Anvilogic vs. Panther
Automate your SOC without centralizing your security data
Panther centralizes security operations around Snowflake or Databricks. Anvilogic works across the SIEMs, data lakes, and security platforms you already use, so you can expand coverage and automate more of the SOC without moving everything into one place first.
Go beyond detection and investigation
Enterprise security data rarely lives in one place. Some belongs in the SIEM. High-volume telemetry may live in a data lake. Other data stays with the security tools and cloud platforms that generate it. Anvilogic works across that environment instead of requiring security teams to centralize it first.
Anvilogic connects that work across four core areas of security operations:
Make new security data usable faster.
Make new security data usable faster without building another ingestion pipeline first.
Search across your environment from one place.
Search across SIEMs, data lakes, cloud storage, and security platforms from one place.
At a glance
Panther is designed around a Snowflake or Databricks-centered security architecture. Anvilogic is designed to operate across a distributed environment, including the SIEMs and data platforms your organization already runs.
| Panther | Anvilogic | |
|---|---|---|
| Where detections run | Snowflake or Databricks | Splunk, Sentinel, Snowflake, Databricks in native syntax |
| What has to move first | Telemetry must be ingested, parsed, and written into the lake | Raw logs stay in your repository and are queried in place |
| Non-cloud sources | Require a connector or forwarder to move data into a supported transport and then into the lake | MCP connector forwards on-premises and other non-cloud data into the team's existing repository, where Anvilogic can onboard, search, and detect against it |
| Search scope | The Snowflake or Databricks lake Panther populates | Federated across Splunk, Sentinel, Elastic, LogScale, Snowflake, Databricks, Azure, S3 |
| Detection authoring | Python, plus a no-code path that can't express scheduled rules or policies | Low code builder, detection-as-code with version control, Dev Kit with CLI, SDK, Terraform, MCP |
| Content maintenance | Managed detections receive updates, while custom detection-as-code content in a forked or cloned repository requires the team to reconcile its own changes | Thousands of MITRE-mapped detections maintained by a dedicated threat research team |
| Analyst surface | Alert statuses, assignment, and comments. No case management layer | Native triage and case management, with coverage and maturity reporting |
| Workflow orchestration | Individual AI tasks with alerts routed to SOAR or ticketing for the broader process | Blueprints chain agents into human-governed workflows, then hands a decided case to SOAR or ticketing |
Why the difference matters
Detect what matters, not just what you can afford to centralize.
Centralizing security data creates a tradeoff: every additional source adds ingestion, storage, and query costs. That can leave high-volume telemetry filtered, sampled, or outside the environment where detection happens. Anvilogic leaves data in the systems already holding it and runs detection and search across those environments. Coverage is driven by what your team needs to detect, not by what fits into a centralized ingestion model.
Modernize without forcing a migration first.
Moving toward a data lake doesn't mean your existing SIEM disappears overnight. Anvilogic lets teams operate across Splunk, Sentinel, Snowflake, Databricks, and other security data platforms at the same time. Detections deploy into each environment in its native syntax, while federated search lets analysts work across them. Your architecture can evolve without making a data migration the prerequisite for improving security operations.
Automate the workflow, not just individual tasks.
AI can triage an alert, propose a detection change, or run a hunt. But the SOC still has to determine what happens next, what context is needed, where approval is required, and how the outcome gets carried forward. Anvilogic Blueprints orchestrate agents, tools, decisions, and human approvals into governed workflows across security operations. Instead of automating isolated tasks, teams can define how the work gets done.
Anvilogic replaced an incumbent SIEM at a regulated financial services company
A regulated financial services company with a Snowflake environment needed to replace its incumbent SIEM by a hard deadline. The company evaluated Anvilogic against Panther and an EDR vendor's SIEM, and signed in about four months from first conversation.
Because raw data stays in the customer's repository and is queried in place, standing up Anvilogic took granting access to tables. No second copy of the data, no duplicated storage bill, and no data project to fund before the security work could begin. The data lake commitment they already held got used for security.
Multi-stage correlation across identity, endpoint, and cloud. A centralized allow-list framework with time-to-live for tuning. Warn, alert, and test rule modes so detections run without generating noise. Auto-deployment of new content by recommendation score. Alerts routed into the SOAR and ticketing tools they already ran.
See Anvilogic in Action
Bring one new data source, two repositories, one detection, and one real workflow. In a demo, we'll show you how Anvilogic onboards it, searches across environments, deploys the detection, and automates the process, without asking you to rebuild your stack first.
Get a demo