Version 1.0 · January 2026 · Frozen Reference Architecture
SMA-SIB defines an architecture for AI systems that must operate in high-risk environments without becoming repositories of sensitive, discoverable, or reconstructable information.
This architecture does not rely on access control, encryption, or policy enforcement. Reconstruction is impossible because the information is never retained in representable form.
No stored representation may correspond to a unique real-world entity, event, utterance, or timeline — even under full system compromise.
This invariant applies equally to memory stores, logs, caches, backups, telemetry, crash dumps, and analytics pipelines.
Most AI systems store high-fidelity representations and attempt to protect them through governance, access control, or retention policies. These protections can fail under breach, subpoena, or insider threat.
SMA-SIB inverts this model: specificity is destroyed at encoding time. The system is architecturally incapable of reconstructing identifiable content — not because access is restricted, but because the information no longer exists.
This architecture does not protect stored sensitive information. It ensures such information is never retained in representable form.
| Approach | Why It Fails |
|---|---|
| Encryption | Encrypted data can be decrypted with keys |
| Anonymization | Anonymized data can be re-identified via correlation |
| Access Control | Access mechanisms can be bypassed or compromised |
| Redaction | Redacted data can be inferred from surrounding context |
| Compression | Compressed data is designed to be decompressed |
| Security Layer / Shielding | Assumes sensitive information is retained and must be protected |
Structural irreversibility. The information required for reconstruction is destroyed during encoding. It is not hidden, not protected, not obfuscated. It is gone.
This architecture defines a class of systems where stored representations are structurally non-recoverable. These systems are distinct from privacy-preserving, anonymized, or encrypted systems.
SMA-SIB enforces irreversibility across three planes of representation.
Inputs are projected into canonical equivalence classes. Many distinct inputs collapse to the same stored form.
| Stage | Content |
|---|---|
| Input | A detailed, specific interaction containing identifiable information |
| Stored | HealthcareInteraction::ChronicCondition::Autoimmune |
| Expressible | "A health-related category was activated" |
No proper nouns, numeric specifics, or raw vectors are retained beyond the projection boundary. The residual — the difference between the specific input and its generic equivalence class — represents non-representable specificity and is discarded without materialization.
Relationships are expressed via domain co-membership, not causal or temporal graphs.
| Traditional Graph | SMA-SIB Representation |
|---|---|
User → [Diagnosis] → [Pharmacy] → [WorkLeave] → [Legal] |
Actor ∈ {HealthDomain, WorkDomain, LegalDomain} |
No directed edges. No temporal ordering. No narrative reconstruction. Domain membership conveys category presence only — not intent, severity, or causality.
Temporal and frequency signals are stored as coarse buckets, not precise values.
| Precise | SMA-SIB |
|---|---|
| 147 activations in 30 days | frequency: HIGH |
| Last accessed: 2026-01-17 14:32:18Z | recency: RECENT |
Recency and frequency buckets are orthogonal dimensions. After bucketing, precise values are discarded. No timeline reconstruction is possible.
Healthcare: Diagnostic reasoning support without patient-identifiable records. Discovery requests yield categories, not case specifics.
Defense / Intelligence: Pattern recognition without source retention. Compromised systems leak analytical domains, not intelligence.
Legal: Strategic reasoning support without attributable content. Opposing counsel obtains risk classifications, not strategy.
Finance: Behavioral anomaly patterns without account specifics. Insider access yields risk profiles, not actionable data.
Governance: Ideation support without position attribution. Leaked systems show topics under consideration, not authorship.
- Verbatim content reconstruction from stored state
- Identity inference from stored representations
- Timeline reconstruction from temporal data
- Specific numeric recovery (IDs, amounts, coordinates)
- Domain co-occurrence inference ("user engaged with health + legal + work domains")
- Generic category inference ("user dealing with complex situation")
This residual inference is intentional, non-actionable, and constitutes the accepted privacy boundary for high-utility systems operating under this architecture.
This architecture is not suitable for:
- Episodic recall ("What did I say on Tuesday?")
- Factual databases or knowledge graphs
- Audit trails or compliance logging
- Consumer personalization
- Systems requiring names, dates, or numeric precision
| Approach | Privacy Mechanism | Failure Mode |
|---|---|---|
| Encryption | Key-based access control | Key compromise or compelled disclosure |
| Anonymization | PII removal / hashing | Re-identification via correlation |
| Federated Learning | Data stays on device | Gradient inversion attacks |
| Differential Privacy | Statistical noise injection | Privacy budget exhaustion |
| SMA-SIB | Structural irreversibility | Domain co-occurrence patterns (accepted) |
Encoding requirements:
- Mandatory projection to equivalence classes (no bypass modes)
- Many-to-one transformation enforced at compilation/deployment
- No tunable precision parameters
Output constraints — structurally forbidden:
- Proper nouns (names of people, places, organizations)
- Specific numeric values (IDs, amounts, counts)
- Exact temporal references (dates, times, durations)
These are architectural properties, not configurable policies.
Verification: Irreversibility is structural. Adversarial probes that attempt reconstruction from stored state verify that the structure holds. If a probe recovers specifics above chance, the implementation has failed.
- Implementation Guide — Reference engineering protocol
- Semantic Non-Adaptation Principle — Required constraint for scaling
- Credits — Development attribution
This specification is released under the MIT License.
SMA-SIB: Non-Recoverable Representational Architecture for High-Sensitivity AI Systems
Version 1.0, January 2026
https://github.com/leenathomas01/SMA-SIB
SMA-SIB addresses environments where reconstruction must be structurally impossible. Separate work explores architectures where controlled reconstruction is permitted under different constraints. These represent distinct design regimes, not points on a shared continuum. SMA-SIB is not a lower-fidelity variant of reconstructive systems — it eliminates reconstructable representation entirely.
This is a frozen reference architecture, not a living project. Do not add features.