IBM-EVS-ASC - Cost Risk Security

"IBM EVS PRODUCES COMPELLING ECONOMIC ARGUEMENTS BASED ON ROI/TCO"

Equitus EVS can help enterprises improve the ROI of existing IBM Power10/11 investments by running video intelligence close to the data, reusing camera infrastructure, and connecting physical-security events with enterprise context. 

IBM EVS - business case is to reduce cloud processing and video-transfer costs while improving investigation speed and audit evidence—not to lower security or compliance standards. 

IBM  EVS runs natively on Power10/11 using Matrix Math Accelerator technology, without requiring GPUs or cloud services, and is tuned for RHEL and OpenShift allowing for security and cost saving benefits.

Turn your IBM Power10/11 investment into an enterprise security-intelligence platform. Equitus EVS adds real-time video analytics to existing camera networks, keeps sensitive processing on premises, and creates searchable event evidence. Connect those events to enterprise data to improve response and investigations—with deployment costs and operating savings measured before expansion

[IBM EVS - AI ENABLED VIDEO STREAMS]

Equitus Intelligent Ingestion Platform — integrating EVS (Equitus Video Sentinel), KGNN (Knowledge Graph Neural Network), ARCXA (AI-agent framework/orchestration), FUSION (cross-domain data harmonizer), and ICAM (Identity, Credential, & Access Management)—turns IBM Power10 and Power11 into cost-saving, highly secure physical/cyber threat intelligence engines.

EVS intended result is to assemble one contextualized incident rather than four disconnected alerts.  The integration of Multi-Modal Video models can generate an intelligence layer to reduce false positives and superior SIEM services.

EVS makes the result: traceable, require timestamps, source identifiers, links to retained evidence, model/version information, correlation rules, and operator actions in the acceptance criteria. Local EVS event logging and KGNN provenance provide relevant foundations, but the end-to-end evidence chain still needs testing.equitus+1

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IBM EVS deploys this modular architecture via a SaS/fixed-fee service model on Red Hat Enterprise Linux (RHEL) and OpenShift allows enterprise leaders to maximize IBM infrastructure return on investment (ROI) across four financial and operational pillars:

1. Eliminating GPU Infrastructure & Cloud Egress Costs (SparQL + Power MMA)

• ROI Driver: Public cloud AI services and external GPU clusters create massive ongoing costs due to bandwidth consumption, cloud egress fees, and hardware procurement.

• How It Works: EVS ingests raw, multi-stream video feeds and uses IBM Power10/Power11 Matrix Math Accelerators (MMA) to perform on-chip ML inference—completely eliminating the need for expensive dedicated GPUs.

• SparQL & Graph Intelligence: EVS converts video frames into structured visual entities and maps them into the KGNN schema-less graph database. With SparQL querying, security teams perform real-time, complex graph searches (" (S)Find person X - (P) who accessed Door Y after hours across (O) 50 camera feeds") directly against local graph data.

• Financial Impact: Cuts recurring cloud egress costs to zero by processing, graph-indexing, and querying 100% of video data locally on-premises.

2. Traceable Deterministic AI with ARCXA & KGNN

• ROI Driver: "Black box" AI produces false positives that waste hundreds of labor hours in manual investigation, creating audit vulnerabilities in highly regulated environments.

• How It Works: ARCXA orchestrates production-grade AI agents across ONNX, YOLO, and SAM multimodal runtimes. Instead of isolated outputs, ARCXA routes detections directly into KGNN, creating a fully explainable, rule-and-graph-backed data lineage (provenance).

• Financial Impact: Delivers 100% data auditability and full explainability. Compliance teams reduce audit preparation overhead and eliminate risk penalties tied to CJIS, NERC CIP, CFATS, and HIPAA standards.

3. Integrated Cyber/Physical Security (FUSION + ICAM) - Connects Multi-Modal Computer Vision Models with Core Stack

• ROI Driver: Cyber and physical security systems usually operate in isolated silos, requiring separate software licenses and distinct operational teams.

• How It Works: Equitus FUSION bridges physical surveillance (EVS) with enterprise enterprise access control and identity systems (ICAM).

  • Example: If an ICAM system logs a physical badge tap at a restricted door while EVS detects an unauthorized entry via computer vision, FUSION flags an immediate identity spoof/tailgating alert.

• Financial Impact: Replaces fragmented security point solutions with a single integrated ingestion fabric, reducing software licensing overhead and unifying threat handling.

4. OpenShift Orchestration & Fixed-Fee Predictability

• The ROI Driver: Software deployment and integration costs frequently balloon far past initial budget projections due to custom integration coding.

• How It Works: Delivered on RHEL and Red Hat OpenShift, EVS + KGNN deploy as containerized microservices optimized for Power10/Power11 architecture. Combined with fixed-fee installation packages and free core software components, organizations avoid trial-and-adoption integration risks.

• Financial Impact: Enables rapid plug-and-play rollout with predictable implementation costs and zero software sprawl.

Executive ROI Summary - The economic value of EVS stretches across the entire enterprise:

Operational Challenge	Traditional Approach	EVS on IBM Power11 / OpenShift	ROI Business Impact
Compute Hardware	High-cost discrete GPU racks	Power11 native MMA acceleration	Reduced CAPEX & lower power/cooling bills
Camera Upgrades	Replace legacy cameras	Intelligent ingestion layer on existing VMS	Maximized life of legacy assets
Operational Labor	Manual multi-screen watching	Auto-indexing + multimodal real-time alerts	50%+ reduction in video review hours
Data Security / Cloud	Cloud AI bandwidth/egress fees	On-prem sovereign processing	Zero cloud egress costs & full audit compliance

EVS supplies the visual intelligence; KGNN assembles the enterprise data-unification layer. Those roles align with Equitus’s published product descriptions, but the complete workflow above is a proposed integration—not a claim that every connector is available out of the box.

Component	Role in the proposed solution	Important boundary
EVS	Convert video into detected objects, behaviors, alerts, and searchable event metadata.	This is the video-intelligence layer, not by itself a complete cyber/physical correlation platform. equitus
Equitus KGNN	Unify structured and unstructured enterprise data and preserve source context and provenance.	Equitus describes these capabilities, but individual source connectors and correlation workflows require validation. equitus
SPARQL	Query relationships in an RDF knowledge graph—for example, relationships among events, assets, locations, and identities.	SPARQL is a query standard, not a video engine. Confirm that the selected graph platform exposes a supported SPARQL endpoint; the reviewed Equitus pages do not establish that integration. w3
IBM Power10/11	Supply on-premises compute for supported EVS inference workloads.	Equitus advertises GPU-free operation; actual capacity depends on the models, video feeds, and service levels being deployed. equitus
RHEL / OpenShift	Provide the operating and application-platform foundation for supported deployments.	IBM documents Equitus on Power10 with OpenShift; confirm the exact Power11, OS, container-image, and application versions in the proposed configuration.

Equitus’ offers Open Source Arcxa as Free Software / Fixed-Fee SaS model, SPARQL knowledge-graph capabilities, IBM Power11, and Red Hat OpenShift transforms high-volume video streams into structured, audit-ready data while slashing total cost of ownership (TCO).

1. Zero Software Capex + Predictable Fixed-Fee Operations

• The ROI Mechanism: Traditional enterprise video analytics software charges per-channel licensing fees that scale aggressively as camera networks grow. Equitus provides the core EVS platform for free and charges a fixed-fee service model for support, maintenance, and deployment.

• Impact on IBM Power11 Investment: Rather than tying up capital in software licenses, your budget goes directly toward maximizing local hardware utilization. Power11’s dense compute handles more streams per node, keeping fixed service tiers predictable as you scale.

2. SPARQL Knowledge Graph & Metadata Abstraction

• Eliminating Heavy Cloud Video Uploads: Standard cloud analytics tools stream raw, high-resolution video to the public cloud, resulting in massive cloud bandwidth, storage, and egress bills.

•  SPARQL + EVS : EVS uses multimodal models (YOLO, SAM, JEV) on-premises via Power11 and RHEL/OpenShift to extract entities, actions, and temporal events from raw video instantly. It converts these extractions into lightweight RDF triplets (Subject–Predicate–Object, (S)<Camera_12> (P)<detected_intrusion> (O)<Vehicle_A> at <Timestamp>).

• SPARQL Query Efficiency: Analysts run precise, complex SPARQL queries across the entire security enterprise (e.g., "Find all instances where unauthorized personnel entered Zone B after 10 PM without a badge").

• Impact: Only tiny metadata queries and specific relevant clips ever touch the cloud or central server, dropping data transport and cloud storage costs by up to 80%–90%.

Feature / Capability	Traditional Cyber & Physical Setup	EVS on Power11 + OpenShift + SPARQL	Business & Financial ROI
Threat Intelligence	Disconnected video logs and IT SIEM dashboards	SPARQL graph fusion correlating physical entry with digital access	Stops multi-vector insider threats and physical breaches early
Cloud & Bandwidth Costs	Streaming raw video to public cloud AI	On-prem Power11 metadata extraction + SPARQL RDF output	80%+ reduction in cloud ingress/egress & storage fees
Software Cost Structure	Per-camera software licensing scale	Free EVS Software + Fixed-Fee SaaS Service Package	Predictable budget scaling; zero per-camera software taxes
System Orchestration	Fragmented server setups and separate GPU appliances	RHEL / OpenShift container management across Power11 compute	Maximized server density and dramatically reduced power/cooling
Audit & Forensic Review	Scrubbing raw video manually alongside IT logs	Automated, linked-data graph query auditing	Cuts forensic incident response and audit prep time by up to 90%