About Anvilogic
Anvilogic is a security analytics software from Anvilogic that helps organizations detect threats and respond to security incidents. It combines threat intelligence, automated data collection, and machine learning to provide actionable insights for security teams. The platform enables users to analyze vast amounts of security data in real-time, improving incident response times and reducing false positives. Anvilogic also supports custom dashboards and reports, allowing for tailored visualizations of security metrics. Key capabilities: threat detection incident response data visualization machine learning integration custom reporting Best for: security teams that need effective tools for monitoring and responding to security threats.
Anvilogic by Anvilogic is an advanced enterprise content management (ECM) and analytics platform designed to unify, secure, and streamline data and document management across complex enterprise environments. Its primary goal is to provide organizations with a centralized solution for managing unstructured and structured content while maintaining governance, traceability, and operational efficiency. Key features include intelligent content search, metadata tagging, AI-driven data classification, automated workflows, and secure collaboration tools that ensure compliance with organizational and regulatory requirements. The user interface of Anvilogic is clean, modern, and highly intuitive, focusing on efficiency and ease of navigation. Its dashboard provides users with customizable widgets and visual analytics, offering quick insights into document activity, security status, and workflow performance. The drag-and-drop document upload and intelligent search bar make it simple for both technical and non-technical users to access and manage information effectively. Functionality-wise, Anvilogic stands out for its AI-powered automation and analytics capabilities, enabling organizations to process large document sets, detect patterns, and extract key information automatically.
Pros & Cons
- +Offers up to 80% lower cost than legacy SIEMs by utilizing cloud data stores like data lakes.
- +Reduces detection engineering effort by 60–80% and increases detection build time by 5–6 times.
- +Cuts alert volume by 90% and decreases alert noise by 45% with high confidence through Agentic Triage.
- +Provides platform-agnostic detection and unified triage across existing SIEMs and modern data lake architectures.
- +Provides 98% accuracy of benign alert identification and achieves up to 50%+ reduction in Mean Time to Detect (MTTD).
- −Full adoption of the AI SOC model requires commitment to a cloud data lake platform (Snowflake, Databricks, or Azure).
- −Migrating detections and integrating across hybrid SIEM and data lake environments can be complex.
- −Relies on data lake providers (Databricks, Snowflake) and existing SIEMs (Splunk, Sentinel) for full functionality.
- −Detection engineers may face a learning curve when shifting from legacy SIEM query languages to standardized SQL-based logic.
- −Trustworthy results are dependent on having clean, normalized, and enriched data pipelines.
Features
Key features
- AI SOC for SIEM & Data Lakes
- Unifies detection, triage, and security mesh analytics across clouds and data lakes, offering ultimate SIEM flexibility for the AI era.
- Detection-as-Code
- Standardizes and scales smarter detection logic by allowing users to build, test, and validate point detections and multi-stage attack scenarios before deployment, using a common logic across platforms.
- Hybrid Detection & Data Lake Modernization
- Allows organizations to shift detections to data lakes (like Databricks, Snowflake, or Azure) while correlating across platforms and maintaining partial operation in existing SIEMs (like Splunk or Sentinel) to reduce ingest costs.
- Agentic Triage
- Automates triage and response by cutting alert noise with high confidence, enriching alerts with context, and instantly converting analyst decisions into repeatable playbooks.
- Tuning Agents
- Uses Machine Learning models to continuously monitor and optimize detection logic within the alert data lake, significantly reducing alert noise and triage fatigue.
- AI-Native Threat Detection Playbook
- Offers a modern foundation for security operations that prioritizes AI and automation to replace legacy complexity, putting the user in control of defining, tuning, and scaling detections.
Additional features
- SIEM & Data Lake Modernization
- Facilitates shifting detections to data lakes while maintaining operations partially in legacy SIEMs.
- An AI SOC as Your Last SIEM
- Offers a full SIEM replacement running on cloud data stores with lower cost than traditional SIEMs.
- AI Workflows
- Bundled AI SOC workflows designed for lean SecOps teams, including onboarding, engineering, maintenance, and triage.
- Detections Grounded in Clean, Normalized, Enriched Pipelines
- Ensures high-quality data is the foundation for detection logic.
- Transparent Reasoning for Trustworthy Results
- Provides visibility into how AI reached a conclusion to build trust in its outcomes.
- Agentic Triage
- Cuts alert noise with high confidence using a single investigation panel, prebuilt timelines, and enriched, action-ready alerts.
- Multi-domain Attack Pattern Detection
- Facilitates faster mean time to detect (MTTD) by correlating signals across various domains (endpoint, identity, cloud).
- Detection-as-Code (Workbench)
- Allows users to build, test, and validate detection logic using drag-and-drop filter components or an agentic workbench that generates logic.
- Thousands of Prebuilt Detections
- Offers an extensive library of detections instantly deployable and mapped to MITRE by threat group and vertical.
- Tuning Agents
- Continuously optimizes detection logic using Machine Learning models to reduce alert noise and triage fatigue.
- Cost Savings via Hybrid Optimization
- Offers a hybrid licensing model to support both SIEM and data lake architectures for cost control.
- Clear, Flexible Pricing
- Pay-as-you-use models that grow with the data strategy.
Pricing
Countries & Languages
Available in
Interface languages
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About Anvilogic
Anvilogic is a security analytics software from Anvilogic that helps organizations detect threats and respond to security incidents. It combines threat intelligence, automated data collection, and machine learning to provide actionable insights for security teams. The platform enables users to analyze vast amounts of security data in real-time, improving incident response times and reducing false positives. Anvilogic also supports custom dashboards and reports, allowing for tailored visualizations of security metrics. Key capabilities: threat detection incident response data visualization machine learning integration custom reporting Best for: security teams that need effective tools for monitoring and responding to security threats.
Anvilogic Details
Anvilogic's In-App Market Place
Does Anvilogic have an in-app market place?
Yes
How many Mini-Apps in the marketplace?
1
Mini Apps
N/A
Pricing Options
Accepted Payment Currencies
USD ($), EUR (€), GBP (£), JPY (¥), AUD ($), CAD ($), CHF (Fr), CNY (¥), SEK (kr), NZD ($), KRW (₩), SGD ($), NOK (kr), MXN ($), INR (₹), BRL (R$), RUB (₽), ZAR (R)
Pros & Cons
- Offers up to 80% lower cost than legacy SIEMs by utilizing cloud data stores like data lakes.
- Reduces detection engineering effort by 60–80% and increases detection build time by 5–6 times.
- Cuts alert volume by 90% and decreases alert noise by 45% with high confidence through Agentic Triage.
- Provides platform-agnostic detection and unified triage across existing SIEMs and modern data lake architectures.
- Provides 98% accuracy of benign alert identification and achieves up to 50%+ reduction in Mean Time to Detect (MTTD).
- Full adoption of the AI SOC model requires commitment to a cloud data lake platform (Snowflake, Databricks, or Azure).
- Migrating detections and integrating across hybrid SIEM and data lake environments can be complex.
- Relies on data lake providers (Databricks, Snowflake) and existing SIEMs (Splunk, Sentinel) for full functionality.
- Detection engineers may face a learning curve when shifting from legacy SIEM query languages to standardized SQL-based logic.
- Trustworthy results are dependent on having clean, normalized, and enriched data pipelines.
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