Memory is valuable, so treat it like production data
AI memory stores become sensitive quickly: project decisions, customer details, internal strategy. Security cannot be optional.
Core principles
- least privilege access
- tenant isolation by default
- data minimization
- auditable operations
API key scoping
Each key should map to a strict namespace. A key for one workspace must never read another workspace memory.
What should be stored
- actionable summaries
- technical decisions
- stable preferences
What should not be stored
- raw secrets
- unnecessary personal identifiers
- full sensitive transcripts when summary is enough
Encryption and transport
- TLS for all in-transit traffic
- encrypted storage at rest
- rotate credentials on a schedule
Access controls
Build role-aware read paths:
- operator tooling can inspect metadata
- end-user-facing assistant only receives filtered recall snippets
Retention controls
Support per-category TTL and deletion workflows. Compliance requires practical deletion, not “we’ll clean it eventually.”
Auditability
Track:
- who wrote memory
- who retrieved memory
- when deletion or supersession occurred
This is critical for trust and incident response.
Incident containment
If key leakage occurs:
- revoke key immediately
- rotate derived tokens
- review access logs
- notify affected tenant if required
Architecture stance
Security in memory systems is mostly boundary discipline: strict scoping, minimal retention, and transparent operations.
Do those three well and you avoid most catastrophic failure modes.