Challenges of AI in the security sector 2100-BW-L-D3SIBE

The module introduces students to the challenges to state security arising from the rapid development, deployment, and use of artificial intelligence (AI) systems. The course presents AI as a dual-use technology that can support threat analysis, risk management, anomaly detection, critical infrastructure protection, cybersecurity, and decision-making processes, while at the same time creating new legal, social, political, ethical, and military risks.

During the course, students examine definitions, typologies, and the basic operating mechanisms of contemporary AI systems, including machine-learning models, general-purpose AI models, generative AI, chatbots, and pattern-recognition systems. The module considers applications of AI in public administration and in institutions responsible for internal security, as well as the limitations of automation, the risk of errors, algorithmic bias, model opacity, and excessive reliance on automated recommendations.

An important element of the module is the analysis of regulatory frameworks and standards, in particular the AI Act, the Digital Services Act (DSA), national strategic documents, the NIST AI Risk Management Framework, and standards relating to AI systems that employ machine learning. Students relate current implementation cases, communications issued by public institutions, and media reports to requirements concerning risk management, the protection of fundamental rights, accountability, human oversight, and system security.

The course also covers the use of AI in cyberattacks and cyber defence, information operations, disinformation, the dissemination of synthetic media and deepfakes, social-engineering attacks, as well as military applications and autonomous systems. Students learn to assess sources critically, identify risks, and formulate well-founded positions on the use of AI by state institutions.
Student workload

Attendance at lectures 10h
Reading core and supplementary literature 5h
Analysis of legal acts, standards, strategic documents, and reports 4h
Independent consolidation of course material and preparation for problem-based discussions 3h
Preparation for the final test 2h
Preparation for the oral component of the examination 1h
Total student workload 25h

Number of ECTS credits: 1

Term 2025Z:

The module introduces students to contemporary state security challenges related to the dynamic development of artificial intelligence. The course focuses on a comprehensive approach to AI as a challenge for the state security sector, covering implementation, regulation, risk management, and the impact of AI on internal and external state security.

The module covers:

The specificity of artificial intelligence systems and the dynamics of their development – analysis of definitions and typologies of AI systems in the context of state security, evolution of AI technology and its impact on the security sector, characteristics of contemporary AI systems used in public administration, and management of AI system lifecycles.

Dimensions of artificial intelligence system utility in security management – utilisation of AI in risk management and security threat analysis, applications in internal security institutions, AI-based decision support systems in the public sector, automation of security processes, and predictive threat analysis.

AI models, algorithms, chatbots: types, functions, applications in the security sphere – overview of general-purpose AI models in the context of the AI Act, Large Language Models (LLMs) and their applications in security, chatbots and conversational systems in public services, pattern recognition systems, and machine learning in anomaly and threat detection.

Challenges and threats associated with artificial intelligence development – economic (impact on labour market, economic competitiveness), political (governance, regulations, state policy in AI), social (digital inequalities, privacy, data protection), military (autonomous systems, cyberwarfare, state defence), and threats to critical infrastructure and security systems.

Social engineering and media in the artificial intelligence era – manipulation and disinformation aided by AI, deepfakes and synthetic media as security threats, AI in social engineering and social engineering attacks, and protection against manipulation in social media.

Course coordinators

Term 2025Z:

Piotr Sosnowski

Term 2026Z:

Piotr Sosnowski
Jacek Ziółkowski

Type of course

obligatory courses

Mode

Classroom

Prerequisites (description)

The module is intended for first-cycle (undergraduate) students in the Internal Security programme. It assumes a basic understanding of state security, internal security, contemporary threats, and the functioning of public institutions, acquired during previous stages of study. Students are expected to have basic skills in using academic literature, public documents, and reliable online sources, as well as a willingness to develop their ability to analyse information and formulate well-founded conclusions relating to security. A general familiarity with cybersecurity, social media, information protection, and threats to state security will be beneficial. The module does not require programming knowledge, familiarity with methods for developing artificial intelligence models, or prior technical experience in the field of AI. Students are expected to prepare for classes systematically, participate actively in discussions, and undertake independent preparation for the final examination.

Learning outcomes

K_W03
The student identifies, characterises, and explains, at an advanced level, the fundamental problems, aspects, and dimensions of internal security, with particular emphasis on challenges arising from the development and use of artificial intelligence (AI) systems.

K_W07
The student analyses and explains, at an advanced level, threats to the functioning of the contemporary state and society, their transformations, consequences, and methods of counteracting them, including technological, informational, social, legal, and military threats related to AI.

K_U03
The student makes and optimises decisions concerning internal-security processes and solves typical and non-standard problems related to the assessment of the applications, risks, and limitations of AI systems.

K_U06
The student communicates competently and professionally with relevant stakeholders, using modern communication techniques, and presents information on AI-related threats and ways of mitigating their effects.

K_U07
The student independently expands, supplements, and improves their knowledge of AI and security, and applies it to solving professional and analytical problems.

K_K01
The student critically and consciously assesses the scope of their own knowledge and competences and applies the acquired knowledge to solving cognitive and practical problems in the field of internal security.

Assessment criteria

Course Assessment Rules

Final Assessment

The final grade is based entirely on the annual assignment, which accounts for 100% of the final mark.

The annual assignment consists of an independently prepared analytical paper on a selected issue related to the use of artificial intelligence in the security sector. It should demonstrate knowledge of key concepts, regulations, standards, applications, threats and AI risk-management mechanisms.

The paper should include an analysis of a specified case, document, risk or dilemma concerning the use of AI in the context of state security. The student should describe and analyse the issue, relate it to the relevant dimension of security — in particular state security, internal security, information security, cybersecurity, critical infrastructure protection or societal security — and formulate their own logically justified conclusions and recommendations.

The annual assignment should be prepared on the basis of reliable sources, in particular academic literature, legal acts, strategic documents, standards, reports issued by public institutions and analytical materials. Assessment will take into account substantive accuracy, the ability to analyse the issue, critical use of sources, the relevance of conclusions and recommendations, the structure of the paper, and linguistic and formal correctness.

The annual assignment accounts for 100% of the final grade. The minimum pass mark for the course is 60%.

Up to two absences are permitted. Each absence must be made up by submitting an independently prepared written assignment on a topic agreed with the lecturer. The assignment should be 3–6 pages long, formatted in Times New Roman, 12-point font, with 1.5 line spacing.
Permitted Use of AI

The permitted level of AI use in the preparation of written assignments is defined on a scale from 1 to 5. In accordance with Faculty Council Resolution No. 29/2025:

Level 1. NO AI

Verification of learning outcomes is carried out entirely without the assistance of artificial intelligence. Students rely exclusively on their own knowledge, understanding and skills.

Artificial intelligence may not be used at any stage of the assessment process.
Verification of Learning Outcomes
K_W03