AI-Powered, Agentless Network Detection for MSSPs

▼ Summary
– IntelliGenesis has launched CYBERSPAN, an AI-driven network detection platform now optimized for multi-tenant use by managed security service providers (MSSPs).
– The platform’s multi-tenant architecture allows MSSPs to scale operations and onboard clients efficiently while maintaining strict data isolation between customers.
– CYBERSPAN is agentless, integrates with existing security tools via API, and learns normal network behavior to flag deviations after an initial setup period.
– It was originally built to protect defense contractors from sophisticated threats and supports key security standards like NIST 800-171 and the MITRE ATT&CK framework.
– The platform reduces analyst workload by correlating alerts into unified threat stories and provides explainable AI insights with detection accuracy metrics.
For managed security service providers seeking to scale high-fidelity threat detection across multiple clients, a new solution offers an AI-driven, agentless approach built for multi-tenant efficiency. IntelliGenesis has expanded the availability of its CYBERSPAN platform, originally engineered for defense contractors, specifically for the MSSP market. This move provides service providers with a tool designed to deliver advanced network detection and response without the typical overhead associated with securing diverse and complex client environments.
The core challenge for MSSPs involves protecting numerous distinct networks without allowing costs or operational complexity to spiral out of control. CYBERSPAN confronts this issue directly through its multi-tenant architecture. This design allows providers to onboard new customers using a consistent, standardized model while ensuring rigorous data and operational isolation between each client. The platform operates without agents and offers cloud-optional flexibility, with sensors that can be deployed across on-premises infrastructure and various cloud environments. It connects seamlessly to existing security tools like SIEM and SOAR systems through API integrations.
A key operational feature is the platform’s learning capability. For each client tenant, the system establishes a unique behavioral baseline during an initial monitoring period. After this, it continuously analyzes network traffic, flagging any activity that meaningfully deviates from learned normal patterns. This method focuses detection on anomalous behavior that could indicate a threat.
Angie Lienert, President and CEO of IntelliGenesis, explained the platform’s origins and its fit for the MSSP sector. The technology was first developed to safeguard smaller defense contractors who face advanced persistent threats but lack massive security budgets. MSSPs encounter a parallel challenge across their entire client portfolio. CYBERSPAN offers them a practical path to providing that same caliber of protection without needing to reconstruct their technical stack for every single customer.
Given its background in protecting sensitive government data, the platform incorporates several rigorous security and compliance features. It supports Security Technical Implementation Guide (STIG) hardening and aligns with controls outlined in NIST 800-171. Every threat detection is mapped to the MITRE ATT&CK framework, providing clear context for adversarial tactics and techniques. Beyond simple alerting, the system offers predictive insights about potential attack paths, empowering MSSPs to proactively address security weaknesses before they can be exploited.
To alleviate alert fatigue and streamline analyst workflows, CYBERSPAN correlates related security events into unified threat narratives instead of generating a flood of isolated alerts. When an incident is flagged, analysts can drill down to see exactly which behavioral models contributed to the detection. They also have access to accuracy metrics for these models, delivering the critical explainability that security teams need to validate alerts and respond with confidence.
(Source: HelpNet Security)





