Equitus.ai software architecture—including:
- ICAM
- FUSION
- ARCXA
- KGNN (Knowledge Graph Neural Network)
- EVS (Equitus Video Sentinel)
With applications running directly on IBM Power infrastructure, organizations overcome vulnerabilities typical of public cloud LLM deployments (such as prompt injection, unauthorized data exfiltration, and black-box untrace-ability) while maintaining tight physical perimeter security.
1. Cyber & Data Security: Equitus KGNN & ARCXA on IBM Power
Standard Large Language Models (LLMs) and cloud-based AI systems suffer from inherent vulnerabilities, including data leakage, hallucination, and susceptibility to indirect prompt injection or payload manipulation. Equitus's data security suite addresses these vulnerabilities directly on IBM Power:
GPU-Free On-Premises Execution: KGNN runs natively on IBM Power10/Power11 servers using IBM's built-in Matrix Math Accelerators (MMA). By executing deep learning, vector operations, and graph analytics locally without requiring GPUs or outbound cloud connectivity, data never leaves the controlled, on-premises enclave.
Air-Gapped & Sovereign Data Operations: Eliminates exposure to external network attack vectors and ensures compliance with strict data residency regulations.
Deterministic & Auditable Context: Rather than feeding raw, unverified internet data to LLMs, KGNN ingests and unifies structured and unstructured enterprise data into a self-constructing knowledge graph.\
Preventing Data Poisoning & Hallucinations: In Retrieval-Augmented Generation (RAG) pipelines, KGNN provides verifiable lineage and provenance. Every piece of context passed to an LLM or decision-making system has explicit audit trails, preventing malicious input manipulation or adversarial data attacks from corrupting model outputs.
Multi-Source Cyber Threat Correlation: ARCXA and the Equitus Fusion platform ingest, cross-reference, and correlate logs, network traffic, endpoints, and threat feeds across disparate systems.
Autonomous Relationship Discovery: By placing network event logs into a knowledge graph structure, KGNN automatically flags anomalous connections, lateral movement, or unauthorized access attempts across the enterprise infrastructure in real time.
2. Physical Security: Equitus Video Sentinel (EVS)
Operates as a computer vision tactical inference engine: Physical security for data centers housing IBM Power10/Power11 servers is critical, as physical breach bypasses all cyber controls.
- Real-Time Edge Visual Inference: EVS runs imagery models directly on the IBM Power edge servers without streaming sensitive video feeds to external cloud providers.
- Autonomous Threat Detection: EVS converts physical security camera networks into active sentinels capable of detecting perimeter breaches, loitering, unauthorized entry into server rooms, object removal, or suspicious physical anomalies.
- Forensic Metadata Indexing: EVS extracts enriched metadata (such as motion patterns, entity classifications, and time-stamped attributes) from raw feeds. Security teams can instantly search historical video archives using semantic queries to investigate physical security incidents.
3. IBM Power10/Power11 Hardware Security Synergies
Deploying the Equitus suite on IBM Power creates a hardware-enforced security posture:
Summary Architectural Flow
[ Physical Perimeter / Cameras ] ──> Equitus Video Sentinel (EVS) ──┐
├──> [ IBM Power10/11 Server Enclave ]
[ Corporate Data & Logs / IT ] ──> KGNN / ARCXA Knowledge Graph ──┘ (On-Prem / Edge MMA Acceleration)
│
▼
Zero-Trust Cyber & Physical Response
By unifying EVS for physical situational awareness with KGNN and ARCXA for semantic cyber threat correlation and safe AI processing—all hosted natively on IBM Power10/11 hardware—organizations achieve a fully localized, cloud-independent security architecture that insulates AI and enterprise data against external exploitation.

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