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.