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馃殌 Arthur Engine Release
October 11, 2026
This release expands SIEM discovery capabilities with new Splunk Enterprise and Elastic Security connectors, introduces a standalone ML Engine discovery mode, and delivers a range of connector validation fixes and observability enhancements.
Discovery & SIEM Connectors
New Connectors
- Added Splunk Enterprise as a SIEM discovery connector, enabling organizations to discover and monitor security events directly from Splunk, with robust TLS handling, job lifecycle management, and comprehensive error classification (#2404)
- Added Elastic Security as a discovery connector that executes customer-authored ES|QL queries and publishes results as discovery records, with configurable TLS verification and automatic error classification (#2405)
Standalone Discovery
- Introduced a standalone agent discovery runner mode for the ML Engine that operates independently of the Arthur Platform and GenAI Engine, scanning sources on a schedule and sending discovered agents to Splunk HEC or a webhook (#2427)
- Bumped arthur-client to 1.4.2770 to enable Test Connection support in the ML Engine, including the required job kind, test-result models, and
gcp_vertexvendor surface (#2415)
Connector Reliability & Validation
- Consolidated URL validation across the Jamf, Splunk, and Elastic connectors into a shared
address_problemfunction, ensuring malformed URLs are consistently rejected at setup time with clearer error messages (#2429) - The Jamf connector now validates
base_urlformat before making requests, reporting malformed URLs asnot_configuredinstead of generic provider errors (#2424) - Vertex AI discovery scans now correctly classify HTTP 401 and 403 failures as authentication and permission errors rather than generic provider errors (#2428)
- Fixed Test Connection result classification so timeouts with no data correctly fail as
TIMEOUTand configuration errors are no longer misclassified as vendor errors (#2417) - Manual discovery scans now tag their chained fetch operations with the scan's ID, allowing the Platform to correctly track scan completion before reporting results (#2425)
This release significantly broadens the Arthur Engine's SIEM integration footprint while making configuration and connectivity diagnostics more accurate and easier to troubleshoot across all discovery sources.
Observability & Evaluations
- Trace annotations now correctly populate the evaluation type field for continuous LLM and ML evaluations, ensuring accurate metadata regardless of sort order or lookup method (#2431)
- Added TypeSafe AI instrumentation support to the Python SDK via an optional
typesafeinstallation extra, included in thealldependency group (#2423) - Fixed a bug where metrics and alert-check jobs could overwrite user-initiated check chains; the engine now stamps follow-up job IDs only onto the chain a completed job actually heads (#2407)
These improvements give users clearer visibility into which evaluations generated each annotation, expand supported instrumentation frameworks, and prevent scheduled runs from interfering with user-initiated checks.
Infrastructure & Governance
- Added a CODEOWNERS human-review gate requiring approval from the Arthur engineering team on PRs that modify sensitive paths across released containers, deployment infrastructure, CI/CD, database migrations, and credential handling (#2421)
- CI workflows now authenticate container and service image pulls to Docker Hub, resolving rate-limit failures during container initialization (#2435)
- Updated the isort dependency to v9, bringing improved lazy import handling, better performance, and Python 3.15 compatibility (#2198)
- Updated the sentence-transformers dependency to v6.1.0, with enhanced multimodal input handling and fixes to loss formulas (#2170)
These changes strengthen security governance around critical code paths and improve the reliability and currency of the build and dependency ecosystem.
Internal & Automation
Release notes generated by Louisa