± SCIENCE Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exist…+ SCIENCE Deep learning takes on protein folding In 1972, Anfinsen won a Nobel prize for demonstrating a connection bet…± SCIENCE Significance Deep neural networks are currently the most successful machine-learning technique for solving a…+ SCIENCE Deep learning allows computational models that are composed of multiple processing layers to learn representa…+ HEALTH For the task of analyzing survival data to derive risk factors associated with mortality, physicians, researc…+ SCIENCE Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer…+ SCIENCE Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech rec…+ SCIENCE Current deep-learning models are mostly built upon neural networks, i.e. multiple layers of parameterized dif…
TruaceTracing the truth around AIMonday, July 13, 2026
Subscribe
TRV-2026-0199Version 1 · Certified

Written 2026-07-13 21:41:18 UTC · current record

Reason for this version

Certified into the record

Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-0199
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-13T21:41:18.284362Z
status: published
lens: trace
sector: crime
headline: The development of cyber threats related to the use of AI
dek: The rapid development of artificial intelligence (AI) means that its role in cyberspace is also growing, both in terms of threats and defence against them. AI supports the automation of anomaly detection, data analysis, and incident response, which enhances protection efficiency. However, cybercriminals use AI-based solutions to create sophisticated attack tools, such as advanced phishing schemes, deepfakes, and hard-to-detect malware. The author analyses the role of AI in generating cyber threats and evaluates…
gain_title: AI-based defense automates anomaly detection, data analysis and incident response to improve protection efficiency in cyberspace.
problem_title: Cybercriminals use AI to create sophisticated attack tools including advanced phishing, deepfakes and hard-to-detect malware.
trace_subject: use of AI in cybersecurity for defense and offense
gain_reading: AI-based defense automates anomaly detection, data analysis and incident response to improve protection efficiency in cyberspace.
gain_evidence: AI supports the automation of anomaly detection, data analysis, and incident response | enhances protection efficiency
problem_reading: Cybercriminals use AI to create sophisticated attack tools including advanced phishing, deepfakes and hard-to-detect malware.
problem_evidence: cybercriminals use AI-based solutions to create sophisticated attack tools | advanced phishing schemes, deepfakes, and hard-to-detect malware
quick_read: By April 2026, a peer-reviewed analysis described AI's growing dual role in cyberspace, where it automates anomaly detection, data analysis and incident response to enhance protection, while also enabling cybercriminals to build advanced phishing, deepfakes and hard-to-detect malware.

The dual-use pattern matters because efficiency gains for defenders are offset by more sophisticated, scalable attack tools, raising the stakes for detection and response and leaving open questions about effective regulation and cross-border cooperation that the article flags as needed.
limitation: 
tag: Automated dual reading
key_points: AI is used defensively to automate anomaly detection, data analysis and incident response. | Cybercriminals use AI to build advanced phishing schemes, deepfakes and hard-to-detect malware. | Author evaluates defensive strategies and stresses need for international cooperation and legal regulation for AI in cybersecurity.
rundown: The source describes a dual-use dynamic where AI is applied to automate security operations while also being weaponized by threat actors to produce more convincing and evasive attacks.

It frames the analysis as an evaluation of defensive strategies against AI-enabled threats and points to governance gaps, noting the need for international cooperation and legal regulation regarding the use of AI in cybersecurity.
sources:
- peer_reviewed | Przegląd Bezpieczeństwa Wewnętrznego | https://doi.org/10.4467/20801335pbw.26.016.23378 | 2026-04-14
prev: 0000000000000000000000000000000000000000000000000000000000000000
sha256
febfe5b5fecf6e90753e6c6478be2b2676b27bd80aea8fc2d8c4dbd103427db6
previous
0000000000000000000000000000000000000000000000000000000000000000
Verify this record
How to verify without trusting this page

Fetch the canonical text of any version from /api/record/TRV-2026-0199 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.