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
Agent-Ops 0.4.0 Released: Methodology for Secure Human-AI Collaboration in IT Operations
Sergey Zhitinsky, founder of Git in Sky, has published the public normative candidate for Agent-Ops 0.4.0, an open industry methodology governing how engineers and AI agents jointly handle IT infrastructure tasks. The framework keeps humans firmly in the decision-making loop while using deterministic programs for data collection and approved changes. It addresses risks such as prompt injection through processed data, unverified model outputs, and unclear accountability when AI recommendations lead to incidents. The methodology divides work across eight explicit steps and three separate planes: data, governance, and independent verification performed by a Guardian role. Two additional companies have joined as maintainers following agreements at the IT Elements 2026 conference, turning the project into a multi-organization effort. Contributors are invited to help refine contracts, schemas, and operational scenarios through GitHub and GitVerse.
ProxyKey MCP: Securing API Access for AI Agents Without Exposing Credentials
ProxyKey has released an MCP server that allows AI coding agents such as Claude Code and Cursor to manage API credentials without ever reading the actual secret values. The solution addresses the risk that any key visible to an agent becomes compromised through logging, tracing, or prompt injection. Real provider keys are stored encrypted with AES-256-GCM and never returned by any API endpoint after initial entry. Agents instead receive limited virtual passes that support IP binding, rate limits, TTL, and detailed request logging. A pending-secret workflow lets agents prepare services before the real token exists, with the human entering the secret only through a web panel. The approach deliberately restricts the MCP tool contract so no operation can read or return secret values.
Shadow AI in CI/CD: Why AI Agents Must Be Modeled as Security Threats
A new analysis from the CNCF highlights the growing risks of Shadow AI within continuous integration and continuous deployment pipelines. The report argues that AI agents should be treated as potential threats rather than simple productivity tools. Starting from a developer's laptop and extending to Kubernetes clusters, these agents can introduce unauthorized access paths and data exposure risks. Security teams are urged to incorporate AI agent behavior into formal threat modeling exercises. The discussion emphasizes the need for visibility and control over autonomous AI components operating in production environments.
Detecting Lateral Movement with Neural Networks Trained Solely on Synthetic Data
A researcher generated entire corporate network histories using a 135-line configuration file to create synthetic authentication logs containing lateral movement attacks. Neural networks trained exclusively on these artificial datasets were then evaluated against 1.65 billion real authentication events from Los Alamos National Laboratory, including 749 red team events across 301 compromised machines. The best ensemble of six models flagged 3.6 million hourly machine windows and placed 16 genuine attacks among the top 23 highest-scoring entries, producing only seven false positives. In comparison, a simple threshold counter required 161,000 false alarms to reach the same detection level. The approach also demonstrated an iterative feedback loop where detector errors directly informed refinements to the synthetic world generator. The work shows that synthetic data can reach AUC performance comparable to models trained on real labeled attacks while providing full control over the underlying attack definitions.