Russian Firms Accumulate Digital Clutter: 35% of Corporate Files Unused for Years, Weak Passwords Expose Sensitive Data
Russian businesses continue to accumulate digital clutter while granting overly broad access rights across corporate storage systems. Garda, a company within IKS Holding, analyzed repositories belonging to more than 100 organizations and uncovered multiple serious concerns.
The dataset included over 157 TB of data and more than 511,000 user accounts. Approximately 35% of files had remained untouched for more than five years. Duplicate copies may account for another 35% of stored volume. In some sectors, up to one-third of the space was occupied by personal photographs, videos, archives, and other non-business content.
Paying to store this digital waste is only part of the problem. The larger the number of uncontrolled copies scattered across folders and resources, the harder it becomes to determine where confidential information resides and who can access it.
Access management practices also raised alarms. More than 33% of accounts used critically weak passwords or passwords that had not been rotated for a long time. Auditors discovered numerous contractor and temporary employee accounts inactive for over 90 days. In large organizations, direct permissions issued outside security groups were common. Folders with wide access rights often contained passport scans, client registries, database exports, and other sensitive documents.
Garda concludes that manual remediation of such volumes is no longer practical. The company advises organizations to regularly inventory and classify information, manage access centrally, and automate the full data lifecycle from creation to deletion.
Related articles
Amnezia VPN Survives Coordinated Russian Censorship Campaign Targeting AmneziaWG Protocol Fingerprints
Amnezia VPN has published a detailed post-mortem on the multi-wave blocking campaign conducted by Russian authorities against its Amnezia Free and Amnezia Premium services during June and July. The company describes a shift from simple protocol blocking to sophisticated fingerprinting of AmneziaWG traffic combined with infrastructure DDoS attacks and automated IP-subnet blacklisting. Engineers closed multiple detection vectors including zero-length UDP packets, fixed-size keepalive messages, handshake timing patterns, and nonce zero bytes. The incident forced accelerated migration to AmneziaWG 2.0, discontinuation of legacy client support, and development of AmneziaWG 3.0 while expanding VLESS infrastructure as a backup. Self-hosted users largely avoided direct protocol blocks but still faced subnet-level restrictions. The report highlights how Roskomnadzor now applies cumulative scoring across multiple traffic features rather than single definitive markers.
Data Masking: 8 Critical Questions Businesses and Developers Ask About Protecting Sensitive Data
Garda expert Dmitry Larin addresses common challenges in data masking during a recent webinar titled 'Data Masking: Battle of Opinions'. The discussion covers why masking remains essential even when encryption is deployed, how to preserve application functionality after anonymization, and the performance trade-offs of processing large databases such as 5 TB PostgreSQL instances. Different masking types including static, dynamic, selective, and streaming are explained with specific use cases for DevOps pipelines, external contractors, and BI systems. The article also examines why machine learning alone is insufficient for discovering personal data and why custom scripts fail at scale across heterogeneous environments like PostgreSQL and Oracle. Practical recommendations include combining masking with encryption, using deterministic transformations for deduplication, and separating replication from masking tasks to avoid production impact.
MAX Desktop Client Tested for VPN Detection on Windows, No Tracking Signs Found
A Habra user named Slava_B conducted an experiment on September 8, 2026, to determine whether the MAX desktop client on Windows could detect or route traffic through a VPN configured at the router level. The setup used a Keenetic router that directed Russian resources directly while sending other connections via an OpenConnect tunnel to a European VPS, with no VPN client or virtual adapter present in Windows itself. Monitoring tools including Process Monitor, Wireshark, TCPView, and tcpdump revealed that MAX.exe and MAX-service.exe processes communicate locally and connect to MAX/ONEME infrastructure along with AppTracer services. The application repeatedly accessed MachineGuid, computer name, proxy settings, device IDs, and microphone/camera information, though these reads may support diagnostics and anti-fraud functions. No connections appeared on the VPN interface, and the client did not attempt to reach IP-checking services, Telegram, or WhatsApp. The researcher noted that TLS traffic was not decrypted, so actual transmission of identifiers could not be confirmed, and results apply only to this router-based configuration.
PII-Guard: Open-Source Detector for Personal Data in Russian Text
Andrey Ivanov, an NLP researcher at red_mad_robot, has released PII-Guard, an open-source system that detects and masks personal data in Russian text before it reaches language models. The tool combines rule-based checks with a fine-tuned ruBert-base NER model to handle names, addresses, phones, passports, INN, SNILS, bank cards and other entities. It replaces detected PII with structured XML-like tags that preserve grammatical information such as gender and entity ID, allowing models to generate coherent responses that are later restored with real values. The hybrid pipeline first applies normalization, pattern matching, Luhn and weighted checksum validation, and context windows with positive and negative keywords, then merges results with model predictions via an arbitration module. Evaluation on four public datasets, including Hivetrace, alexen2 and alrosait, shows PII-Guard outperforming other open solutions on both strict span matching and type-overlap micro-F1 metrics. The project, including datasets and code, is available on GitHub and aims to reduce leakage risks while maintaining downstream model utility.