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Data, RAG & Knowledge Pipeline Assurance

Security and quality assurance for the data, retrieval, memory, embeddings, and context that shape what an AI system knows and trusts.

The objective

Protect the intelligence entering the model—because compromised context can redirect an otherwise well-defended system.

We evaluate how source data is selected, transformed, labeled, embedded, retrieved, ranked, cached, remembered, and presented to models. The review follows provenance and authority from original evidence through every derived representation.

Testing covers poisoning, contamination, stale or conflicting context, hidden instructions, cross-tenant leakage, weak retrieval controls, unsafe document ingestion, and evaluation blind spots. The result is a knowledge pipeline that is measurable, explainable, and recoverable.

Often requested as

RAG security assessmentVector database securityKnowledge-base hardeningData poisoning reviewAI data governance

Assessment surface

What we cover

01Data eligibility, provenance, licensing, and lineage
02Ingestion, parsing, chunking, embedding, and indexing controls
03RAG poisoning, hidden instruction, and retrieval manipulation
04Vector and embedding authorization and tenant isolation
05Context ranking, freshness, contradiction, and source authority
06Agent memory write, retrieval, retention, and deletion boundaries
07Contamination, deduplication, split, and benchmark leakage review
08Retrieval quality, groundedness, and citation evaluation

The handoff

What you receive

01End-to-end knowledge and context data-flow map
02RAG, memory, and vector-store risk assessment
03Provenance and access-control requirements
04Poisoning and retrieval-quality test suite
05Hardening, monitoring, and recovery plan

Designed outcomes

Trusted context lineagePoisoning resistanceStronger retrieval qualityRecoverable knowledge systems

How it works

Trace the evidence

Follow source material through ingestion, transformation, storage, retrieval, and final context assembly.

Test trust

Challenge provenance, permissions, poisoning resistance, freshness, isolation, and retrieval behavior.

Measure quality

Evaluate retrieval, groundedness, context conflicts, failure cases, and benchmark contamination.

Harden the pipeline

Define controls, monitoring, lineage, re-indexing, rollback, and incident recovery procedures.

Operating boundary: All work is performed within explicitly authorized scope. High-risk actions remain human-approved, and findings are communicated with evidence, uncertainty, and practical remediation context.

Research-driven security

Built for attack surfaces traditional security models were not designed to see.

Aetherward’s assessment methods are informed by continuous internal research into behavioral attack chains, legitimate-tool abuse, permission composition, cross-tool escalation, context manipulation, model-to-tool boundary failures, poisoning, and abnormal agent behavior.

Explore Aetherward research ↗

Additional capabilities

Fixed-scope or project-based

Security Automation Engineering

Custom security tooling for teams that need specialized automation without building a full internal platform.

Triage automationDetection toolingScope-aware scannersEvidence collectionThreat-intelligence workflowsAnalyst tooling

Recurring engagement

Ongoing AI Security & Architecture Advisory

Independent review as models, data, integrations, vendors, threats, and business requirements change.

Architecture reviewIntegration reviewThreat updatesRelease gatesSecurity driftDecision support

Building or deploying an AI system?

Assess it. Secure it. Build it. Repair it. ↗