In the Aimlux.ai ecosystem, Graphixa is the "Final Mile" of data movement. While Teleseer provides the raw packet and Fusion provides the semantic meaning, Graphixa uses Triple Logic to build an unbreakable audit chain called "Semantic Lineage."
In a standard migration, you have a source row and a destination row, but the "logic" of the move is often a black box of scripts. In the Aimlux.ai stack, the move itself is recorded as a series of immutable Semantic Facts (Triples).
1. The "Proof of Transit" Triple Chain
Graphixa doesn't just move data; it generates a "Digital Receipt" for every record. This receipt is a chain of triples that links the destination record back to the physical wire.
|
Subject (The Entity) |
Predicate (The Action) |
Object (The Evidence) |
|
Record_123_SAP |
wasGeneratedFrom |
Packet_456_PCAP |
|
Packet_456_PCAP |
wasVerifiedBy |
Network_Eye_Sensor |
|
Record_123_SAP |
containsMapping |
Oracle_to_SAP_Logic_v1 |
|
Oracle_to_SAP_Logic_v1 |
isGovernedBy |
Regulatory_Policy_XYZ |
Why this is "100% Complete"
Because every triple is a First-Class Citizen with a unique URI, there are no gaps. If an auditor asks, "Why is this salary $100k?" Graphixa can traverse the triple logic:
Target: View the record in SAP.
Lineage: Follow the
:wasGeneratedFromtriple to the Fusion Knowledge Graph.Ground Truth: Follow the
:wasVerifiedBytriple to the exact Teleseer PCAP timestamp that proves the data left the source database on port 1521.
2. Deterministic Mapping: The "Zero-Loss" Guarantee
Traditional ETL relies on "Heuristic" mapping (guesses based on column names). Graphixa uses Deterministic Mapping powered by the Triple Ontology.
Standard Migration: Column "EMP_ID" moves to "PersonnelNum." You hope the script worked.
Graphixa (Triple Logic): Graphixa validates the Semantic Identity. It doesn't just move "EMP_ID"; it identifies that the Subject (the Employee) has an Attribute (ID) that is logically the same across both systems. If the packet captured by Teleseer shows a 10-digit number but the target only accepts 8, the Triple Logic flags a Semantic Mismatch before the move ever happens.
3. The "Closed-Loop" Audit Cycle
Graphixa closes the loop between the "Data-in-Motion" and "Data-at-Rest" by comparing two distinct sets of triples:
Source Triples: Generated by Teleseer/Network Eye (The physical reality).
Target Triples: Generated by Graphixa upon ingestion (The new reality).
The Result: If the two graphs do not align perfectly, Graphixa fails the migration for that record. This is why Aimlux.ai can guarantee a "Clean Core"—it is mathematically impossible for a record to exist in the destination without a corresponding verified triple from the source.
Summary of the Aimlux.ai Advantage
By using Triple Logic, Graphixa moves enterprises from "Sample-based Testing" to "100% Deterministic Verification."
Teleseer: Provides the Nervous System (The Packet).
Fusion: Provides the Brain (The Meaning).
Graphixa: Provides the Muscle and Memory (The Movement and the Audit).
The "Triple" Query (SPARQL/Semantic Example)
Instead of searching for a line, we search for a Logical Inconsistency:
SELECT ?account ?action
WHERE {
?account rdf:type :ServiceAccount ;
:hasPurpose :AutomatedTask .
?action rdf:subject ?account ;
rdf:predicate :InteractiveLogin ; # The contradiction
:target ?sensitiveHost .
}
In a 2D graph, :hasPurpose would be a hidden text string. In a Triple, it is a Logical Rule that the AI uses to automatically block the movement before it reaches the data.
To demonstrate the difference, let’s look at a "Slow and Low" lateral movement scenario: an attacker has compromised a low-level service account and is using it to perform an unusual sequence of logins to reach a sensitive Database server.
1. The 2D Property Graph Approach (Structural Matching)
A 2D property graph (like Neo4j) relies on explicit paths. You have to know exactly what pattern to look for.
The Query: "Find any User who logs into Host A and then Host B within 1 hour."
The Flaw: If the attacker waits 61 minutes, or uses a legitimate service account that normally logs into many hosts, the 2D query returns thousands of false positives or misses the threat entirely because the "Edge" (the login) looks legal on paper.
2. The "Triple" Logic Approach (Semantic Inference)
In a Triple-based KGNN, the login isn't just an edge; it’s a series of semantic facts that the system can "reason" about using an ontology.
The "Triple" Logic Chain:
Fact 1:
Service_Account_X$\rightarrow$hasRole$\rightarrow$Batch_Backup_JobFact 2:
Batch_Backup_Job$\rightarrow$typicallyAccesses$\rightarrow$Storage_VLANFact 3 (From Network Eye):
Service_Account_X$\rightarrow$initiates_RDP$\rightarrow$Domain_Controller
Why the KGNN Catches It:?
The Knowledge Graph Neural Network performs "Semantic Reasoning" across these triples. It doesn't just look for a path; it identifies a Semantic Contradiction:
The ontology knows that a
Batch_Backup_Job(Fact 1) should only talk toStorage(Fact 2).
When it sees an
RDPconnection (Fact 3), the KGNN calculates a Semantic Distance between "Automated Backup" and "Interactive Remote Desktop."
The Inference: Even though the login is "valid," the intent is a 99% mismatch for the account's defined semantic purpose.
Comparison: Identifying the Threat
|
Threat Indicator |
2D Property Graph Detection |
KGNN Triple Logic Detection |
|
Credential Misuse |
Misses it (login is valid/authorized). |
Flagged:
"Backup Account performing Admin task." |
|
Time-Delayed
Hopping |
Misses it (outside
the hard-coded time window). |
Flagged: Semantic state
of "compromised" persists regardless of time. |
|
Living-off-the-Land |
Misses it (uses legitimate tools like PowerShell). |
Flagged:
The Predicate (Action) contradicts the Subject's (User)
ontology. |
