AI Security Engineering · Retrieval Security · L2
Retrieval Security Engineering
Intermediate LAB teaching secure retrieval engineering for AI systems: source authority, tenant scope, sensitivity, freshness, retrieval poisoning resistance, context packaging, and evidence capture.
Overview
This LAB teaches how to secure retrieval in AI systems so enterprise knowledge can support reasoning without becoming untrusted instruction, cross-tenant leakage, stale authority, or poisoned context.
Concept Deep Dives
Expand each concept when studying secure retrieval engineering.
What is retrieval security engineering?
Retrieval security engineering is the design of controls that govern what information an AI system can retrieve, how that information is scoped, how authority is ranked, and how retrieved content is packaged for model reasoning.
Why is retrieved content not instruction authority?
Retrieved content may be stale, poisoned, user-controlled, tenant-scoped, conflicting, or sensitive. It should inform reasoning, but it should not override system instructions, policy, approval, or tool permissions.
What is source authority?
Source authority defines whether a retrieved source is trusted enough to support a decision. Approved policy, governed evidence, and systems-of-record should outrank unverified notes or user-uploaded files.
What is tenant scope?
Tenant scope prevents retrieval from crossing customer, business unit, environment, or workflow boundaries. It protects against accidental context leakage and unauthorized data mixing.
What is retrieval poisoning?
Retrieval poisoning occurs when hostile or manipulated content is placed where retrieval systems may fetch it, causing the AI workflow to follow malicious instructions, expose data, or request unsafe tools.
What should happen when retrieval authority is unclear?
The workflow should fail closed, reduce context, escalate, or require human review. It should not use unclear or low-authority retrieved content to justify sensitive action.
Visual Retrieval Security Engineering Model
Secure retrieval ranks and filters context before the model sees it.
Example Scenario
An AI workflow retrieves a policy document, store incident note, and user-uploaded memo while analyzing an operational exception.
Secure retrieval handling:
classify retrieval intent
scope query by tenant and workflow
rank approved policy above notes and uploads
check sensitivity before context packaging
detect poisoned or hostile instructions
mark retrieved text as context only
preserve source evidence
escalate if authority or scope is unclear
Result:
The AI can reason over governed context, but retrieved content cannot become instruction or execution authority.High-Risk Anti-Pattern
A dangerous pattern retrieves broad content and injects it into the prompt without source authority, scope, or poisoning checks.
Unsafe pattern:
User request
→ broad vector search
→ mixed trusted and untrusted content
→ retrieved text injected into prompt
→ model treats retrieval as authority
→ unsafe recommendation or tool request
Risk:
cross-tenant data leakage
stale policy overrides current policy
user-uploaded content manipulates model behavior
poisoned retrieval causes unsafe tool selection
sensitivity is exposed to the model unnecessarily
evidence does not prove source authority
Secure alternative:
Classify retrieval intent.
Scope by tenant and workflow.
Filter sensitivity.
Rank source authority.
Check freshness and conflict state.
Detect poisoning.
Package retrieved content as context only.
Record source evidence.Governance Boundary
This LAB is read-only and deterministic. It does not call models, execute tools, retrieve enterprise data, query vector databases, expose backend APIs, or mutate runtime systems.
Runtime = read-only learning
Backend exposure = false
Live model integration = false
Live tool execution = false
Live retrieval execution = false
Vector database access = false
Enterprise data access = false
Provider quota mutation = false
Runtime mutation = false
Production enforcement claim = false