The article addresses the problem of cybersecurity in intellectual information aggregation (IIA) processes within digital archives, which arise during the automated collection, structuring, semantic enrichment, and analysis of heterogeneous data using artificial intelligence (AI), machine learning (ML), and large language models (LLMs). The study focuses on identifying vulnerabilities of IIA processes and their mathematical formalization across stages such as digitization, image processing, optical character recognition (OCR), classification, indexing, and archival system creation. Particular attention is given to formalizing cyber threats, including unauthorized access, integrity violations, metadata forgery, adversarial attacks on AI/ML models, data manipulation, prompt injection, data exfiltration, and digital signature forgery. For each threat category, mathematically grounded countermeasures are proposed, including encryption, multi‑factor authentication, monitoring, anomaly detection, access control, metadata protection, and adversarial training. The paper emphasizes the emergent properties of combined defenses, highlighting the resilience of digital archives against cyber th
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