e8537460a3
- 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.
101 lines
4.8 KiB
Markdown
101 lines
4.8 KiB
Markdown
# Advisory AI architecture
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> Captures the retrieval, guardrail, and inference packaging requirements defined in the Advisory AI implementation plan and related module guides.
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## 1) Goals
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- Summarise advisories/VEX evidence into operator-ready briefs with citations.
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- Explain conflicting statements with provenance and trust weights (using VEX Lens & Excititor data).
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- Suggest remediation plans aligned with Offline Kit deployment models and scheduler follow-ups.
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- Operate deterministically where possible; cache generated artefacts with digests for audit.
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## 2) Pipeline overview
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```
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+---------------------+
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Concelier/VEX Lens | Evidence Retriever |
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Policy Engine ----> | (vector + keyword) | ---> Context Pack (JSON)
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Zastava runtime +---------------------+
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v
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+-------------+
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| Prompt |
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| Assembler |
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+-------------+
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v
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+-------------+
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| Guarded LLM |
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| (local/host)|
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+-------------+
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v
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+-----------------+
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| Citation & |
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| Validation |
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+-----------------+
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v
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+----------------+
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| Output cache |
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| (hash, bundle) |
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+----------------+
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```
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## 3) Retrieval & context
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- Hybrid search: vector embeddings (SBERT-compatible) + keyword filters for advisory IDs, PURLs, CVEs.
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- Context packs include:
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- Advisory raw excerpts with highlighted sections and source URLs.
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- VEX statements (normalized tuples + trust metadata).
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- Policy explain traces for the affected finding.
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- Runtime/impact hints from Zastava (exposure, entrypoints).
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- Export-ready remediation data (fixed versions, patches).
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All context references include `content_hash` and `source_id` enabling verifiable citations.
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## 4) Guardrails
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- Prompt templates enforce structure: summary, conflicts, remediation, references.
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- Response validator ensures:
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- No hallucinated advisories (every fact must map to input context).
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- Citations follow `[n]` indexing referencing actual sources.
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- Remediation suggestions only cite policy-approved sources (fixed versions, vendor hotfixes).
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- Moderation/PII filters prevent leaking secrets; responses failing validation are rejected and logged.
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## 5) Output persistence
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- Cached artefacts stored in `advisory_ai_outputs` with fields:
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- `output_hash` (sha256 of JSON response).
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- `input_digest` (hash of context pack).
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- `summary`, `conflicts`, `remediation`, `citations`.
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- `generated_at`, `model_id`, `profile` (Sovereign/FIPS etc.).
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- `signatures` (optional DSSE if run in deterministic mode).
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- Offline bundle format contains `summary.md`, `citations.json`, `context_manifest.json`, `signatures/`.
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## 6) Profiles & sovereignty
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- **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.
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- **CryptoProfile/RootPack integration:** generated artefacts can be signed using configured CryptoProfile to satisfy procurement/trust requirements.
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## 7) APIs
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- `POST /v1/advisory-ai/summaries` — generate (or retrieve cached) summary for `{advisoryKey, artifactId, policyVersion}`.
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- `POST /v1/advisory-ai/conflicts` — explain conflicting VEX statements with trust ranking.
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- `POST /v1/advisory-ai/remediation` — fetch remediation plan with target fix versions, prerequisites, verification steps.
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- `GET /v1/advisory-ai/outputs/{hash}` — retrieve cached artefact (used by CLI/Console/Export Center).
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All endpoints accept `profile` parameter (default `fips-local`) and return `output_hash`, `input_digest`, and `citations` for verification.
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## 8) Observability
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- Metrics: `advisory_ai_requests_total{profile,type}`, `advisory_ai_latency_seconds`, `advisory_ai_validation_failures_total`.
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- Logs: include `output_hash`, `input_digest`, `profile`, `model_id`, `tenant`, `artifacts`. Sensitive context is not logged.
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- Traces: spans for retrieval, prompt assembly, model inference, validation, cache write.
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## 9) Operational controls
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- Feature flags per tenant (`ai.summary.enabled`, `ai.remediation.enabled`).
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- Rate limits (per tenant, per profile) enforced by Orchestrator to prevent runaway usage.
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- Offline/air-gapped deployments run local models packaged with Offline Kit; model weights validated via manifest digests.
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