Kaspersky Releases KUMA 4.6 with Knowledge Base, External LLM Support and Automated Regex Generation
Kaspersky has released Kaspersky Unified Monitoring and Analysis Platform (KUMA) version 4.6, bringing significant updates to its SIEM solution used for security event monitoring and analysis.
The most substantial change involves a completely redesigned mechanism for delivering vendor content such as normalizers, correlation rules and other detection materials. The new knowledge base replaces the previous delivery system as the primary tool while retaining the old method for compatibility reasons. Analysts can now search and select content more efficiently for specific use cases, and emergency packages containing detection rules for newly discovered attacks can be distributed faster.
KUMA 4.6 also introduces support for connecting external large language models that are compatible with the OpenAI API. Supported models include GPT-4, Llama 3 and GLM-5.2, which can be hosted either in the cloud or within the customer’s own infrastructure. When deployed on-premises, the Kaspersky Investigation & Response Assistant can perform event analysis and assist with investigations without transmitting data to external services.
One of the standout AI features allows the assistant to automatically generate regular expressions for parsing logs. Security administrators simply provide sample log entries, and the system attempts to create the appropriate Regex patterns itself.
The platform’s integration capabilities have been expanded with support for SFTP and SMB protocols for retrieving data from file repositories, as well as ODBC drivers that enable connections to various database management systems.
Finally, the interface has been refreshed and now includes a dark theme, addressing long-standing requests from corporate users who frequently work during nighttime incident response shifts.
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.