Release date: 22.06.2026
LogMan.io v26.12 brings major enhancements to Alert Management, including ticket compression, unified severity levels, SLA monitoring, and push notifications. Discover defaults to the last 4 hours, supports up to 1,000 rows with infinite scroll, and lets you filter by alert ticket and navigate back. Parsec adds processors for VMware Aria Operations for Logs and SAP SAL logs and automatic parsing of Windows Event Access fields.
More than 57% of developers' time is spent in war rooms solving application performance issues rather than building new features (Cisco/Splunk Developer Survey, 2024). That's not a skills problem. It's a logging problem.
The logs your application writes today were crafted for the developer sitting at the keyboard. Someone who knows the code structure, remembers which module emits which message, and understands the internal state machine. When an incident hits at 02:00 in the morning, the person staring at those logs isn't that developer. It's an on-call engineer who doesn't know your code, or an AI agent parsing them for patterns.
Microsoft has changed the way Message Trace data is retrieved from Exchange Online. The original Reporting Webservice API that TeskaLabs LogMan.io used for Message Trace collection is being deprecated and replaced by a new Microsoft Graph API. If you collect Microsoft 365 Message Trace logs, you must update your Azure / Entra ID configuration, otherwise Message Trace collection will stop working.
Over the past few months, the landscape of open-weight large language models has changed dramatically. New models are being released at a pace that makes systematic evaluation difficult, yet increasingly necessary.
Most LLM comparisons still rely on synthetic benchmarks. While useful, they often fail to answer a more practical question:
How do these models perform in a real, production-like task?
This article presents a structured evaluation of local LLMs based on a concrete, repeatable, and measurable workload: autonomous code generation. Specifically, we focus on how well an autonomous agent, backed by these models, generates production-ready log parsers, and how that performance scales in terms of quality vs. speed.
Most security monitoring focuses on what happened, such as a failed login, a new admin, or a suspicious process. That approach is useful, but many strong warning signs do not come from one event. They come from a change in how a host behaves over time. When normal behavior starts to shift, that is often where analysts first see a real problem forming.
Vector baselines solve this by learning the usual shape of activity for each host and then detecting when that shape changes. Instead of writing and tuning many static thresholds, baselines let the system learn what is typical for each host or device.
When current behavior is statistically far from that learned normal, the baseline creates a signal or a complex event, that is visible in the Discover section.
Release date: 16.02.2026
LogMan.io v25.47 introduces a complete redesign of the Observability interface, featuring intuitive point-and-click customization for Dashboards, Homepage, and Discover screens. This release also delivers significant performance improvements with an optimized row lookup algorithm and faster event replay capabilities.
TeskaLabs LogMan.io is a SIEM (Security Information and Event Management) and advanced log management cyber security tool.
The NFR (Not-For-Resale) release of TeskaLabs LogMan.io is distributed as a virtual machine. The NFR VM is intended for evaluation, demonstrations, proof-of-concept deployments, and training purposes.
The following guide covers how to start and operate the TeskaLabs LogMan.io NFR Virtual Machine.