How Claude Detects and Bans Accounts Using VPNs and Unsupported Passports

GISDAYS 2026: IDM Implementation, VOC and SIEM Strategies, AI Security Platforms Take Center Stage

The third business day of GISDAYS 2026 focused on practical cybersecurity scenarios for businesses and critical infrastructure. Experts from Gazinformservice, Security Vision, R-Vision, Xello, Solar, and Positive Technologies presented on vulnerability management, identity and access management, and protecting AI systems. Key discussions covered the role of VOC as a complement to SOC, challenges in deploying Ankey IDM, and using indicators of compromise to improve SIEM efficiency. Speakers also addressed data leakage paths into the dark web and the need for Zero Trust models in distributed environments. The event concluded with the IB-Prophets panel forecasting trends in digital identity protection, multi-agent AI systems, and AI model security standards.

Noise in Embeddings Fails to Protect Data: 8 Schemes Tested, ~90% Documents Identified with Just 100 Known Pairs

A detailed evaluation of eight embedding protection schemes shows that adding noise does not prevent document identification attacks when an adversary knows even a small number of document-embedding pairs. The study tested Gaussian noise, secret rotations combined with noise, SAP/DCPE, binary LSH, random projections, product quantization, secret nonlinearities, and k-anonymous quantization on BEIR collections SciFact and NFCorpus using all-MiniLM-L6-v2 and bge-small-en-v1.5 models. Even when noise levels reduced retrieval quality by half, identification rates remained above 98% for most noise-based methods. With only 100 known pairs (roughly 2% of the corpus), linear alignment attacks recovered around 90% of mappings for several schemes. Product quantization offered better protection but required significant quality loss to bring identification below 50%. The findings highlight that protections evaluated only against text reconstruction attacks can still leak membership information in realistic partial-leak scenarios.

Logical Argument Claims Full Alignment of Superintelligent AGI Is Fundamentally Impossible

A detailed analysis argues that the AI alignment problem cannot be solved for systems exceeding human cognitive capabilities. The paper presents a formal proof showing that any control mechanism created by humans will inevitably be circumvented by a more intelligent agent. It draws on foundational theorems from logic and cybernetics to support the conclusion that alignment is not an engineering challenge but a logical impossibility. Recent 2025-2026 research papers reach similar conclusions using computability theory and diagonal arguments. The work concludes that the only way to guarantee alignment is to avoid creating superintelligence altogether. All proposed restrictions are described as temporary delays rather than permanent solutions.