BlueprintPage 29
29AI SECURITY
Context, Memory & Retrieval Security
Agent memory can outlive a session; treat it as governed enterprise data, not harmless chat history.
- Priority
- P1
- Phase
- Phase 2
- Primary owner
- AI Platform / Security
- Status
- Blueprint
Objective
Agent memory and retrieval systems can become hidden persistence layers for sensitive data, malicious instructions and stale authority assumptions. Agent Trust Cloud should make memory stores and retrieval sources visible and governable.
What to build
- Inventory vector stores, knowledge bases, conversation memory and retrieval connectors associated with agents.
- Tag memory by tenant, user, data class, retention and provenance.
- Control cross-user or cross-agent memory access.
- Detect persistent malicious instructions or secrets written into memory.
- Enforce retention and deletion workflows.
Implementation decisions
- Separate business facts from executable instructions where architectures allow it.
- Include provenance metadata in retrieved chunks and propagate it to policy/risk engines.
- Do not persist raw secrets or high-risk tokens in long-term memory.
- Revalidate access at retrieval time rather than assuming ingestion-time permission is sufficient.
Definition of done
- Memory access is identity- and policy-aware.
- Deleted/expired source data can be removed from derived memory stores under defined workflows.
- Security can identify which retrieval source influenced a risky action.
Success metrics
- Memory stores inventoried
- Unauthorized retrieval blocks
- Sensitive memory findings
- Deletion SLA
RISK
Agent memory can outlive a session; treat it as governed enterprise data, not harmless chat history.