June 2026 monthly summary for armosec/armoapi-go focusing on AI-Sandbox features, DTO migrations, and modular architecture improvements across the repo. The month emphasized delivering data-model extensions, read/write separation for scalability, and structural reorganization to improve downstream integration and business value.

May 2026 monthly summary for armosec/armoapi-go: Delivered key improvements in documentation and GitHub ticketing integration, enhancing developer onboarding, library usability, and ticketing workflows. The work strengthened maintainability and set the foundation for cross-provider integrations.

April 2026: End-to-end hot CVE handling enhancements in armosec/armoapi-go, delivering stronger detection, richer alerts, and improved reliability. Key changes include new HotCVE types and evaluation logic, API fields to surface hot CVEs across vulnerabilities, per-workload context in alerts, and canonical severity validation with expanded tests. These deliver faster triage, reduce missed hot CVEs, and preserve backward compatibility for dashboards and UNS integrations.

March 2026 monthly summary for armosec/armoapi-go focusing on Vulnerability Scan Failure Reporting Enhancements and API contract consolidation. Highlights include expansion of ScanFailureReport, standardized failure reasons, and improved customer-facing notifications, delivering measurable improvements in traceability, alert quality, and deploy-flexibility.

February 2026 monthly summary focusing on key accomplishments for armosec/armoapi-go. Delivered Workflow Filtering Enhancements introducing CVSSMax, NoExploitability, and RiskFactorNone to refine vulnerability filtering and strengthen security posture assessment. PR merge completed (commit 7c1fa58501992cab01e681fa955c2becb7fa6873). Result: more granular filtering, faster triage, and clearer risk visibility for security teams.

October 2025: Stabilized the system-tests suite by restoring correct success signaling for CloudVulnScan and CloudConnect. Reverted two commits to reestablish baseline behavior, improving reliability of test outcomes and confidence in automation results. This work reduces CI noise, accelerates feedback to developers, and strengthens downstream integrations relying on accurate success status.

August 2025: Stabilized system-tests by reverting regression changes to restore expected behavior and maintain automation reliability. Key deliveries included restoring RegistryChecker startup semantics (removing the unintended explicit success signal) and re-enabling Teams-related Workflows, ensuring CI/CD pipelines and team notifications operate as designed. These actions reduce flaky tests, prevent misleading success signals, and protect release velocity.

February 2025 monthly summary for armosec/system-tests focused on stabilizing CloudConnect behavior by removing an explicit success return, which simplifies control flow and prevents misleading status signals across systems. The change emphasizes reliability and maintainability in our system-tests suite while keeping the public API contract intact.

January 2025 monthly summary for armosec/system-tests focusing on reliability improvements and test orchestration accuracy. Delivered a fix to Vulnerability Scan Start return semantics to ensure consistent signaling for orchestration, and updated test mappings to reflect target repositories as part of the same change. These changes reduce downstream misinterpretation and stabilize automated security tests across environments.

December 2024: Stabilized the system-tests suite by restoring critical behavior and notification flow. No new features released this cycle; focused on bug fixes that reestablish expected test outcomes and ensure issue visibility.

Month 2024-11 | armosec/system-tests: Focused on reliability and clarity of automated alerts through a targeted bug fix. No new features were delivered this month. The primary change improves the accuracy and readability of alert messages by correcting the spelling of 'compliance' and ensuring the misconfiguration score is represented correctly in automated alerts. This reduces ambiguity for operators and strengthens the integrity of automated testing feedback.