MinTsifry Considers Annual 10 Billion Rubles Support Package for Russian AI Development
Russian AI developers may soon receive significant state backing as the Ministry of Digital Development discusses an annual support package reaching 10 billion rubles. The funds are intended to accelerate neural network development, finance pilot projects, and cover expenses related to computing power.
According to Kommersant, the package splits into 8 billion rubles allocated for technology development and deployment and 2 billion rubles earmarked for compensating computational costs. One proposed instrument involves subsidized loans issued through authorized banks, allowing companies to finance infrastructure, dataset collection and labeling, as well as model fine-tuning.
A second mechanism envisions grants for pilot testing of previously unseen solutions. These grants could cover up to 80 percent of project costs and would target priority economic sectors, social services, and public administration. The RFIT fund is being considered as a potential operator for project selection.
Both the Ministry and the office of Deputy Prime Minister Dmitry Grigorenko have confirmed that the initiative is under active discussion. However, specific parameters and implementation timelines have not yet been finalized, meaning companies cannot apply for funding at this stage.
Representatives of the market, including Sergey Golitsyn, head of AI projects at T1, believe the support could help smaller startups bring products to pilot stage. At the same time, participants stress the need for transparent selection criteria and clear reporting rules to avoid the emergence of intermediaries and to maintain competitive balance between independent teams and large corporations.
Related articles
PKI Storm: Managing 100,000 Simultaneous Certificate Requests in Kubernetes Recovery Scenarios
A large organization's PKI infrastructure faced a critical bottleneck when a data center outage triggered simultaneous startup of tens of thousands of Kubernetes pods, each requiring mTLS certificates. The existing setup using ESAUS and Citadel routed all requests through external certificate authorities that could only sustain 50-70 RPS against an incoming burst of 100,000 requests. Average daily load of 10-11 RPS had masked the thundering herd risk during mass recovery. Scaling the CA 15x was rejected due to cost and the fundamental dependency on real-time signing. The team introduced pre-issuance of certificates stored in a dedicated Unified Secret Storage (ЕХС) layer that supports 14,000 RPS reads while the CA continues normal operation. This architectural separation of issuance and consumption reduced recovery time from nearly 24 minutes to seconds while shifting focus to secure secret lifecycle management including KRA key protection.
Indid Reports Russian Identity Security Market Reaches 17 Billion Rubles Amid High Incident Rates
According to Indid, the Russian Identity Security market reached 17 billion rubles by the end of 2025. The assessment highlights that organizations continue to allocate significant budgets to access protection while account-related problems persist. Survey data shows that 87.5 percent of companies experienced incidents involving user accounts and access rights during the period. Identity Security solutions focus on managing digital identities, controlling permissions, and preventing unauthorized access across corporate systems. The findings indicate ongoing challenges in maintaining secure access despite growing investments in specialized tools and platforms.
How a Node.js Bridge Connects MAX and VK Messengers to Chatwoot with Secure Bidirectional Sync
A detailed technical case study describes building a lightweight Node.js service that links the MAX messenger and VK communities to Chatwoot without scraping or using personal accounts. The bridge uses official bot APIs and community callbacks, separate API inboxes, and persistent state stored in a Docker volume to maintain conversation mappings across restarts. Security measures include webhook secret validation, deduplication of events using ring buffers, SSRF protections when handling images, and strict filtering to prevent loops or private notes from leaking externally. The implementation covers contact and conversation creation via Chatwoot Application API, image transfer for VK, and graceful recovery after partial failures. Limitations such as lack of exactly-once delivery and absence of a durable queue are acknowledged, with recommendations for production use including SQLite, retries, and structured logging. The author provides configuration examples, health checks, and a capability matrix showing current support for text and media in each direction.
NtechLab AI Video Analytics Helps Locate 250 Missing Children in Novosibirsk Region
NtechLab has reported that its generative AI-powered video analytics platform assisted Russian law enforcement in finding 250 missing children in the Novosibirsk region in less than 18 months. The FindFace Multi system operates as part of the Safe City complex and processes live video feeds from cameras installed at transport hubs, streets, squares, and government buildings. Facial recognition capabilities for locating children became available to regional authorities in April 2025. The same technology has also been used to identify more than 3,000 offenders throughout 2025. NtechLab states that its solutions are deployed across more than 70 Russian regions and 34 countries, although the company provided no detailed breakdown of individual cases or average search times. All final decisions and physical searches remain the responsibility of human police officers.