AI-Fusion Cyber/Physical Intelligence, the primary operational challenge with video is that traditional Video Management Systems (VMS) isolate visual data from core enterprise IT and cyber feeds.
Equitus Intelligent Ingestion Systems—combining Equitus Video Sentinel (EVS) and the Knowledge Graph Neural Network (KGNN)—achieve focused situational awareness and rapid threat response on IBM Power10/Power11 infrastructure through specific architecture and processing capabilities.
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1. GPU-Free Real-Time Ingestion at the Edge (IBM Power MMA)
The Problem: Video analytics historically required energy-heavy, expensive GPU clusters, forcing organizations to either process video off-site (cloud risk) or drop resolution/frames.
The Focused Solution: EVS natively utilizes IBM Power10/Power11 Matrix Math Assist (MMA) acceleration.
MMA handles complex matrix algebra directly on CPU cores, executing deep learning and computer vision inference (tracking people, license plates, loitering, perimeter breaches) on-premises without discrete GPUs. Impact: Reduced latency, eliminated cloud egress fees, and lower energy consumption while maintaining data sovereignty locally.
2. Cross-Domain Semantic Unification (KGNN & SPARQL)
Problem: Video alerts alone create "alert fatigue." Knowing a vehicle entered a gate is useless unless correlated with cyber events (e.g., an unauthorized badge swipe or unusual server access at that exact time).
Focused Solution: As EVS extracts rich spatial-temporal metadata (attributes, motion, behaviors), Equitus KGNN automatically converts this unstructured visual data into graph entities.
SPARQL Engine: Using standard SPARQL queries, KGNN links video entities across disparate IT logs, access control systems, and network traffic without requiring rigid schemas or complex manual ETL (Extract, Transform, Load) pipelines.
3. Seamless On-Prem MMA to Cloud GPU Hybrid Bridge
Focused Solution:
On-Premise (Power MMA): Daily inference, real-time alerting, local data storage, and knowledge graph queries stay entirely on-premise within the RHEL/OpenShift environment.
Cloud Bridge (GPUs): When heavy fine-tuning of large foundation models, global model retrains, or complex multi-modal LLM vectorizations are required, the architecture securely offloads non-sensitive vector representations to cloud GPU clusters.
Impact: Maximum performance where needed, without sacrificing localized, air-gapped security for sensitive video feeds.
4. Human-in-the-Loop (HITL) Precision & Traceability
Problem: Automated AI can generate false positives or hallucinate relationships when linking cyber and physical threats.
Focused Solution: The platform keeps human operators centrally involved. Automated graph correlations flag suspicious patterns (e.g., "Physical access attempt matched with internal network privilege escalation"), which are routed to security analysts for one-click validation.
Operator confirmations feed directly back into the system to refine accuracy and maintain complete audit traceability for compliance (CJIS, NERC CIP, HIPAA).
5. Accelerated Deployment via Equitus Expert Labs
Turnkey Delivery: To streamline adoption, Equitus Expert Labs provides end-to-end consulting, deployment, and testing services.
Pre-configured deployment packages (designed for servers like the IBM Power IC922/S1022 running Red Hat OpenShift) ensure seamless integration with Active Directory, existing cameras/VMS, and up to enterprise-wide data lakes out-of-the-box.
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