kanaria007/agi-structural-intelligence-protocols
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kanaria007/agi-structural-intelligence-protocols
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7 days ago
✅ New Guide: OSS Supply Chain with SI Stack (v0.1)
Title:
🔐 OSS Supply Chain with the Structured Intelligence Stack — Patent‑Safe PoC Design (v0.1)
🔗 https://huggingface.co/blog/kanaria007/oss-supply-chain-v0-1
---
Summary:
Supply chain attacks (xz backdoor, malicious npm/PyPI packages) exploit blind spots
in commit → build → artifact chains. At 100+ transitive dependencies, human review
breaks down.
This guide shows how to eliminate blind spots using BuildAttestation + hermetic builds +
rollback marks. Covers binaries, containers, ML models—with ML framework dependencies
(PyTorch, TensorFlow, CUDA) getting special attention.
> From blind trust to causal proof.
> Deployable on existing CI/CD.
---
Why It Matters:
- *ML supply chain*: PyPI/conda packages with deep transitive deps (transformers, torch)
- *Hermetic builds*: pinned toolchains + deterministic modes (CUDA/cuDNN) → reproducible
- *SBOM enforcement*: coverage ≥99%; no SBOM → no publish
- *Rollback marks*: rebuild from trusted checkpoints when deps compromised
---
What's Inside:
*Foundation:*
BuildAttestation (causal chain), MalwareTombstone (proof-preserving removal),
Orchestrator API; hermetic builds with env_hash + toolchain pins
*ML specifics:*
Determinism profile (BLAS variant, CUDA deterministic, TensorFlow strict mode);
driver versions (CUDA/cuDNN/GPU); distributed training clock sync
*Compliance:*
SLSA 3+, Sigstore interop, SBOM (SPDX/CycloneDX); depth limits (max: 6);
policy gates (allow/deny/freeze)
*Integration:*
npm/PyPI/crates.io/Go examples; CI snippets (GitHub Actions, GitLab, Jenkins);
phased migration strategy
---
📖 Patent-Safe PoC Design
10 KPIs with SLA targets; 6-8 week PoC plan
Text: CC BY 4.0. Code: MIT.
---
For ML/AI systems—PyPI packages, model artifacts, containers—this provides
recipes and SLAs to ship supply chain security.
posted
an
update
11 days ago
✅ New Guide: GDPR-Compatible "Ethical Redaction" (v0.1)
Title:
🔒 GDPR‑Compatible “Ethical Redaction” on Conventional Stacks — Guide (v0.1)
🔗 https://huggingface.co/blog/kanaria007/gdpr-ethical-redaction-v0-1
---
Summary:
The "right to erasure" often breaks on real stacks: backups, replicas, caches,
vendor processors—and especially ML training pipelines and feature stores.
This guide shows how to make erasure operational using crypto-shredding + WORM
audit and an Erasure Orchestrator covering databases, objects, search, BI,
caches, vector DBs/models, and more.
> Privacy compliance without losing explainability.
> Deployable on conventional infra, now.
---
Why It Matters:
- *Backups included by design* (key destruction = erasure across all copies)
- *ML-aware*: track subject_ref through pipelines; handle model disable/retrain/output-guard
- *Provable evidence*: anonymous tombstones + WORM logs, no raw IDs
- *Measurable compliance*: p95 SLAs, coverage %, re-ID risk—tracked continuously
---
What's Inside:
*Foundation:*
KMS hierarchy, WORM audit, Erasure Orchestrator; state machine (verify → crypto-shred → propagate); API endpoints for all GDPR rights
*Coverage:*
All data products (RDBMS/objects/search/BI/caches/backups); ML specifics (§12): subject_ref lineage + erasure strategies; processor propagation (Art.28)
*Compliance:*
14 KPIs with SLA targets + automated probes; special categories & minors safeguards; multi-region KMS; migration playbook + runbooks
---
📖 Informative Engineering Guide
Legal mapping: Arts. 5, 15–22, 25, 28 + Recital 26 → deployable patterns
Not legal advice. Text: CC BY 4.0. Code: MIT.
---
For production AI/ML stacks, this provides recipes, SLAs, and evidence models to ship privacy-by-design.
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