Equitus.AI ArcXA with Cyberspatial Teleseer (ACT)
AIMLUX.ai Proposes: Physical/Cyber Security - "Intelligent Digital Twin" : Equitus.AI ArcXA with Cyberspatial Teleseer (ACT), AWS and AMI (Amazon Machine Image) users can move beyond simple cloud monitoring into Full-Stack Semantic Traceability.
ACT integration bridges the gap between the "logical" code residing inside your AMIs and the "physical" network traffic flowing through your VPCs.
1. How the ACT Integration Works
The ACT combination creates a Semantic Mapping Layer that connects code-level data with network-level behavior.
ArcXA (The Intelligence Layer): Arcxa uses a Knowledge Graph Neural Network (KGNN) to ingest software blueprints and codebases. It understands the intent of your software—identifying API endpoints, data schemas (like PII), and logical service dependencies inside your AMIs.
Teleseer (The Terrain Layer): Teleseer performs passive packet analysis (PCAP) to map the actual network topology. It sees the reality of the network—every EC2 instance, flow log, and "Mission Relevant Terrain" (critical assets).
The Traceability Layer
When ArcXA’s logical graphs are overlaid on Teleseer’s network maps, you get a unified graph where:
AMI Code Symbols are linked to...
Running EC2 Instances which are linked to...
Real-time Traffic Flows (identified by Teleseer)*.
*Cyberspatial's Teleseer is a network visibility and analysis tool that maps and discovers network attack surfaces using packet capture analysis — scanless, agentless, and running entirely in the browser. Cyberspatial It identifies 6,000+ protocols and apps and reconstructs network topology automatically from PCAP data ideally suited for AXS Users.
2. Benefits for AWS & AMI Users
A. "Deep" Zero-Trust Validation
Most AWS users rely on Security Groups and NACLs, which only look at IP/Port combinations. This layer allows you to validate if the actual code intent matches the traffic.
The Benefit: If an AMI was built to only send encrypted logs to a specific S3 bucket, but Teleseer detects it communicating with an unknown external IP, the system flags a "Semantic Violation" rather than just a network alert.
B. Automated AMI Compliance & Provenance
For users managing hundreds of AMIs, tracking what data each image is authorized to handle is a nightmare.
The Benefit: The semantic layer creates a "living BOM" (Bill of Materials). You can query: "Which of my running AMIs are currently processing PII data across the VPC peering link?" The system traces the PII schema from the code (Arcxa) to the live traffic flows (Teleseer).
C. Radical Incident Response (IR) Speed
When a GuardDuty alert triggers in AWS, an analyst usually spends hours tracing which instance is affected and what that instance actually does.
The Benefit: With this layer, you click an anomalous node in Teleseer’s network map and instantly see the Arcxa-derived intelligence: "This instance is running AMI-v2.1, which contains the 'Auth-Service' module currently under a CVE-2026-X threat."
D. Mission Impact Analysis for Hybrid Cloud
Many Teleseer users operate in "combat-born" or edge environments where AWS Outposts or Local Zones are used.
The Benefit: If a specific network path is degraded, the layer provides a Mission Impact report. It tells you exactly which software functions (e.g., "Target Acquisition" or "Payroll Processing") are offline based on the network outage, rather than just listing "EC2 instance unreachable."
3. Summary Table: Why it Matters to AWS Users
ARCXA is described as "mapping intelligence for enterprise data migrations: schema mapping, lineage, and transformation traceability that compounds across every project." The GitHub repo shows it's a modular stack with arcxa-core, arcxa-coordinator, arcxa-model-service, arcxa-shard, a Python SDK, a frontend, and Kubernetes/Helm deployment — meaning it's built to run as a containerized service, which maps cleanly onto AWS deployment patterns.
Teleseer is a PCAP-based network visibility and analysis tool that provides mapping, discovery, and validation of your network attack surface — scanless, agentless, and zero-hardware.
The combination creates something neither does alone: ARCXA understands what your data is and where it came from, Teleseer understands what your network looks like and what's traversing it. Together they form a semantic mapping and traceability layer — every EC2 instance, VPC flow, and data pipeline becomes a typed, traceable node in a knowledge graph you can query.
The core mechanic: what ACT actually does
ARCXA sits above an existing ETL stack and makes every migration and pipeline explainable without touching the pipeline. On AWS, that means ARCXA observes Glue jobs, Lambda transforms, and EMR pipelines as a non-invasive observer — capturing schema maps and transformation lineage. Meanwhile, Teleseer is scanless, agentless, and zero-hardware, making it ideal for deployment via AWS VPC Traffic Mirroring, which routes packet copies to a Teleseer instance without touching live traffic flows.
KGNN automatically ingests, structures, and augments raw data — transforming it into a semantically rich, machine-readable format optimized for AI processing and RAG pipelines — eliminating manual ETL, schema design, and mapping, cutting time to ingest and build knowledge graphs by up to 80%. The combined output is a unified graph where every EC2 instance has both a network identity (from Teleseer) and a data lineage identity (from ARCXA).
The six AWS AMI beneficiary segments
Government / defense (GovCloud): These customers already have explicit MRT-C requirements. Equitus KGNN automates real-time analysis of diverse defense data, fusing reports, sensors, and legacy systems into a unified knowledge graph, delivering instant actionable insights for mission-critical decisions without cloud reliance. On GovCloud AMIs, ACT provides the audit-ready traceability that FedRAMP and MRT-C demand.
Healthcare / life sciences: HIPAA requires that every field containing PHI be traceable end-to-end. Equitus' KGNN is a fully automated graph engine that ingests, cleans, and structures untapped data from any source — PDFs, logs, chats, SQL, and more — into a dynamic, explainable knowledge graph within hours. For a hospital running analytics AMIs on AWS, that means an automatically maintained PHI lineage map without a team of data stewards.
Financial services: SOX and PCI-DSS require that schema changes be documented and their downstream impact understood before deployment. Lineage makes dependencies visible before changes cause problems and root causes traceable when they do — before modifying a pipeline or altering a schema, engineers can see every dashboard, model, and process that depends on the data they're about to change. ARCXA makes that automatic.
AWS Marketplace ISVs: Any vendor publishing an AMI containing data processing software can bundle ACT as a provenance layer — turning traceability into a differentiating product feature rather than a compliance afterthought. Organizations are recognizing that they need a single, comprehensive view that aggregates passive network data, active scanning, and insights from existing security tools — all normalized and contextualized in one place. An AMI that ships with ACT pre-integrated delivers that from day one.
DevSecOps / platform teams: An AMI can be used to launch multiple new instances, all with the same underlying configurations — customized AMIs enable organizations to pre-install required packages, implement security controls for all instances at once, and launch production-ready machines quickly. ACT running at the VPC level catches drift between what an AMI was configured to do and what instances are actually doing on the network.
AI / ML teams on SageMaker and Bedrock: AI accountability mandates clear documentation about the source of data fed into models — with lineage tools, every pipeline's dataset transformation and decision point is traceable, so stakeholders can gain insight into how a model was built and implemented, making it more defensible during audit or public scrutiny. ARCXA provides exactly that lineage for training data pipelines, while Teleseer maps the network traffic patterns of inference endpoints.
The deployment path is clean: both Teleseer and ARCXA are containerized (ARCXA's GitHub repo shows Docker, Kubernetes, and Helm chart support), meaning an arcxa-teleseer-act AMI on the AWS Marketplace is a straightforward packaging play — click to deploy, immediate semantic terrain visibility.
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III. INTEGRATION POINT
The AWS-native integration point: VPC Traffic Mirroring → S3 → Teleseer → ARCXA
AWS VPC Traffic Mirroring is a VPC feature that lets you copy network traffic from an elastic network interface of an EC2 instance and send it to a target storage service for analysis — including exporting packet captures (PCAPs) to a centralized S3 bucket. AWS This is the unlock. AWS already generates the raw network telemetry; Teleseer and ARCXA together turn it into governed, semantic knowledge.
VPC Traffic Mirroring can be used in multi-account AWS environments, capturing traffic from VPCs spread across many AWS accounts and routing it to a central VPC for inspection. AWS That means a large enterprise with dozens of AWS accounts can funnel all their traffic telemetry into a single hub where Teleseer processes it and ARCXA governs it.
Three-stage pipeline looks on AWS specifically:
Stage 1 — capture. You can think of VPC Traffic Mirroring as a "virtual fiber tap" that gives you direct access to the network packets flowing through your VPC — you can choose to capture all traffic or use filters to capture packets of particular interest, with an option to limit the number of bytes captured per packet. AWS These PCAPs land in S3.
Stage 2 — discover and map. Teleseer is the fastest, most powerful way to analyze any network from packet captures — passive asset discovery with no agents, scanning, or appliances, finding unmanaged devices and shadow IT that other tools miss. Cyber Security Intelligence In an AWS context, "shadow IT" means unregistered EC2 instances, rogue Lambda endpoints calling external services, or ECS containers making unexpected lateral connections — the things CloudTrail and VPC Flow Logs don't fully expose.
Stage 3 — semanticize and govern. ARCXA registers the Teleseer-derived topology as a governed data source, maps raw AWS assets (EC2 instance IDs, ENI addresses, VPC IDs, security group memberships) to business ontology terms, and maintains full lineage — so when something changes in your AWS environment, you know exactly what downstream AI pipelines, datasets, and workflows are affected.
AWS reference architecture:
AWS Security Hub / GuardDuty users get a semantic blast-radius engine. Today, GuardDuty can tell you "this EC2 instance is behaving suspiciously."
Missing? What it cannot tell you is which SageMaker training jobs consumed data from that instance, which Glue ETL pipelines flowed through it, and which downstream S3 datasets are now potentially compromised. ARCXA's lineage graph answers exactly that, making every GuardDuty finding actionable in minutes rather than hours of manual tracing.
SageMaker and Bedrock users get model data provenance that regulators will actually accept. Incorporating AI governance into an organization's AI strategy is instrumental in building trust, enabling the deployment of AI technologies at scale — AI governance frameworks create consistent practices to address organizational risks, ethical deployment, data quality and usage, and regulatory compliance.
AWS ARCXA makes that concrete: every SageMaker training run can be traced back through the data transformation chain, through the network topology that carried it, all the way to the originating source — with cryptographic lineage anchors, not just log files.
AWS Lake Formation and Glue users gain a semantic layer that Lake Formation's own catalog lacks: the ability to map dataset columns not just to IAM permissions, but to business ontology terms with full transformation history.
When a Glue job reshapes a table, ARCXA records the field-level lineage. When Teleseer detects anomalous traffic to that data source, the risk flows automatically into the governance graph.
AMI / Marketplace ISVs building security or data products have a compelling integration story: package ARCXA and a Teleseer ingest connector together as a Marketplace AMI, and any customer who launches it immediately gets a governed network intelligence layer standing up against their existing VPC traffic mirror sessions — the Mountpoint for Amazon S3 can be used to mount a centralized bucket and access it as a local file system, providing optimized high-throughput performance for storing and accessing generated PCAPs. AWS That's the storage bridge between AWS's native capture infrastructure and the analysis stack.
Multi-account enterprise AWS users (AWS Organizations) get something nobody else currently offers: a single semantic map of every asset across all accounts, with lineage showing exactly how data moves between account boundaries — critical for FedRAMP, HIPAA, PCI-DSS, and SOC 2 audits where cross-account data flows are the hardest thing to document.
The practical AMI packaging path would be: ARCXA coordinator + shard + model service deployed as a single EC2 launch template, pre-configured with an S3 event notification listener that triggers Teleseer analysis whenever new PCAPs land, and an ARCXA workflow that automatically ingests the Teleseer topology export into the governance graph.
A customer with VPC Traffic Mirroring already enabled would have a working semantic traceability layer running within an hour of launching the AMI.
AWS Security Hub / GuardDuty users get a semantic blast-radius engine. Today, GuardDuty can tell you "this EC2 instance is behaving suspiciously."
Missing? What it cannot tell you is which SageMaker training jobs consumed data from that instance, which Glue ETL pipelines flowed through it, and which downstream S3 datasets are now potentially compromised. ARCXA's lineage graph answers exactly that, making every GuardDuty finding actionable in minutes rather than hours of manual tracing.
SageMaker and Bedrock users get model data provenance that regulators will actually accept. Incorporating AI governance into an organization's AI strategy is instrumental in building trust, enabling the deployment of AI technologies at scale — AI governance frameworks create consistent practices to address organizational risks, ethical deployment, data quality and usage, and regulatory compliance.
AWS ARCXA makes that concrete: every SageMaker training run can be traced back through the data transformation chain, through the network topology that carried it, all the way to the originating source — with cryptographic lineage anchors, not just log files.
AWS Lake Formation and Glue users gain a semantic layer that Lake Formation's own catalog lacks: the ability to map dataset columns not just to IAM permissions, but to business ontology terms with full transformation history.
When a Glue job reshapes a table, ARCXA records the field-level lineage. When Teleseer detects anomalous traffic to that data source, the risk flows automatically into the governance graph.
AMI / Marketplace ISVs building security or data products have a compelling integration story: package ARCXA and a Teleseer ingest connector together as a Marketplace AMI, and any customer who launches it immediately gets a governed network intelligence layer standing up against their existing VPC traffic mirror sessions — the Mountpoint for Amazon S3 can be used to mount a centralized bucket and access it as a local file system, providing optimized high-throughput performance for storing and accessing generated PCAPs. AWS That's the storage bridge between AWS's native capture infrastructure and the analysis stack.
Multi-account enterprise AWS users (AWS Organizations) get something nobody else currently offers: a single semantic map of every asset across all accounts, with lineage showing exactly how data moves between account boundaries — critical for FedRAMP, HIPAA, PCI-DSS, and SOC 2 audits where cross-account data flows are the hardest thing to document.
The practical AMI packaging path would be: ARCXA coordinator + shard + model service deployed as a single EC2 launch template, pre-configured with an S3 event notification listener that triggers Teleseer analysis whenever new PCAPs land, and an ARCXA workflow that automatically ingests the Teleseer topology export into the governance graph.
A customer with VPC Traffic Mirroring already enabled would have a working semantic traceability layer running within an hour of launching the AMI.
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