AI-Driven Detection of Adversarial Attacks in Post- Quantum Cryptographic Systems. Int. J. Sci. Res. Mar. 2025; 14(3): 62– 66. 10.21275/SR25302093317 · DOI
  85. [96]Maharajan DK Mugeshwaran K Nithish D: Threat Intelligence Platform Empowered by Generative Ai with Quantum-Security. Int. Res. J. Adv. Eng. Hub. Apr. 2025; 3(04): 1336– 1342. 10.47392/IRJAEH.2025.0190 · DOI
  86. [97]Samunnisa K Gaddam SVK Madhavi K: Design and Evaluation of a Quantum-Resilient Cryptographic Framework for Enhancing Security and Efficiency in Distributed Cloud Environments. Int. J. Electr. Electron. Eng. Jul. 2024; 11(7): 1– 22. 10.14445/23488379/IJEEE-V11I7P101 · DOI
  87. [98]Siddiqui HA Khan A Shaikh S: Unveiling Barriers and Enablers: A Study on AI Adoption in Business Management. J. Soc. & Organ. Matters. Mar. 2025; 4(1): 193– 209. 10.56976/JSOM.V4I1.179 · DOI
  88. [99]Loevenich JF: Training Autonomous Cyber Defense Agents: Challenges & Opportunities in Military Networks. MILCOM 2024-2024 IEEE Mil. Commun. Conf. 2024; pp. 158– 163. 10.1109/MILCOM61039.2024.10773923 · DOI
  89. [100]Habler I Huang K Narajala VS: Building A Secure Agentic AI Application Leveraging A2A Protocol. ArXiv. 2025; abs/2504.1. 10.48550/ARXIV.2504.16902 · DOI
  90. [101]Ugo GC Apata AC Dawodu PO: A Multi-Stakeholder Perspective on the Limitations of Implementing Artificial Intelligence in Highway Transport. J. Eng. Res. Reports. 2024; 26(2): 243– 249. 10.9734/JERR/2024/V26I21086 · DOI
  91. [102]Adnan HS Shidani A Clifton L: Implementation Framework for AI Deployment at Scale in Healthcare Systems. SSRN Electron. J. 2023. 10.2139/SSRN.4465877 PMC12083986 40384926 · DOI
  92. [103]Kandasamy S: Control Plane as a Tool: A Scalable Design Pattern for Agentic AI Systems. 2025. 10.48550/ARXIV.2503.23037 · DOI
  93. [104]Ajiboye KJ: Ensuring data security and compliance in AI-powered business applications. Glob. J. Eng. Technol. Adv. Apr. 2023; 15(1): 125– 142. 10.30574/GJETA.2023.15.1.0067 · DOI
  94. [105]Mbah GO Nkechi A: AI-powered cybersecurity: Strategic approaches to mitigate risk and safeguard data privacy. World J. Adv. Res. Rev. Dec. 2024; 24(3): 310– 327. 10.30574/WJARR.2024.24.3.3695 · DOI
  95. [106]Daraojimba DO Adewusi AO Okoli U: Artificial intelligence in cybersecurity: Protecting national infrastructure: A USA review. World J. Adv. Res. Rev. Jan. 2024; 21(1): 2263– 2275. 10.30574/WJARR.2024.21.1.0313 · DOI
  96. [107]Al Siam A Alazab M Awajan A: A Comprehensive Review of AI’s Current Impact and Future Prospects in Cybersecurity. IEEE Access. 2025; 13: 14029– 14050. 10.1109/ACCESS.2025.3528114 · DOI
  97. [108]Bin Akhtar Z Rawol AT: Enhancing Cybersecurity through AI-Powered Security Mechanisms. IT J. Res. Dev. Oct. 2024; 9(1): 50– 67. 10.25299/ITJRD.2024.16852 · DOI
  98. [109]Aryal S: Leveraging Multi-AI Agents for Cross-Domain Knowledge Discovery. ArXiv. 2024; abs/2404.0. 10.48550/ARXIV.2404.08511 · DOI
  99. [110]Graham CM: AI skills in cybersecurity: global job trends analysis. Inf. & Comput. Secur. 2025. 10.1108/ICS-09-2024-0235 · DOI
  100. [111]Kour R Karim R Dersin P: Cybersecurity for Industry 5.0: trends and gaps. Front. Comp. Sci. 2024; 6. 10.3389/FCOMP.2024.1434436/PDF · DOI
  101. [112]Jaiswal A Mishra PC: ARTIFICIAL INTELLIGENCE (AI) AND CYBERSECURITY LAW: LEGAL ISSUES IN AI-DRIVEN CYBER DEFENSE AND OFFENSE. ShodhKosh J. Vis. Perform. Arts. Jun. 2024; 5(6). 10.29121/SHODHKOSH.V5.I6.2024.4144 · DOI
  102. [113]Wang WC: Legal, Policy, and Compliance Issues in Using AI for Security: Using Taiwan’s Cybersecurity Management Act and Penetration Testing as Examples. 2024 16th Int. Conf. Cyber Confl. Over Horiz. 2024; pp. 161– 176. 10.23919/CYCON62501.2024.10685586 · DOI
  103. [114]Habler I Huang K Narajala VS: Building A Secure Agentic AI Application Leveraging A2A Protocol. ArXiv. 2024; abs/2504.1. 10.48550/ARXIV.2504.16902 · DOI
  104. [115]Sengupta A: Securing the Autonomous Future A Comprehensive Analysis of Security Challenges and Mitigation Strategies for AI Agents. Int. J. Sci. Res. Eng. Manag. Dec. 2024; 08(12): 1– 2. 10.55041/IJSREM40091 · DOI
  105. [116]Alnaffar A: Cybersecurity Resilience, Cryptocurrency, and AI: Navigating the Risks in the Middle East. Int. J. Sci. Res. Mar. 2024; 13(3): 240– 241. 10.21275/SR24305132807 · DOI
  106. [117]Oloyede J: Ethical Reflections on AI for Cybersecurity: Building Trust. SSRN Electron. J. 2024. 10.2139/SSRN.4733563 · DOI
  107. [118]Shanker B Neyigapula: Ethical Considerations in AI Development: Balancing Autonomy and Accountability. J. Adv. Artif. Intell. 2024. 10.18178/JAAI.2024.2.1.138-148 · DOI
  108. [119]Pujari T Goel A Kejriwal D: Ethical and Responsible AI in the Age of Adversarial Diffusion Models: Challenges, Risks, and Mitigation Strategies. Int. J. Sci. Technol. Dec. 2022; 1(3): 54– 68. 10.56127/IJST.V1I3.1963 · DOI
  109. [120]Bahangulu JK Owusu-Berko L: Algorithmic bias, data ethics, and governance: Ensuring fairness, transparency and compliance in AI-powered business analytics applications. World J. Adv. Res. Rev. Feb. 2025; 25(2): 1746– 1763. 10.30574/WJARR.2025.25.2.0571 · DOI
  110. [121]Ahmad MN: Narrative Literature Reviews in Scientific Research: Pros and Cons. Jordan J. Agric. Sci. Mar. 2025; 21(1): 1– 4. 10.35516/JJAS.V21I1.4143 · DOI
  111. [122]Faisal A Cupiadi H: Cybersecurity in Digital Supply Chains: A Narrative Review of Threats and Strategic Frameworks for Sustainable Logistics. Sinergi Int. J. Logist. Aug. 2024; 2(3): 174– 186. 10.61194/SIJL.V2I3.728 · DOI
  112. [123]Malatji M Tolah A: Artificial intelligence (AI) cybersecurity dimensions: a comprehensive framework for understanding adversarial and offensive AI. AI Ethics. Apr. 2025; 5(2): 883– 910. 10.1007/S43681-024-00427-4 · DOI
  113. [124]Kodete CS Thuraka B Pasupuleti V: A Systematic Review of AI-Driven and Quantum-Resistant Security Solutions for Cyber-Physical Systems: Blockchain, Federated Learning, and Emerging Technologies. 2024 Int. Conf. Comput. Appl. 2024; pp. 1– 6. 10.1109/ICCA62237.2024.10927942 · DOI
  114. [125]Piplai A Kotal A Mohseni S: Knowledge-Enhanced Neurosymbolic Artificial Intelligence for Cybersecurity and Privacy. IEEE Internet Comput. Sep. 2023; 27(5): 43– 48. 10.1109/MIC.2023.3299435 · DOI
  115. [126]Yu W Zhao J: Quantum Multi-Agent Reinforcement Learning as an Emerging AI Technology: A Survey and Future Directions. 2023 Int. Conf. Comput. Appl. 2023; pp. 1– 7. 10.1109/ICCA59364.2023.10401605 · DOI
  116. [127]Fazil AW Hakimi M Shahidzay AK: A COMPREHENSIVE REVIEW OF BIAS IN AI ALGORITHMS. Nusant. Hasana J. Jan. 2024; 3(8): 1– 11. 10.59003/NHJ.V3I8.1052 · DOI
  117. [128]Shah Z: ETHICAL CONSIDERATIONS IN THE USE OF AI FOR ACADEMIC RESEARCH AND SCIENTIFIC DISCOVERY: A NARRATIVE REVIEW. Insights-Journal Life Soc. Sci. Apr. 2025; 3(2): 183– 189. 10.71000/JFESGV69 · DOI
  118. [129]Murphy A Bowen K El Naqa IM: Bridging Health Disparities in the Data-Driven World of Artificial Intelligence: A Narrative Review. J. Racial Ethn. Health Disparities. 2024; 12: 2367– 2379. 10.1007/S40615-024-02057-2 38955956 · DOI
  119. [130]Bellini V: Artificial intelligence and anesthesia: a narrative review. Ann. Transl. Med. May 2022; 10(9): 528– 528. 10.21037/ATM-21-7031 35928743 PMC9347047 · DOI
  120. [131]Elamin MOI Ismaiel OMO: The AI Revolution in Cybersecurity: Transforming Threat Detection, Defense Mechanisms, and Risk Management in the Digital Era. Int. J. Relig. Feb. 2025; 6(1): 228– 246. 10.61707/1DMVN671 · DOI
  121. [132]Garcia AB Babiceanu RF Seker R: Artificial Intelligence and Machine Learning Approaches For Aviation Cybersecurity: An Overview. 2021 Integr. Commun. Navig. Surveill. Conf. Apr. 2021; vol. 2021-April: pp. 1– 8. 10.1109/ICNS52807.2021.9441594 · DOI
  122. [133]Papagiannopoulos I: Enhancing Green Financing Through AI Analytics and Cross-Domain Data Sharing. 2024 15th Int. Conf. Information, Intell. Syst. Appl. 2024; pp. 1– 11. 10.1109/IISA62523.2024.10786617 · DOI
  123. [134]Jabbar H Al-Janabi S Syms F: AI-Integrated Cyber Security Risk Management Framework for IT Projects. 2024 Int. Jordanian Cybersecurity Conf. 2024; pp. 76– 81. 10.1109/IJCC64742.2024.10847294 · DOI
  124. [135]Shoetan PO Amoo OO Okafor ES: SYNTHESIZING AI’S IMPACT ON CYBERSECURITY IN TELECOMMUNICATIONS: A CONCEPTUAL FRAMEWORK. Comput. Sci. & IT Res. J. Mar. 2024; 5(3): 594– 605. 10.51594/CSITRJ.V5I3.908 · DOI

Cited by

Sorted by citations

Authors
ADABARA, I., Olaniyi Sadiq, B., Nuhu Shuaibu, A., Ibarahim Danjuma, Y., & Venkateswarlu, M.
Affiliations
Kampala International University (UG)
Corresponding author
ADABARA, IBRAHIM
ORCID
Venue
F1000Research
Publisher
F1000 Research Ltd
Published
2025-09-01
Volume
14
Pages
843
ISSN
2046-1402 (ISSN-L)
DOI
10.12688/f1000research.169337.1
PMC
PMC12569510
OpenAlex
W4413880655
Cited by
34
References
8
Citations by year
2026: 32 · 2025: 2
FWCI
4.90
Type
journal article
Language
en
Open access
Licence
Locations
  • F1000Research (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
None
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
Keywords
agentic AI, cybersecurity, cognitive autonomy, ethical AI governance, post-quantum security, neuromorphic architectures, anomaly detection, dual-use risks, autonomous decision-making
MeSH terms
Artificial Intelligence, Humans, Computer Security

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