Investigate security alerts and logs in minutes — not hours

Log Analyzer AI helps investigate a wide range of security events including: authentication anomalies (failed logins, credential patterns, impossible travel), privilege changes (unauthorized sudo usage, permission modifications), suspicious data access (unusual transfers, off-hours activity), process execution anomalies (suspicious processes, unusual commands), lateral movement indicators, and access pattern changes. The AI identifies these patterns in your logs and presents structured findings with timelines and recommended actions.

Log Analyzer AI is designed to complement your existing SIEM and EDR tools, not replace them. Your SIEM generates alerts — Log Analyzer AI helps you investigate those alerts faster. Export the relevant logs from Splunk, Sentinel, Elastic, or any other platform, scan them, and ask questions in plain English. The AI reconstructs timelines, identifies related events, and delivers structured findings so you can determine whether it's a real incident or a false positive — in minutes instead of hours.

Log Analyzer AI provides structured investigation reports with severity levels (CRITICAL/HIGH/MEDIUM/LOW) that can support compliance documentation. Many teams use these reports as investigation evidence during SOC 2, HIPAA, PCI-DSS, and other audits. One-click branded PDF export produces executive summaries, chronological timelines, and findings documentation. We also use encryption in transit and at rest and follow data protection best practices to protect your sensitive log data.

Log Analyzer AI works with logs exported from any SIEM or EDR platform — Splunk, Microsoft Sentinel, Elastic, CrowdStrike, Palo Alto, and others. Scan logs in any standard format (CSV, JSON, syslog, EVTX, plain text) and start investigating. Enterprise tier includes scheduled scans with webhook notifications to Slack, Teams, PagerDuty, or custom endpoints, plus repository monitoring for automated log ingestion from S3, Azure, GitHub, and SFTP.

Log Analyzer AI offers a generous free tier so you can try real investigations immediately — no credit card required. When you need more compute capacity, the Professional plan at $49/month covers individual analyst investigations, and the Enterprise plan at $149/month adds multi-seat team access, role-based permissions, and advanced workflows. Sign up free to get started.

Automated Repository Monitoring allows you to connect your cloud storage or version control systems directly to Log Analyzer AI. Instead of manually uploading log files, the system automatically fetches new files from your repositories on a schedule (daily, weekly, or monthly) and analyzes them for security threats. Supported repositories include AWS S3, Azure Blob Storage, GitHub, SFTP servers, and Google Drive. This provides continuous, zero-touch security monitoring for your infrastructure.

Setting up repository monitoring is simple: (1) Navigate to Schedule Scan page, (2) Click "Connect a repository", (3) Select your repository type (S3, Azure, GitHub, SFTP, or Google Drive), (4) Enter your credentials (encrypted and stored securely), (5) Configure the file path and pattern (e.g., *.log), (6) Set your sync interval. The system will test the connection and begin automatically fetching files on your schedule. All credentials are encrypted using AES-256 and never stored in plain text.

Yes! You can create scheduled security scans that run daily, weekly, or monthly. When combined with repository monitoring, this provides fully automated continuous security monitoring. The system will: (1) Automatically fetch new log files from your repository, (2) Analyze them using your selected detection profile, (3) Send email and webhook alerts for any threats detected, (4) Maintain a complete audit trail of all scans and findings. This is perfect for compliance requirements and 24/7 security operations without manual intervention.

Absolutely. We take security seriously: All repository credentials are encrypted using AES-256 encryption before storage. Credentials are never logged or exposed in error messages. We use read-only access whenever possible—we never modify or delete files in your repositories. Downloaded files are analyzed and then deleted according to your retention policy. All connections use TLS/SSL encryption in transit. We also provide connection testing before saving credentials to ensure everything works correctly.

The system includes comprehensive error handling and monitoring: Automatic retry logic with exponential backoff for transient failures. Detailed error logging in the execution history showing exactly what went wrong. Email notifications for persistent failures so you're always aware. Repository sync status tracking showing last successful sync, error messages, and next scheduled sync. Complete audit trail of all execution attempts for compliance and troubleshooting. You can view the full history and status of all scheduled scans and repository syncs from your dashboard.

Log Analyzer AI is an AI-powered security investigation assistant that helps analysts understand what happened in their logs — fast. Scan logs from any source (SIEM exports, cloud logs, system logs), ask questions in plain English, and get structured investigation reports with chronological timelines, prioritized findings, and recommended actions. It works alongside your existing SIEM and EDR tools to accelerate alert triage and incident investigation. Enterprise plans include multi-seat team access with role-based permissions (Admin, Analyst, Viewer) for secure collaboration.

Currently, Log Analyzer AI supports all common file formats.

Text and Log Files
.log: A plain text file used to store log data generated by software applications. Often contains a chronological record of events, errors, and other significant occurrences.
.txt: A simple text file that contains unformatted text. It’s widely used for basic text storage and documentation.
.csv: Comma-Separated Values file used to store tabular data in plain text format, where each line represents a row, and fields are separated by commas.
.tsv: Tab-Separated Values file similar to CSV but uses tabs instead of commas to separate fields.

Document and Spreadsheet Files
.docx: Microsoft Word document file used to create and edit text documents with rich formatting.
.pdf: Portable Document Format file used for presenting documents in a manner independent of software, hardware, or operating systems. It’s widely used for sharing documents.
.rtf: Rich Text Format file used for text documents that include formatting information, such as bold, italics, and font size.
.xls / .xlsx: Microsoft Excel spreadsheet files used to store and analyze tabular data. .xls is the older binary format, while .xlsx is the newer XML-based format.

Image Files
.jpg / .jpeg: Image files using the JPEG format, which is commonly used for photographic images due to its compression capabilities.
.png: Portable Network Graphics file used for images. It supports lossless compression and is commonly used for web graphics with transparent backgrounds.

Script and Batch Files
.sh: Shell script file used to write scripts for Unix and Linux systems. It contains commands that can be executed in the shell.
.bat: Batch file used in Windows to automate command-line tasks. It contains a series of commands to be executed by the command interpreter.
.ps1: PowerShell script file used to write scripts in the PowerShell language, often for automating administrative tasks on Windows.

Markup and Data Files
.html / .htm: HyperText Markup Language file used to create and structure content on the web. .htm is an alternative extension used by older operating systems.
.xml: eXtensible Markup Language file used to store and transport data. It’s a flexible way to create information formats and share structured data.
.json: JavaScript Object Notation file used for data interchange. It’s lightweight, easy to read, and commonly used in web applications for data transmission.
.yml / .yaml: YAML Ain’t Markup Language files used for configuration and data serialization. They are human-readable and often used in configuration files for software applications.

Programming and Scripting Files
.cs: C# source code file used in .NET applications. It contains source code written in the C# programming language.
.java: Java source code file used to write applications or applets in the Java programming language.
.py: Python script file that contains code written in Python, a popular high-level programming language.
.js: JavaScript file used to write scripts that can be embedded in web pages to provide interactive functionality.
.css: Cascading Style Sheets file used to style HTML documents. It defines the visual presentation of web pages, including layout, colors, and fonts.
.sql: Structured Query Language file used to manage and manipulate databases. It contains queries for creating, reading, updating, and deleting data.
.ini: Initialization file used to store configuration settings for software applications. It’s a simple text file with a structured format of sections and key-value pairs.
.cfg / .conf: Configuration files used to store settings and preferences for software applications. They are often plain text and can be specific to a particular application.
.env: Environment file used to set environment variables for software applications. Commonly used in development environments to store sensitive information like API keys.
.pyw: Python script file similar to .py but is used to run Python programs without opening a command prompt or console window.
.pyt: Python script file often associated with Esri’s ArcGIS for custom geoprocessing tools.
.c: C source code file used to write programs in the C programming language.
.cpp: C++ source code file used to write programs in the C++ programming language.
.h / .hpp: Header files used in C and C++ programming to declare the interfaces to functions and data structures. .hpp is typically used for C++.
.php: PHP source code file used to write server-side scripts in the PHP programming language, often for web development.
.rb: Ruby source code file used to write programs or scripts in the Ruby programming language.
.ts / .tsx: TypeScript files used to write applications in TypeScript, a superset of JavaScript. .tsx files are used when working with React components.
.kt / .kts: Kotlin source code files used to write programs in the Kotlin programming language. .kts is used for Kotlin scripts.
.go: Go source code file used to write programs in the Go programming language, developed by Google.
.swift: Swift source code file used to write applications for iOS, macOS, watchOS, and tvOS.
.pl / .pm: Perl script files where .pl is a standard script file and .pm is a Perl module file.
.r: R script file used to write programs and perform data analysis in the R programming language.
.md: Markdown file used for formatting text using plain text syntax. Commonly used for README files and documentation.
.rs: Rust source code file used to write programs in the Rust programming language.
.toml: TOML (Tom’s Obvious, Minimal Language) file used for configuration. It’s designed to be easy to read and write.
.m: Source code file used by multiple programming languages. In Objective-C, it’s used to implement class files; in MATLAB, it’s used for scripts and functions.

Event Viewer Logs
.evtx: Windows Event Log file used by the Windows operating system to log system, security, and application events.

Log Analyzer AI uses sophisticated GPT-based algorithms to read and analyze the contents of your log files. It identifies patterns, anomalies, and provides comprehensive reports based on the file data.

Yes, there is a free version of the service.

To use Log Analyzer AI, simply provide your log file in the supported format by attaching it to the ‘analyze’ form. Our tool will analyze the file and provide you with a detailed report in a matter of minutes or seconds depending on the file size.

Yes, using Log Analyzer AI is completely safe and secure. We do not upload your files to our servers. Our system analyzes the data directly from the files on your device, ensuring that your sensitive information remains private and secure.

No, we do not store the files you provide for analysis. Log Analyzer AI processes files locally on your device, ensuring that your original data remains private and is not transmitted to our servers. However, to continually improve and tailor our services specifically to your needs, we do store the results of the analysis performed on your data. Please be assured that these results are used exclusively to enhance our service offerings to you and are not utilized for any other purposes.

Yes, Log Analyzer AI is designed to efficiently handle and analyze large log files.

Log Analyzer AI provides insights such as error detection, usage patterns, system performance issues, security breaches, and more, depending on the nature of your log files.

SOC analysts, security engineers, security managers, compliance teams, and IT professionals who investigate alerts and analyze logs regularly. Whether you're triaging SIEM alerts, reviewing cloud access logs, or documenting incidents for compliance — Log Analyzer AI accelerates the investigation process. From individual analysts to multi-seat enterprise teams, the platform scales to fit your needs.

Yes, we offer customer support for Log Analyzer AI. You may open a chat or email us directly at support@loganalyzer.ai