ARTIFICIAL INTELLIGENCE IN INTELLIGENCE AND SECURITY SERVICES: REGULATORY AND ETHICAL CHALLENGES OF BALANCING OPERATIONAL EFFICIENCY WITH DEMOCRATIC OVERSIGHT
DOI:
https://doi.org/10.54658/ps.28153324.2026.16.2.pp.22-32Keywords:
artificial intelligence in security, intelligence services regulation, democratic oversight, algorithmic surveillance, AI ethics in public security, national security governanceAbstract
Purpose. The article examines how democratic states can govern the use of artificial intelligence in intelligence and security services without undermining operational effectiveness. It addresses the tension between AI-enabled speed, scale, and pattern detection, on the one hand, and legality, accountability, privacy, and democratic oversight, on the other. Methods. The study applies qualitative comparative legal and policy analysis. It combines review of regulatory instruments, intelligence oversight models, and AI ethics literature with a structured comparison of the European Union, the United States, the United Kingdom, and Israel. The article also develops an original analytical model, the Security–Oversight Balance framework. Findings. AI is already reshaping intelligence work through open-source intelligence analysis, signals intelligence triage, biometric identification, predictive threat analytics, cyber intelligence, and decision-support tools. Existing governance regimes are uneven. The EU has adopted a rights-based risk model through the AI Act, but national security exemptions remain significant. The United States relies more heavily on executive policy, agency guidance, and national security memoranda. The United Kingdom has a mature investigatory powers regime but lacks a dedicated AI-specific intelligence statute. Israel demonstrates a security-driven and innovation-oriented model with more limited public transparency. Across all cases, the central risks are algorithmic bias, opacity, mission creep, weak auditability, and the displacement of responsibility from human decision-makers to technical systems. Conclusions. Democratic oversight of AI in intelligence should not be treated as an obstacle to security capacity. It is a condition of legitimate and sustainable security governance. The proposed Security–Oversight Balance framework recommends four linked safeguards: legal authorization, technical auditability, managerial accountability, and ethical proportionality. The article argues for classified but reviewable AI registers, mandatory human accountability for consequential decisions, independent technical audit capacity, and international minimum standards for AI use in security services.
Downloads
References
Amoore, L. (2020). Cloud ethics: Algorithms and the attributes of ourselves and others. Duke University Press.
Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732.
Brayne, S. (2017). Big data surveillance: The case of policing. American Sociological Review, 82(5), 977–1008. https://doi.org/10.1177/0003122417725865
Brayne, S. (2021). Predict and surveil: Data, discretion, and the future of policing. Oxford University Press.
Brayne, S., & Christin, A. (2021). Technologies of crime prediction: The reception of algorithms in policing and criminal courts. Social Problems, 68(3), 608–624.
Browne, T. O., Abedin, M., & Chowdhury, M. J. M. (2024). A systematic review on research utilising artificial intelligence for open source intelligence applications. International Journal of Information Security, 23(4), 2911–2938. https://doi.org/10.1007/s10207-024-00868-2
Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81, 77–91.
Calo, R., & Citron, D. K. (2021). The automated administrative state: A crisis of legitimacy. Emory Law Journal, 70(4), 797–846.
Citron, D. K., & Pasquale, F. (2014). The scored society: Due process for automated predictions. Washington Law Review, 89(1), 1–34.
Council of Europe. (2018). Protocol amending the Convention for the Protection of Individuals with regard to Automatic Processing of Personal Data. Council of Europe.
Council of Europe. (2024). Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law. Council of Europe.
Deibert, R. J. (2023). Subversion Inc.: The age of private spycraft. MIT Press.
European Parliament and Council of the European Union. (2016). Regulation (EU) 2016/679 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data. Official Journal of the European Union.
European Parliament and Council of the European Union. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence. Official Journal of the European Union.
Feldstein, S. (2021). The rise of digital repression: How technology is reshaping power, politics, and resistance. Oxford University Press.
Ferguson, A. G. (2017). The rise of big data policing: Surveillance, race, and the future of law enforcement. New York University Press.
Gill, P., & Phythian, M. (2018). Intelligence in an insecure world (3rd ed.). Polity Press.
Hildebrandt, M. (2015). Smart technologies and the end(s) of law: Novel entanglements of law and technology. Edward Elgar.
Investigatory Powers Act 2016, c. 25. (UK).
Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165(3), 633–705.
Leslie, D., Burr, C., Aitken, M., Katell, M., Briggs, M., & Rincon, C. (2022). Human rights, democracy, and the rule of law assurance framework for AI systems: A proposal. The Alan Turing Institute.
Lyon, D. (2018). The culture of surveillance: Watching as a way of life. Polity Press.
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1–21. https://doi.org/10.1177/2053951716679679
National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.100-1
North Atlantic Treaty Organization. (2021). Summary of the NATO Artificial Intelligence Strategy. NATO.
North Atlantic Treaty Organization. (2024). Summary of NATO’s revised Artificial Intelligence Strategy. NATO.
Office of the Director of National Intelligence. (2020a). Principles of artificial intelligence ethics for the intelligence community. ODNI.
Office of the Director of National Intelligence. (2020b). Artificial intelligence ethics framework for the intelligence community. ODNI.
Office of Management and Budget. (2025). Memorandum M-25-21: Accelerating federal use of AI through innovation, governance, and public trust. Executive Office of the President.
O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
Organisation for Economic Co-operation and Development. (2024). OECD AI principles. OECD.
Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
Privacy International. (2014). Who’s watching the watchers? A comparative study of intelligence organisations oversight mechanisms. Privacy International.
President of the United States. (2025). Executive Order 14179: Removing barriers to American leadership in artificial intelligence. The White House.
President of the United States. (2026). National Security Presidential Memorandum/NSPM-11: Artificial intelligence in the national security enterprise. The White House.
Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. Proceedings of the Conference on Fairness, Accountability, and Transparency, 59–68. https://doi.org/10.1145/3287560.3287598
Slobogin, C., & Brayne, S. (2023). Surveillance technologies and constitutional law. Annual Review of Criminology, 6, 219–240.
UNESCO. (2021). Recommendation on the ethics of artificial intelligence. UNESCO.
United Nations High Commissioner for Human Rights. (2021). The right to privacy in the digital age: Report of the United Nations High Commissioner for Human Rights (A/HRC/48/31). United Nations.
Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76–99. https://doi.org/10.1093/idpl/ipx005
White House. (2025). America’s AI Action Plan. Executive Office of the President.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Andrii Lyseiuk
This work is licensed under a Creative Commons Attribution 4.0 International License.
