Solution Guide

Unify Your SIEM and Data Lake Without Replacing Splunk

Close detection gaps and cut SIEM costs by 80%

The Security Data Challenges for Enterprise SOCs

Enterprise Security Operation Centers (SOCs) need better threat detection over their expanding attack surfaces, but security has a data problem that makes threat detection and response more complicated than necessary.

Many SOCs leave half of their security data outside their SIEM, often due to the high costs of scaling resources in Splunk. This practice results in what Gartner defines as 'dark data'—information assets collected but not used effectively for threat detection. This dark data often exceeds twice the volume of data analyzed in the SIEM.

As a result, analysts engage in inefficient "swivel-chair" investigations across multiple systems: their SIEM (a limited primary source of truth), their EDR tool, and an Amazon S3 bucket, where excess data is offloaded without cross-correlation. This fragmented approach generates low-quality alerts and leads to severe alert fatigue among security personnel.

While cloud long-term storage may seem cost-effective, it significantly impairs threat detection capabilities. Indicators of compromise (IoCs) may exist in this dark data, but they remain hidden until threat actors have progressed far down the kill chain. Attempts to query this data using services such as AWS Athena often result in long wait times and unexpected costs, making timely incident response difficult. The promise of log rehydration also falls short, with limitations on restoring volumes and frequencies that make historical investigations impractical and expensive.

Mature security teams must take these challenges into consideration when thinking about scaling their detection engineering program and architecture.

“Anvilogic has been a valuable partner in our journey. They’ve provided a solution that allows us to realize incredible cost savings with Snowflake and helped us gain more comprehensive security detection coverage from data we previously didn’t have visibility into.”
Anand Sastry
Head of Cyber Operations,
First Citizens Bank

Break Free From SIEM Lock-In with Anvilogic

Anvilogic is a multi-data platform SIEM that enables you to detect, hunt, and investigate seamlessly across your data platforms. It helps break the SIEM lock-in that drives detection gaps and high costs for enterprise SOCs. It is the first and only product on the market that enables detection engineers and threat hunters to keep using their existing SIEMs like Splunk and Azure while seamlessly adopting a scalable and cost-effective data cloud such as Snowflake for high-volume data sources and advanced analytics use cases.  

  

With Anvilogic and Snowflake, you can:
Illuminate Dark Data
Leverage previously untapped data sources for threat detection at 80% of the cost of Splunk and Sentinel.
Optimize Detection Management
Maintain healthier detections with version management and tuning insights.
Close Detection Gaps
Easily build and deploy high-fidelity detections across your data lakes.
Streamline SecOps
Utilize our Gen AI assistant for triage, hunting, and SQL generation.
90%
Cost savings with Anvilogic and Snowflake
$1.2M
Saved using Snowflake and Splunk
Use Case One

Illuminate Dark Data with Snowflake Integration

Use Case:
Building detections to cover your organization against threats
Current Way:
It is time-consuming to research threats and build queries to cover detection gaps. Data collection is limited, fragmented and sits in silos. Limited data schema skill sets make building query logic time and labor-intensive.

Now with Anvilogic, you can...

Capitalize on untapped data for threat detection by integrating it into Snowflake. Snowflake is compatible with all major cloud providers (AWS, Azure, GCP) and offers a cost-effective pricing model that scales at over 80% less than Splunk.

Tap into Anvilogic’s ready-to-use Snowflake connectors that allow us to periodically schedule our expanding repository of over 800 Snowflake detections, updated weekly with new security use cases and advanced attack correlation patterns. Our modular architecture lets you deploy detection content directly where your data resides, delivering top-tier detections meticulously tested and validated by our in-house Anvilogic Forge Purple Team. These detections are enriched with metadata on threat actor groups and the TTPs (Tactics, Techniques, and Procedures) they defend against.

Our detailed tagging of each detection content helps identify the most relevant rulesets based on the data feeds present in Snowflake tables. With our AI Recommendation Engine, you can easily choose the best TTPs from our extensive library based on your available data feeds. Enhance your defense against MITRE ATT&CK TTPs with quantifiable coverage scores, and identify areas for improvement to close your detection gaps.

Case Study: Alteryx

Alteryx is a leading data analytics software company with over 8,000 customers globally. It faced many challenges in enhancing its security operations. The company's monolithic SIEM couldn't keep pace with evolving threats, and it needed to involve all team members in security efforts, regardless of technical expertise. Alteryx sought a bridge solution instead of a complete overhaul to effectively handle today’s cybersecurity practices and accommodate growing data volumes.

With Anvilogic, Alteryx was able to enhance its threat detection capabilities. They can now:

  • Build and deploy security threat detection use cases through a visual builder.
  • Enhance maturity assessments via MITRE ATT&CK framework integration.
  • Improve cost control by decoupling the detection layer.
"Anvilogic is the perfect solution because it doesn't depend on any specific underlying data lake or SIEM solution. It isolates and abstracts the layer of data storage down to the schema, so we don't have to worry about making a big decision for the underlying storage solution. Instead, we have the flexibility to plan for the future."
Guang Wang
Sr. Director of Security Operations and Engineering, Alteryx
Use Case Two

Get Tuning and Detection Gap Insights Across Data Platforms

Use Case:
Maintaining healthy detections throughout the lifecycle
Current Way:
An iterative process that requires constant updating and tuning of logic as data formats change or if new telemetry is added. It makes it difficult to scale and maintain high-quality detections effectively.

Now with Anvilogic, you can...

Search across Snowflake tables, save those searches, and effortlessly track, manage, and optimize all your queries with a workbench that facilitates version control and tuning. Our intuitive detection-building wizard streamlines the entire detection lifecycle, offering metadata fields for tagging customization, risk score adjustments, and the ability to input reasoning and track change history for monitoring detection logic changes side-by-side. You can slide into Monte Copilot’s DMs to help you build the SQL logic needed for complex queries using simple natural language.
Use Case Three

Reduce Alert Fatigue with Multi-Stage Attack Correlation

Use Case:
Correlating across saved queries and building advanced detections to protect the organization against threats.
Current Way:
Discrete/atomic event queries cause high alert volumes. Advanced correlations require complex query logic, which is time-consuming and requires deep schema knowledge to build.

Now with Anvilogic, you can...

Build complex detection logic using a simple Low Code Builder that can correlate atomic events across multiple stages and upon correlated entities.
Easily correlate Crowdstrike, Defender, or other EDR alerts with SIEM Saved Searches.
Leverage hundreds of out-of-the-box scenarios or easily create your own.
Automatically builds SPL, KQL, & SQL Logic.
Automated MITRE mapping and risk-based scoring.
You can maximize the value of your team's atomic saved searches in your SIEM by correlating them to model sophisticated attack narratives. Each saved search is tagged to MITRE, scored, and correlated into threat scenarios and augments the fidelity of your alerts. 
Anvilogic's low-code detection builder allows you to create advanced detections across data platforms like Splunk, Snowflake, and Azure without complexity. It can automatically translate natural language query requirements into SPL, SQL, and KQL search logic, empowering practitioners by lowering entry barriers and reducing reliance on specific logging platforms. 

When higher-fidelity alerts are generated, Monte Copilot is ready to assist. Trained with Tier 3 Analyst expertise and access to common data sets and tools, Monte Copilot provides real-time answers for your triage needs. Transform slow, manual tasks into smarter, automated workflows with Monte Copilot's powerful functions, empowering your team to work more efficiently and effectively.

Case Study: Rakuten

Rakuten Mobile is a subsidiary of Rakuten Group, a global company that offers users worldwide various services in e-commerce, fintech, digital content, and communications. Their SOC team struggled with managing an overwhelming volume of low-fidelity alerts that led to analyst fatigue and inefficient threat detection processes.

With Anvilogic, Rakuten significantly improved its SIEM operations. They can now:

  • Reduce false positives by implementing scenario-based detections.
  • Improve alert fidelity through correlated detections.
  • Streamline use case development with pre-built scenarios.
"After implementing Anvilogic, we were able to take these singular detections and form a scenario based on sequential alerting. This decreased our false positives and painted a more specific picture for the analyst to understand the whole attack chain, which allowed us to triage alerts more promptly and solved our alert fatigue."
Sota Aoki
Security Engineer, formerly of Rakuten Mobile
Architecture and Product Features

Anvilogic Architecture

Product Features:

Detection
Detection Content (Anvilogic Armory)
  • Forge Threat Research delivering over 1000s of ready-to-deploy detections (updated weekly) in SPL, KQL, SQL.
  • Daily detections updated based on trending threats.
  • Premium Threat Scenarios & Cloud Detection Content Packs.
  • Hunting detection packs to detect anomalous behavior.
Detection Creation
  • Low-Code detection builder to create behavior pattern-based detections or risk based detection scenarios.
  • Import your pre-existing rules to be standardized across all alert data.
  • Frameworks, machine learning recommendations and documentation to help define testing (TTPs) all in one place.
Detection Management
  • Automated end-to-end detection lifecycle management.
  • Easy to clone/modify/deploy detections.
  • Use case documentation.
  • Automated maintenance.
  • Versioning & audit history of changes.
  • Parsing and normalization code management.
Continuous Maturity Scoring
  • End-to-end visibility of your SOC maturity based on data quality analysis, detection coverage across MITRE, and productivity metrics (ex. hunting, alert dwell time, etc.).
  • Measurable technique coverage and gap analysis.
  • Assessment validation testing integrated into maturity scoring framework.
AI-Insights
  • Hunting, Tuning, and Health Insights that continuously monitor your unique environment, escalate activity that requires attention, and remind you of crucial maintenance actions.
  • Hunting Insights delivered to help identify high-fidelity alerts and suspicious patterns across raw event logs.
  • Detection recommendations based on your industry threat.
  • Landscape, infrastructure, and MITRE ATT&CK coverage/gaps.
  • Data prioritization & recommendations based on your unique environment.
  • Automated Tuning recommendations to ensure your deployment is performing optimally.
Deployment Architecture
  • Licensing: annual subscription model based on the user count.
  • SaaS Deployment: Meta data, analytics, insights, audit logs, alerts, allowlisting, and enrichment stored in Anvilogic Alert Lake.
  • Ability to search, query data, and deploy detections across multiple SIEMs and/or cloud data lakes.
  • Able to automatically tag, normalize, and enrich detections before storage for optimal correlation.
  • Highly flexible, open API platform that integrates with many existing security technologies.
Data & Integrations
  • Supported Data Platforms: Splunk (On-Prem & Cloud), Azure Data Explorer, Azure Log Analytics, Snowflake (AWS, Azure, GCP).
  • SOAR Integrations: Torq, Tines, XSOAR, Swimlane, more.
  • Case Management Integrations: Jira, ServiceNow.
  • Security Vendor Integrations: Crowdstrike, Proofpoint, Palo Alto Cortex, Tanium, VMware Carbon Black, Microsoft Defender, StackRox, DarkTrace, SentinelOne, ReversingLabs, Hunters, Abnormal Security, and more.
Triage (Splunk Only)
Triage Management
  • Alert tuning, allow listing, triage observations.
  • Alert triage assisted by the link analysis of the hunting graph.
  • Triage across multiple hybrid cloud, cloud, and data lakes.
  • Visualize alert attack pattern and timeline.
Alert Correlation
  • We supply detections across multiple data repositories, allowing you to easily query different sources and centralize them for seamless correlation in one location.
Monte Copilot
  • SecOps Companion trained across various SOC personas for investigation & detection building assistance.
  • Access to common tools and data sets used by analysts for triage ex) VirusTotal, Shodan, IPInfo, and more.

Maximize SIEM Efficiency and Cut Costs

Anvilogic empowers detection engineers and threat hunters to enhance their current SIEM setup. By integrating a scalable and cost-effective data lake, you can manage high-volume data sources and advanced analytics without disruption.

Achieve up to 80% cost savings while closing detection gaps and reducing expenses for your SOC.

Calculate Your Cost Savings

Break Free From SIEM Lock-In

Break Free From SIEM Lock-In