# AI w cyberbezpieczeństwie

EN: **AI for cybersecurity** · id `cyberai` · aliases: AI in cybersecurity, AI for cyber defence, security AI, LLM4Security, AI w cyberobronie, sztuczna inteligencja w bezpieczeństwie IT

Path: [AI Atlas](https://aiatlasgraph.com/llms.txt) › AI w cyberbezpieczeństwie

## Description

- PL: Uczenie maszynowe i modele językowe w obronie systemów IT: wykrywanie włamań i malware, szukanie podatności, praca SOC oraz ataki wspomagane przez AI.
- EN: Machine learning and language models in defending IT systems: intrusion and malware detection, vulnerability discovery, SOC work and AI-assisted attacks.

## In one sentence

- PL: Programy, które pomagają pilnować komputerów i sieci: wyłapują włamania, wirusy i dziury w oprogramowaniu, zanim wykorzysta je przestępca.
- EN: Software that helps guard computers and networks, catching break-ins, viruses and software holes before criminals use them.

## Problem it solves

- PL: Ataków jest tak dużo, a specjalistów tak mało, że ludzie nie nadążają czytać alarmów ani łatać dziur w kodzie.
- EN: There are so many attacks and so few specialists that people can't keep up with the alarms or patch holes in code.

## What it does

- PL: Jak strażnik z setką kamer, który nie męczy się w nocy: przegląda wszystko naraz, a ludziom zostawia decyzje w trudnych sprawach.
- EN: Like a guard watching a hundred cameras who never tires at night: scans everything at once and leaves the hard calls to people.

## Everyday example

- PL: Pocztowy filtr odsyła do kwarantanny fałszywy mail z „fakturą” od rzekomego kontrahenta, zanim ktokolwiek w biurze go otworzy.
- EN: The mail filter quarantines a fake email with an 'invoice' from a supposed supplier before anyone in the office opens it.

## This concerns you if you…

- PL: dostajesz podejrzane maile lub esemesy
- PL: odpowiadasz za komputery w firmie lub szkole
- PL: piszesz oprogramowanie używane przez innych
- EN: get suspicious emails or text messages
- EN: look after computers at a company or school
- EN: write software that other people use

## Keywords

LLM4Security · intrusion detection · malware classification · vulnerability detection · AI-assisted fuzzing · AIxCC · SOC automation · alert triage · cyber threat intelligence · MITRE ATT&CK · phishing detection · penetration testing agents · Cybench · EMBER

## How it works

- PL: Klasyczne podejścia to klasyfikatory i detektory anomalii na ruchu sieciowym, logach i plikach wykonywalnych. Od kilku lat modele językowe i agenci z dostępem do narzędzi czytają kod, generują testy fuzzingowe, analizują alerty i raporty o zagrożeniach, a w eksperymentach samodzielnie wykonują etapy testów penetracyjnych.
- EN: Classic approaches are classifiers and anomaly detectors on network traffic, logs and executables. More recently, language models and tool-using agents read code, generate fuzz targets, investigate alerts and threat reports, and in experiments carry out stages of penetration tests on their own.

## Limits

- PL: Przeciwnik aktywnie się dostosowuje, więc modele szybko się starzeją. Dane są poufne, słabo oznaczone i niezbalansowane, a fałszywe alarmy kosztują uwagę analityków. Te same zdolności pomagają atakującym, a wyniki z publikacji często zawyżają skuteczność wdrożeń.
- EN: The adversary adapts, so models age quickly. Data are confidential, poorly labelled and imbalanced, and false alarms drain analyst attention. The same capabilities help attackers, and published results often overstate real-world effectiveness.

## Key terms

- PL: **SOC**: Zespół, który całodobowo monitoruje alarmy bezpieczeństwa w organizacji.
- PL: **podatność**: Błąd w oprogramowaniu, który atakujący może wykorzystać.
- PL: **malware**: Złośliwe oprogramowanie: wirusy, trojany, ransomware.
- PL: **podwójne zastosowanie**: Ta sama technika służy obrońcom i atakującym.
- EN: **SOC**: A team that monitors an organisation's security alerts around the clock.
- EN: **vulnerability**: A software flaw an attacker can exploit.
- EN: **malware**: Malicious software: viruses, trojans, ransomware.
- EN: **dual use**: The same technique serves defenders and attackers.

## Know first

- [Wykrywanie anomalii i nowości (Outlier and novelty detection)](https://aiatlasgraph.com/t/anomalydetection.md)
- [Agenci i orkiestracja (Agents and orchestration)](https://aiatlasgraph.com/t/agentsnode.md)

## Subtopics (6 direct, 32 in total)

- [AI po stronie atakującego i testy penetracyjne (Offensive AI and AI-assisted penetration testing)](https://aiatlasgraph.com/t/offensiveai.md): Using models and agents for reconnaissance, exploitation and social engineering, both in legitimate penetration tests and in real attacks.
  - [Agenci testów penetracyjnych (LLM penetration-testing agents)](https://aiatlasgraph.com/t/autopentest.md): Language agents planning and executing penetration-test steps: reconnaissance, scanning, exploitation and reporting, supervised by a tester.
  - [Ataki prowadzone z pomocą AI (AI-enabled and AI-orchestrated cyberattacks)](https://aiatlasgraph.com/t/aiattacks.md): Use of models by criminals and state groups: from writing convincing phishing to agents carrying out most stages of an intrusion.
- [AI w zespole reagowania (SOC) (AI for security operations (SOC))](https://aiatlasgraph.com/t/secopsai.md): Language models and agents supporting security analysts: alert triage, investigation, log analysis and threat-report analysis.
  - [Analiza raportów o zagrożeniach (CTI) (Cyber threat intelligence analysis with language models)](https://aiatlasgraph.com/t/ctianalysis.md): Language models extracting attack techniques, indicators of compromise and links to vulnerabilities from reports and bulletins in structured form.
    - [Mapowanie opisów ataków na techniki MITRE ATT&CK (Automated mapping of threat text to ATT&CK techniques)](https://aiatlasgraph.com/t/ttpmapping.md): Classifying sentences and threat reports into techniques and tactics from the MITRE ATT&CK catalogue, a multi-label text classification task.
  - [Generowanie reguł detekcji z raportów o zagrożeniach (LLM-assisted detection rule generation)](https://aiatlasgraph.com/t/detectionrulegen.md): Using language models to turn attack descriptions and indicators from reports into detection rules in formats such as Sigma or YARA.
  - [Triaż alertów i agenci śledczy (AI alert triage and investigation agents)](https://aiatlasgraph.com/t/alerttriage.md): Language agents that gather evidence from security tools for each alert, assess its credibility and prepare a justified verdict.
- [Analiza programów binarnych i inżynieria wsteczna z AI (AI for binary analysis and reverse engineering)](https://aiatlasgraph.com/t/binaryai.md): Models trained on machine code and disassembly output that support decompilation, name recovery, similar-function search and malware analysis without source code.
  - [Dekompilacja z użyciem modeli językowych (Neural and LLM-based decompilation)](https://aiatlasgraph.com/t/llmdecompile.md): Translating machine code, or a classic decompiler's output, into readable, compilable source code with language models.
  - [Odzyskiwanie nazw zmiennych w zdekompilowanym kodzie (Neural variable-name recovery in decompiled code)](https://aiatlasgraph.com/t/binsymbolrecovery.md): Models learn to restore meaningful variable names lost during compilation by using lexical and structural context from decompiled code.
  - [Wyszukiwanie podobnych funkcji w kodzie binarnym (Binary code similarity detection)](https://aiatlasgraph.com/t/binsimilarity.md): Learning embeddings of binary functions so the same code compiled with different compilers, options or architectures lies close together.
- [Szukanie i łatanie podatności z AI (AI-assisted vulnerability discovery and repair)](https://aiatlasgraph.com/t/vulndiscovery.md): Language models and agents that generate fuzz tests, read code for security bugs and propose fixes.
  - [Bezpieczeństwo kodu pisanego przez AI (Security of AI-generated code)](https://aiatlasgraph.com/t/aicodesecurity.md): Measuring and reducing vulnerabilities in code produced by coding assistants and agents, via tests, benchmarks and secure-generation techniques.
  - [Fuzzing wspomagany modelami językowymi (LLM-assisted fuzzing)](https://aiatlasgraph.com/t/llmfuzzing.md): Language models write and fix fuzzing harnesses and classify crashes, raising code coverage without manual work.
    - [Generowanie sterowników fuzzingu przez modele językowe (LLM-based fuzz driver generation)](https://aiatlasgraph.com/t/fuzzdrivergen.md): Language models automatically writing the glue code that connects a fuzzer to a library's functions so random inputs reach deep into the tested code.
  - [Systemy automatycznego znajdowania i łatania błędów (Cyber reasoning systems for automated find-and-patch)](https://aiatlasgraph.com/t/cyberreasoning.md): Autonomous systems combining fuzzing, static analysis and language models that find a vulnerability, confirm it and generate a tested patch.
    - [Generowanie łatek na podatności przez modele językowe (LLM-based vulnerability patch generation)](https://aiatlasgraph.com/t/vulnpatchgen.md): Generative models propose code changes intended to repair a known vulnerability. A candidate requires functional testing and security evaluation before use.
  - [Wykrywanie podatności w kodzie przez modele językowe (Vulnerability detection in source code with language models)](https://aiatlasgraph.com/t/llmvulndetect.md): Language models and agents analysing source code and changes to flag security bugs and variants of known vulnerabilities.
- [Wykrywanie zagrożeń i włamań (Threat and intrusion detection)](https://aiatlasgraph.com/t/threatdetection.md): Models that detect attacks in network traffic, system logs and email: classifying known patterns and flagging departures from normal behaviour.
  - [Analiza zachowań użytkowników i zagrożenia wewnętrzne (UEBA) (User and entity behaviour analytics (UEBA) and insider threat detection)](https://aiatlasgraph.com/t/ueba.md): Modelling the typical behaviour of accounts, users and devices in an organisation and detecting deviations that suggest a hijacked account or a malicious insider.
  - [Wykrywanie anomalii w logach systemów (Log anomaly detection)](https://aiatlasgraph.com/t/loganomaly.md): Learning models of event sequences from system and application logs to detect unusual runs that signal failures or attacks.
    - [Parsowanie logów do szablonów zdarzeń (Automated log parsing)](https://aiatlasgraph.com/t/logparsing.md): Automatically splitting raw log messages into a fixed event template and variable parameters, as the first step in anomaly detection and diagnostics.
  - [Wykrywanie ataków na grafach pochodzenia zdarzeń systemowych (Provenance-graph-based intrusion detection)](https://aiatlasgraph.com/t/provenanceids.md): Detecting long-running stealthy attacks by analysing dependency graphs between processes, files and network connections in an operating system.
  - [Wykrywanie losowo generowanych domen (DGA) (Detection of algorithmically generated domains (DGA detection))](https://aiatlasgraph.com/t/dgadetect.md): Classifying domain names as algorithmically generated by malware to reach its command server, often from the character string alone.
  - [Wykrywanie phishingu (Phishing detection)](https://aiatlasgraph.com/t/phishingdetect.md): Classifying messages, URLs and webpages as phishing from their content, URL representation and visual similarity to legitimate brands.
  - [Wykrywanie włamań w ruchu sieciowym (Machine-learning network intrusion detection)](https://aiatlasgraph.com/t/networkids.md): Classifiers and anomaly detectors trained on network flows and packets, separating normal traffic from attacks.
- [Wykrywanie złośliwego oprogramowania (Machine-learning malware detection)](https://aiatlasgraph.com/t/malwareml.md): Classifying files and program behaviour as malicious or benign from static and dynamic features.
  - [Omijanie detektorów malware przykładami adwersarialnymi (Adversarial evasion of malware detectors)](https://aiatlasgraph.com/t/advmalware.md): Modifying real malicious programs so they keep working while a classifier judges them benign, and defending against such attacks.
  - [Starzenie się detektorów i realistyczna ocena w czasie (Concept drift and time-aware evaluation of malware detectors)](https://aiatlasgraph.com/t/malwaredrift.md): Methods for evaluating and maintaining malware detectors that respect data chronology, realistic class ratios and performance decay over time.
  - [Statyczna klasyfikacja plików wykonywalnych (Static malware classification of executables)](https://aiatlasgraph.com/t/staticmalware.md): Classifying executables (e.g. Windows PE files) as malicious or benign from features extracted without running the program.
  - [Wykrywanie malware po zachowaniu w piaskownicy (Dynamic (behavioural) malware detection)](https://aiatlasgraph.com/t/dynmalware.md): Classifying programs from traces of their execution in an isolated environment, most often sequences of system API calls with their arguments.

## Related topics

Untyped, symmetric "see also" links.

- [Bezpieczeństwo i governance (AI safety, security and governance)](https://aiatlasgraph.com/t/safety.md)

## Sources

- [Large Language Models for Cyber Security: A Systematic Literature Review](<https://arxiv.org/abs/2405.04760>) `xu-llm4security-survey-2024` · by Hanxiang Xu, Shenao Wang, Ningke Li, Kailong Wang, Yanjie Zhao, Kai Chen, Ting Yu, Yang Liu, Haoyu Wang
- [A Survey of Agentic AI and Cybersecurity: Challenges, Opportunities and Use-case Prototypes](<https://arxiv.org/abs/2601.05293>) `lazer-agentic-ai-cybersecurity-2026` · by Sahaya Jestus Lazer, Kshitiz Aryal, Maanak Gupta, Elisa Bertino
- [Dos and Don'ts of Machine Learning in Computer Security](<https://arxiv.org/abs/2010.09470>) `arp-dos-donts-ml-security-2020` · by Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, Konrad Rieck

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AI Atlas revision 122 (2026-10-30) · HTML: https://aiatlasgraph.com/t/cyberai · index: https://aiatlasgraph.com/llms.txt · everything: https://aiatlasgraph.com/llms-full.txt · updated 2026-10-30