CYBERSPAN brings AI-driven, agentless network detection to MSSP environments

IntelliGenesis has announced the availability of CYBERSPAN for managed security service providers (MSSPs). The AI-driven network detection and response platform, originally developed to protect small and mid-sized contractors in the Defense Industrial Base, is now optimized for multi-tenant service delivery.

Managed security service providers must scale cybersecurity operations across diverse client environments without driving up costs or operational complexity. CYBERSPAN addresses this with a multi-tenant architecture that enables providers to onboard new customers using a standardized delivery model while maintaining strict tenant isolation.

The platform is agentless and cloud-optional, with flexible sensor deployment across on-premises and cloud environments. It integrates with existing SIEM, SOAR, and ticketing systems via API. Each tenant maintains its own baselines and models, and the system learns normal network behavior during an initial burn-in period, then flags activity that deviates from established patterns.

“We built CYBERSPAN to protect companies that face nation-state threats every day but don’t have enterprise security budgets,” said Angie Lienert, President and CEO of IntelliGenesis. “MSSPs are dealing with the same challenge across their client base. This gives them a way to deliver defense-grade detection without rebuilding their stack for every customer.”

CYBERSPAN was built to protect organizations handling sensitive government data, where cyber threats are persistent and sophisticated. The platform supports STIG hardening and aligns with NIST 800-171 controls. Threat detections are mapped to the MITRE ATT&CK framework, and the system provides predictive insights into potential attack paths, enabling MSSPs to address vulnerabilities before exploitation.

The platform reduces analyst workload by correlating related activity into unified threat stories rather than generating separate alerts. When events are flagged, analysts can see which models contributed to the detection and review accuracy metrics, providing the explainability security teams require.