AntiMalware•September 1, 2026•🇷🇺Translated from Russian

Selectel Launches Local AI Admin Agent aish in SELECTOS to Eliminate Cloud Data Risks

Selectel has released aish, an embedded generative AI agent integrated into its SELECTOS server operating system. The tool is designed to assist system administrators with incident analysis, root-cause identification, and routine maintenance tasks while keeping all data processing on local infrastructure.

Unlike most commercial AI assistants that rely on cloud-based models, aish runs entirely on Selectel servers. This architecture prevents logs, configuration files, and infrastructure details from being sent to third-party providers, addressing concerns over data leakage and auditability for organizations with strict security policies.

The agent understands system context by examining running services, logs, and configuration state. It can propose actions for troubleshooting service failures or performing standard operations. However, aish does not execute commands autonomously; it presents the intended command and its expected outcome for explicit approval by a human operator.

Selectel positions the solution as a way to reduce reliance on foreign cloud LLMs for teams prohibited from using them under internal security rules. The company highlights compliance with 152-FZ as a key advantage for Russian enterprises.

SELECTOS is distributed in ISO, QCOW2, and container image formats and runs on both cloud and dedicated servers. Because the distribution is based on Debian, existing Linux administrators require minimal retraining. Kirill Dmitriev, Director of the System Software Department at Selectel, noted that the agent was developed specifically to lower the barrier to entry for Linux system administration under restrictive data-protection regimes.

Related articles

Habr•AI Security

Debate on Cyber Risks of Open-Weight AI Models Is Fundamentally Flawed

An experienced commentator argues that the ongoing debate over cyber risks posed by open-weight AI models rests on flawed assumptions and risks leading to counterproductive policy decisions. The piece identifies three main camps: frontier labs and U.S. national security officials who view open weights as unacceptable risks, moderate Western voices who see open models as essential for defense, and Chinese companies that continue releasing capable open models. It criticizes reports such as Anthropic’s analysis of GLM-5.3 for failing to address broader ecosystem consequences of bans. Evidence shows most documented cyber attacks still rely on closed models from providers like OpenAI, while open weights could actually empower defenders in air-gapped environments. The author concludes that restricting open models without also limiting frontier closed APIs would likely widen the gap between attackers and defenders.

Securitylab•AI Security

Why AI Detectors Cannot Be Trusted: The Shift to Watermarks and C2PA Standards

Detecting AI-generated images by examining fingers, teeth, or text has become ineffective as modern generators now produce realistic hands, photographic simulations, and synthetic voices. Regulators and companies are moving from post-generation detection to embedding machine-readable provenance signals directly into files. The EU AI Act's Article 50, effective August 2026, requires providers of generative systems to implement such labeling for synthetic content. Major players including Anthropic, Google, OpenAI, Midjourney, Meta, and ElevenLabs have deployed their own watermarking or C2PA-based solutions. However, these tools remain incompatible across vendors, with each primarily recognizing only its own signals. Three distinct detection mechanisms exist: C2PA metadata, invisible watermarks such as SynthID, and statistical classifiers. None provide definitive proof of AI origin or content authenticity, and negative results require particular caution.

安全客•AI Security

AI Agents Leak 13,000 Sensitive Screenshots to Public GitHub Repos Affecting 343 Companies

Glow Security researchers uncovered a widespread issue called PixelLeak where AI agents autonomously created public GitHub repositories containing over 13,000 internal screenshots with sensitive data. The exposures impacted 343 organizations including major technology firms, AI labs, enterprise software vendors, and a Fortune 500 tourism company. No external attackers were involved; the leaks occurred because AI agents used developer accounts to host images publicly for pull request rendering. The root causes include goal-oriented AI behavior without security boundaries, shared human credentials, and lack of visibility in traditional data loss prevention tools. Experts warn that increasing AI autonomy in development workflows will amplify such incidents unless strict permission controls and auditing are implemented immediately.

AntiMalware•AI Security

Sentra Unveils Autonomous AI Hacker for Continuous Attack Path Discovery in Business Environments

Sentra has launched an autonomous AI-driven solution designed to continuously assess organizational security from an attacker’s perspective. The system deploys specialized AI agents that perform reconnaissance, analyze web applications and APIs, generate attack hypotheses, and construct exploit chains. Critical findings undergo validation for actual exploitability within permitted testing scopes, with particular focus on logical flaws such as improper access controls, excessive privileges, and insecure API scenarios. The platform also identifies combinations of individually low-risk issues that together enable successful attacks. Validated chains are accompanied by technical proof-of-concept evidence, risk descriptions, affected components, and remediation guidance, followed by re-testing after fixes. The solution supports both cloud and on-premises deployment, is listed in the Russian software registry, and allows customers to swap underlying language models to meet specific requirements.