feat(docs): Add comprehensive documentation for Vexer, Vulnerability Explorer, and Zastava modules

- Introduced AGENTS.md, README.md, TASKS.md, and implementation_plan.md for Vexer, detailing mission, responsibilities, key components, and operational notes.
- Established similar documentation structure for Vulnerability Explorer and Zastava modules, including their respective workflows, integrations, and observability notes.
- Created risk scoring profiles documentation outlining the core workflow, factor model, governance, and deliverables.
- Ensured all modules adhere to the Aggregation-Only Contract and maintain determinism and provenance in outputs.
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# Advisory AI architecture
> Captures the retrieval, guardrail, and inference packaging requirements defined in the Advisory AI implementation plan and related module guides.
## 1) Goals
- Summarise advisories/VEX evidence into operator-ready briefs with citations.
- Explain conflicting statements with provenance and trust weights (using VEX Lens & Excititor data).
- Suggest remediation plans aligned with Offline Kit deployment models and scheduler follow-ups.
- Operate deterministically where possible; cache generated artefacts with digests for audit.
## 2) Pipeline overview
```
+---------------------+
Concelier/VEX Lens | Evidence Retriever |
Policy Engine ----> | (vector + keyword) | ---> Context Pack (JSON)
Zastava runtime +---------------------+
|
v
+-------------+
| Prompt |
| Assembler |
+-------------+
|
v
+-------------+
| Guarded LLM |
| (local/host)|
+-------------+
|
v
+-----------------+
| Citation & |
| Validation |
+-----------------+
|
v
+----------------+
| Output cache |
| (hash, bundle) |
+----------------+
```
## 3) Retrieval & context
- Hybrid search: vector embeddings (SBERT-compatible) + keyword filters for advisory IDs, PURLs, CVEs.
- Context packs include:
- Advisory raw excerpts with highlighted sections and source URLs.
- VEX statements (normalized tuples + trust metadata).
- Policy explain traces for the affected finding.
- Runtime/impact hints from Zastava (exposure, entrypoints).
- Export-ready remediation data (fixed versions, patches).
All context references include `content_hash` and `source_id` enabling verifiable citations.
## 4) Guardrails
- Prompt templates enforce structure: summary, conflicts, remediation, references.
- Response validator ensures:
- No hallucinated advisories (every fact must map to input context).
- Citations follow `[n]` indexing referencing actual sources.
- Remediation suggestions only cite policy-approved sources (fixed versions, vendor hotfixes).
- Moderation/PII filters prevent leaking secrets; responses failing validation are rejected and logged.
## 5) Output persistence
- Cached artefacts stored in `advisory_ai_outputs` with fields:
- `output_hash` (sha256 of JSON response).
- `input_digest` (hash of context pack).
- `summary`, `conflicts`, `remediation`, `citations`.
- `generated_at`, `model_id`, `profile` (Sovereign/FIPS etc.).
- `signatures` (optional DSSE if run in deterministic mode).
- Offline bundle format contains `summary.md`, `citations.json`, `context_manifest.json`, `signatures/`.
## 6) Profiles & sovereignty
- **Profiles:** `default`, `fips-local` (FIPS-compliant local model), `gost-local`, `cloud-openai` (optional, disabled by default). Each profile defines allowed models, key management, and telemetry endpoints.
- **CryptoProfile/RootPack integration:** generated artefacts can be signed using configured CryptoProfile to satisfy procurement/trust requirements.
## 7) APIs
- `POST /v1/advisory-ai/summaries` — generate (or retrieve cached) summary for `{advisoryKey, artifactId, policyVersion}`.
- `POST /v1/advisory-ai/conflicts` — explain conflicting VEX statements with trust ranking.
- `POST /v1/advisory-ai/remediation` — fetch remediation plan with target fix versions, prerequisites, verification steps.
- `GET /v1/advisory-ai/outputs/{hash}` — retrieve cached artefact (used by CLI/Console/Export Center).
All endpoints accept `profile` parameter (default `fips-local`) and return `output_hash`, `input_digest`, and `citations` for verification.
## 8) Observability
- Metrics: `advisory_ai_requests_total{profile,type}`, `advisory_ai_latency_seconds`, `advisory_ai_validation_failures_total`.
- Logs: include `output_hash`, `input_digest`, `profile`, `model_id`, `tenant`, `artifacts`. Sensitive context is not logged.
- Traces: spans for retrieval, prompt assembly, model inference, validation, cache write.
## 9) Operational controls
- Feature flags per tenant (`ai.summary.enabled`, `ai.remediation.enabled`).
- Rate limits (per tenant, per profile) enforced by Orchestrator to prevent runaway usage.
- Offline/air-gapped deployments run local models packaged with Offline Kit; model weights validated via manifest digests.