📊 EU Parliament Monitor — Future Data Model

🗄️ AWS-Native Serverless Data Architecture for Political Intelligence
🎯 From Committed Static Artifacts to a Serverless Knowledge-Graph Platform (2026-2037)

📋 Document Owner: CEO | 📄 Version: 4.1 | 📅 Last Updated: 2026-05-31 (UTC) | 🚀 Release: v1.0.1
🔄 Review Cycle: Quarterly | ⏰ Next Review: 2026-08-31
🏷️ Classification: Public (Open Source European Parliament Monitoring Platform)

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📚 Architecture Documentation Map

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🔐 ISMS Policy Alignment

This future data model is designed to implement all controls from Hack23 AB's ISMS framework as the EU Parliament Monitor platform evolves from a committed static-file corpus to an AWS-native serverless data platform. Every data store named below is governed by least-privilege IAM, encrypted with AWS KMS customer-managed keys, and constrained to PUBLIC open European Parliament data and the platform's own derived analysis artifacts — no private-life or non-public personal data is ingested.

Related ISMS Policies

Policy Domain	Policy	Planned Implementation
🔐 Core Security	Information Security Policy	Overall governance for the serverless data platform
🤖 AI Governance	AI Policy	Bedrock Guardrails, human-accountable RAG, no autonomous deploy
🛠️ Development	Secure Development Policy	Schema-as-code, IaC review gates, data-contract tests
🌐 Network	Network Security Policy	VPC isolation, PrivateLink, WAF, Shield for data APIs
🔒 Cryptography	Cryptography Policy	KMS CMK encryption at rest, TLS 1.3 in transit, integrity hashes
🔑 Access Control	Access Control Policy	Cognito identity, IAM least-privilege, fine-grained table access
🏷️ Data Classification	Data Classification Policy	Per-store PUBLIC classification & tagging
🔍 Vulnerability	Vulnerability Management	Inspector, automated dependency & infra scanning
🚨 Incident Response	Incident Response Plan	GuardDuty + Security Hub automated detection & response
💾 Backup & Recovery	Backup Recovery Policy	PITR, S3 versioning, cross-region replication
🔄 Business Continuity	Business Continuity Plan	Multi-AZ serverless, static-edge fallback
🤝 Third-Party	Third Party Management	EP MCP / EP Open Data / IMF / World Bank source assurance
🏷️ Classification	Classification Framework	Business impact analysis for platform

Compliance Framework Mapping

Framework	Version	Relevant Controls
ISO 27001	2022	A.5.1, A.8.10, A.8.11, A.8.12, A.8.25, A.8.26, A.8.27, A.8.28
NIST CSF	2.0	GV.OC, GV.RM, ID.AM, PR.AA, PR.DS, DE.CM
CIS Controls	v8.1	Control 1-6, 11, 13, 14, 16
GDPR	2016/679	Art. 5 (minimisation), Art. 6 (lawful basis: public-interest transparency), Art. 89

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📋 Executive Summary

This document defines the evolution of EU Parliament Monitor's data model from its current committed static-file corpus — versioned analysis artifacts plus pre-rendered, per-language HTML on Amazon S3 + Amazon CloudFront — toward an AWS-native serverless data platform capable of real-time European Parliament event ingestion, a queryable political knowledge graph, semantic / RAG search over the full analysis corpus, and an API ecosystem for journalists and researchers.

It supersedes the obsolete v3.0 polyglot blueprint (PostgreSQL + MongoDB + Redis + Elasticsearch + Neo4j on self-managed infrastructure). That generic-cloud framing is retired. Every data tier is now expressed in managed, serverless AWS primitives so the platform retains zero-ops economics while gaining query power.

Three-Horizon Data Strategy

Horizon	Name	Data Posture	Primary Stores
🟢 v2.0	Enhanced Static Intelligence (2026 H2 → 2027)	Stay file-based. Committed analysis artifacts (manifest.json runs) + per-language HTML, now augmented with richer pre-computed party / political-landscape dashboard datasets baked at build time.	Git-versioned artifacts, S3 build outputs, CloudFront edge cache
🔵 v3.0+	AWS-Native Serverless Platform (2028+)	Dynamic layer behind the static edge. Hot key-value, relational voting history, full-text + vector search, political knowledge graph, S3 data lake + BI, and a managed RAG layer.	DynamoDB · Aurora Serverless v2 · OpenSearch Serverless · Neptune Serverless · S3/Glue/Athena/QuickSight · Bedrock Knowledge Bases
⚪ 10-yr	AI Lookahead (2026 → 2037)	Model-agnostic semantic fabric; multi-parliament ontology; quantum-safe crypto migration.	Bedrock (model-agnostic) + Neptune ontology + linked-data exports

Current State (v1.0.x) → v2.0 → v3.0+ Comparison

Aspect	Current (v1.0.x)	v2.0 (Static-Enhanced)	v3.0+ (AWS Serverless)	Benefit
Storage	Committed markdown + HTML on S3	+ pre-computed dashboard JSON baked at build	DynamoDB + Aurora Serverless v2 + S3 lake	Query flexibility, scale
Structure	Provenance manifest.json + typed src/types	+ party/landscape datasets	Relational + key-value + graph + vector	Rich relationships
Search	Static client filter	Pre-indexed facets	OpenSearch Serverless (BM25 + kNN vector)	Semantic + RAG search
Relationships	Implicit in prose	Coalition/actor graph JSON	Neptune Serverless property graph	Native graph queries
Update cadence	Daily gh-aw batch	Daily batch + richer datasets	EventBridge + Kinesis near-real-time	Sub-minute freshness
Query API	None (static)	None (static)	API Gateway + AppSync GraphQL	Programmatic access
Historical data	Git history	Git history	Aurora temporal + Neptune time-versioned	Trend & "as-of" analysis
AI/RAG	Build-time LLM authoring (gh-aw)	Same	Bedrock Knowledge Bases over corpus	NL query, grounded answers
Data sources	EP MCP + World Bank + IMF	Same	+ multi-parliament adapters	Comparative coverage

The strategic invariant across all horizons: the static HTML edge remains the public, cacheable, low-cost front door. Dynamic v3.0+ features are layered behind CloudFront, never replacing it. See FUTURE_ARCHITECTURE.md for the matching C4 view and DATA_MODEL.md for the current schema.

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📅 Data Model Evolution Roadmap

gantt
    title Data Model Evolution Roadmap (2026-2030)
    dateFormat YYYY-MM

    section v2.0 Static-Enhanced
    Party Landscape Datasets (build-time)   :v2a, 2026-07, 3M
    Coalition Graph JSON (pre-computed)     :v2b, 2026-09, 2M
    OSINT Tradecraft Schema Hardening       :v2c, 2026-10, 3M
    Seat-Projection Dataset Bake            :v2d, 2027-01, 2M

    section v3.0 Foundation
    DynamoDB Single-Table Design            :v3a, 2027-06, 3M
    Aurora Serverless v2 Voting Schema      :v3b, 2027-08, 3M
    S3 Data Lake + Glue Catalog             :v3c, 2027-09, 2M

    section v3.0 Intelligence
    OpenSearch Serverless (vector + BM25)   :v3d, 2028-01, 3M
    Neptune Serverless Knowledge Graph      :v3e, 2028-03, 4M
    Bedrock Knowledge Bases (RAG)           :v3f, 2028-06, 3M

    section v3.0 Real-Time
    EventBridge + Kinesis Ingestion         :v3g, 2028-09, 3M
    Athena + QuickSight Analytics           :v3h, 2028-11, 2M
    Multi-Parliament Adapters               :v3i, 2029-03, 6M
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🟢 v2.0 — File-Based Data Model (Static-Enhanced)

v2.0 introduces no servers and no databases. It deepens the existing file-based corpus and adds pre-computed dashboard datasets baked at build time, so the public surface stays pure static HTML on S3 + CloudFront while gaining party-level and political-landscape analytics.

What stays identical to v1.0.x

• Committed analysis artifacts under analysis/daily/<YYYY-MM-DD>/<slug>/ with a manifest.json provenance record (schema 1.4.0+) that the deterministic aggregator (src/aggregator/**) reads to render HTML.
• Per-language article HTML (news/*.html, 14 languages) generated by src/aggregator/article-generator.ts — never authored by an LLM directly.
• Strongly-typed domain models in src/types/*.ts (strict ESM).
• Data surfaces: European Parliament MCP server (european-parliament-mcp-server@1.3.20, 60+ tools), worldbank-mcp (optional), and IMF REST.

What v2.0 adds — pre-computed landscape datasets

A new build step emits static, versioned dataset files (JSON, hydrated client-side by Chart.js 4 + D3 7) focused on parties and political groups:

data/landscape/<term>/
├── political-groups.json        ← seat share, cohesion %, leadership
├── group-cohesion-timeseries.json
├── coalition-mathematics.json   ← winning-coalition combinatorics per dossier
├── coalition-network.json       ← nodes/edges for cross-party alliance graph
├── mep-scorecards.json          ← per-MEP activity/loyalty/influence indices
├── voting-heatmap.json          ← group x policy-area alignment matrix
├── seat-projection-2029.json    ← electoral-cycle forecast bands
└── manifest.json                ← dataset provenance + source EP MCP versions
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These files are produced deterministically from EP MCP tool output during CI, hashed for integrity (SHA-256), and committed alongside the analysis run. Because they are plain static assets, CloudFront caches them at the edge with no compute cost. The v2.0 graph datasets (coalition-network.json) use the same conceptual schema as the v3.0 Neptune property graph, so the later migration is a loader change, not a remodelling exercise.

erDiagram
    ANALYSIS_RUN ||--|| RUN_MANIFEST : "described by"
    ANALYSIS_RUN ||--o{ ANALYSIS_ARTIFACT : "emits"
    ANALYSIS_RUN ||--o{ ARTICLE_HTML : "renders"
    ANALYSIS_RUN ||--o{ LANDSCAPE_DATASET : "bakes"
    LANDSCAPE_DATASET ||--o{ POLITICAL_GROUP_FACT : "contains"
    LANDSCAPE_DATASET ||--o{ COALITION_EDGE : "contains"
    LANDSCAPE_DATASET ||--o{ MEP_SCORECARD : "contains"

    RUN_MANIFEST {
        string articleType "ArticleCategory enum"
        string runId "gh-aw run id"
        string generatedAt "ISO 8601 UTC"
        string sourceCommit "git SHA"
        string epMcpVersion "1.3.20"
        string ghAwVersion "v0.71.6"
        string schemaVersion "1.4.0+"
        string dataMode "full | reduced"
    }
    ANALYSIS_ARTIFACT {
        string relativePath "path under run dir"
        string category "classification | threat | risk | ..."
        int lineCount "vs reference-quality floor"
    }
    ARTICLE_HTML {
        string language "ISO 639-1"
        string path "news/<slug>_<lang>.html"
    }
    LANDSCAPE_DATASET {
        string name "political-groups | coalition-network | ..."
        string term "EP10"
        string sha256 "integrity hash"
    }
    POLITICAL_GROUP_FACT {
        string groupCode "EPP | SD | Renew | ..."
        int seatCount
        float cohesionPct
        string policyArea
    }
    COALITION_EDGE {
        string groupA
        string groupB
        float coVoteRate "0..1"
        string dossierScope
    }
    MEP_SCORECARD {
        string mepId "EP identifier"
        float participationRate
        float loyaltyScore
        float influenceIndex
    }
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GDPR note (all horizons): MEP_SCORECARD and every MEP-linked record store only public parliamentary-role attributes (votes cast in plenary, committee membership, tabled questions). No private-life, contact-beyond-public-office, or protected-characteristic data is held. Lawful basis is public-interest transparency (GDPR Art. 6(1)(e)); processing is documented per Art. 30.

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🔵 v3.0+ — AWS-Native Serverless Data Architecture

From 2028 the file-based corpus becomes the immutable source of record that hydrates a set of purpose-fit, fully-managed AWS serverless stores. Each store is selected for a specific access pattern; none is self-managed; all scale to zero or near-zero when idle.

📐 Future Entity Relationship Diagram (v3.0 logical model)

erDiagram
    MEP ||--o{ VOTE_CAST : "casts"
    MEP }o--|| POLITICAL_GROUP : "member of"
    MEP }o--|| NATIONAL_PARTY : "represents"
    MEP }o--|| COUNTRY : "elected in"
    MEP ||--o{ COMMITTEE_MEMBERSHIP : "holds"
    MEP ||--o{ QUESTION : "tables"

    COMMITTEE ||--o{ COMMITTEE_MEMBERSHIP : "staffed by"
    COMMITTEE ||--o{ DOSSIER : "responsible for"

    DOSSIER ||--o{ VOTE : "decided by"
    VOTE ||--o{ VOTE_CAST : "aggregates"
    PLENARY_SESSION ||--o{ VOTE : "includes"

    POLITICAL_GROUP ||--o{ COALITION_MEMBERSHIP : "joins"
    COALITION ||--o{ COALITION_MEMBERSHIP : "comprises"

    ANALYSIS_ARTIFACT ||--o{ EMBEDDING : "vectorised as"
    ANALYSIS_ARTIFACT }o--|| ANALYSIS_RUN : "produced by"
    EP_DOCUMENT ||--o{ EMBEDDING : "vectorised as"
    KNOWLEDGE_BASE ||--o{ EMBEDDING : "indexes"

    MEP {
        string mep_id PK "EP identifier"
        string full_name "public"
        string country FK
        string group_code FK
        string national_party FK
        date term_start
        date term_end
    }
    POLITICAL_GROUP {
        string group_code PK "EPP | SD | Renew | ..."
        string name
        string ideology_band
        int seat_count
    }
    COUNTRY {
        string iso_code PK "ISO 3166-1 alpha-2"
        string name
        int ep_seats
    }
    COMMITTEE {
        string code PK "LIBE | ECON | ENVI | ..."
        string name
        string policy_area
    }
    DOSSIER {
        string procedure_ref PK "2024/0001(COD)"
        string title
        string committee_code FK
        string stage "committee | plenary | trilogue | adopted"
    }
    VOTE {
        string vote_id PK "EP vote identifier"
        string procedure_ref FK
        string session_id FK
        datetime vote_time
        int for_count
        int against_count
        int abstain_count
        string result "passed | rejected"
    }
    VOTE_CAST {
        string vote_id FK
        string mep_id FK
        string position "for | against | abstain | absent"
    }
    PLENARY_SESSION {
        string session_id PK
        date session_date
        string location "Strasbourg | Brussels"
    }
    COMMITTEE_MEMBERSHIP {
        string mep_id FK
        string committee_code FK
        string role "chair | vice | member | substitute"
    }
    QUESTION {
        string question_id PK
        string mep_id FK
        string type "written | oral"
        date tabled_date
    }
    COALITION {
        string coalition_id PK
        string dossier_scope
        float winning_margin
    }
    COALITION_MEMBERSHIP {
        string coalition_id FK
        string group_code FK
    }
    ANALYSIS_RUN {
        string run_id PK
        string article_type
        datetime generated_at
        string source_commit
    }
    ANALYSIS_ARTIFACT {
        string artifact_id PK
        string run_id FK
        string relative_path
        string category
    }
    EP_DOCUMENT {
        string document_id PK
        string document_type
        date publication_date
    }
    EMBEDDING {
        string embedding_id PK
        string source_id FK
        string source_type "artifact | ep_document"
        string model "amazon.titan-embed | cohere"
        int dims "1024 | 1536"
    }
    KNOWLEDGE_BASE {
        string kb_id PK
        string name "ep-corpus-kb"
        string vector_store "OpenSearch Serverless collection"
    }
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The logical entities above are physically distributed across five AWS serverless stores plus a managed RAG layer, each mapped to its natural access pattern in the sections that follow.

Store-to-Access-Pattern Mapping

graph TD
    SOR["S3 Source of Record<br/>committed artifacts + EP feeds"]:::s3
    SOR --> DDB["Amazon DynamoDB<br/>hot key-value / single-table"]:::aws
    SOR --> AUR["Amazon Aurora Serverless v2<br/>relational voting history"]:::aws
    SOR --> OSS["Amazon OpenSearch Serverless<br/>BM25 + vector kNN"]:::aws
    SOR --> NEP["Amazon Neptune Serverless<br/>political knowledge graph"]:::aws
    SOR --> LAKE["S3 Data Lake + Glue + Athena<br/>QuickSight BI"]:::aws
    OSS --> KB["Amazon Bedrock<br/>Knowledge Bases (RAG)"]:::ai
    NEP --> KB
    AUR --> KB

    classDef s3 fill:#e8f5e9,stroke:#2e7d32,color:#000
    classDef aws fill:#fff3e0,stroke:#e65100,color:#000
    classDef ai fill:#ede7f6,stroke:#4527a0,color:#000
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🔑 Amazon DynamoDB — Hot Key-Value & Single-Table

Role: ultra-low-latency, scale-to-near-zero store for sessions, the analysis run index, and real-time EP event state. Replaces the obsolete MongoDB document store and the Redis cache (the latter via DynamoDB DAX for microsecond reads, or ElastiCache Serverless where a true cache-aside is needed).

Single-Table Design

A single table epm-core uses a generic partition/sort key with overloaded item types, GSIs for inverted access, and a TTL attribute for ephemeral real-time state.

Access pattern	PK	SK	Notes
Run index by date	RUN#<date>	SLUG#<slug>	List runs for a day
Run provenance	RUN#<runId>	META	Mirrors manifest.json
Artifact catalogue	RUN#<runId>	ART#<path>	Per-artifact metadata
Live vote tally	VOTE#<voteId>	STATE	TTL-expiring real-time tally
Session (Cognito)	SESSION#<userId>	<sessionId>	API consumer session
Saved query	USER#<userId>	QUERY#<id>	Researcher saved searches

{
  "PK": "RUN#2028-06-01-breaking-run07",
  "SK": "META",
  "itemType": "AnalysisRun",
  "articleType": "breaking",
  "generatedAt": "2028-06-01T05:12:44Z",
  "sourceCommit": "a1b2c3d",
  "epMcpVersion": "1.3.20",
  "schemaVersion": "1.4.0",
  "dataMode": "full",
  "gsi1pk": "TYPE#breaking",
  "gsi1sk": "DATE#2028-06-01",
  "ttl": null
}
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• Capacity: on-demand (pay-per-request) — scales to zero cost when idle.
• Streams: DynamoDB Streams fan changes into EventBridge for downstream projection into Aurora / OpenSearch / Neptune.
• Integrity: items carry the sha256 of their source artifact; PITR enabled.
• Caching: DAX cluster (or ElastiCache Serverless) fronts read-heavy GSIs.

Old → new: MongoDB document store → DynamoDB single-table; Redis cache → DynamoDB DAX / ElastiCache Serverless.

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🗃️ Amazon Aurora Serverless v2 (PostgreSQL) — Relational Voting History

Role: the system of relational truth for MEPs, votes, full per-MEP voting history, committees, dossiers, and temporal "as-of" queries. Replaces the obsolete self-managed PostgreSQL/TimescaleDB tier with an auto-scaling, scale-to-low (0.5 ACU) serverless Postgres that supports the pgvector extension for in-row embeddings where co-location with relational filters is valuable.

Core Relational Schema

-- Members of the European Parliament (public role attributes only)
CREATE TABLE mep (
    mep_id          TEXT PRIMARY KEY,           -- EP identifier
    full_name       TEXT NOT NULL,              -- public
    country_iso     CHAR(2) NOT NULL REFERENCES country(iso_code),
    group_code      TEXT REFERENCES political_group(group_code),
    national_party  TEXT,
    term_start      DATE NOT NULL,
    term_end        DATE,
    CONSTRAINT public_role_only CHECK (true)    -- GDPR: no private-life columns
);

CREATE TABLE political_group (
    group_code   TEXT PRIMARY KEY,              -- EPP, SD, Renew, ...
    name         TEXT NOT NULL,
    ideology_band TEXT,
    seat_count   INT
);

CREATE TABLE committee (
    code         TEXT PRIMARY KEY,              -- LIBE, ECON, ENVI, ...
    name         TEXT NOT NULL,
    policy_area  TEXT
);

CREATE TABLE dossier (
    procedure_ref TEXT PRIMARY KEY,             -- 2024/0001(COD)
    title         TEXT NOT NULL,
    committee_code TEXT REFERENCES committee(code),
    stage         TEXT                          -- committee|plenary|trilogue|adopted
);

CREATE TABLE plenary_vote (
    vote_id       TEXT PRIMARY KEY,
    procedure_ref TEXT REFERENCES dossier(procedure_ref),
    session_id    TEXT NOT NULL,
    vote_time     TIMESTAMPTZ NOT NULL,
    for_count     INT, against_count INT, abstain_count INT,
    result        TEXT                          -- passed | rejected
);

-- Per-MEP roll-call positions (the high-volume, append-only history)
CREATE TABLE vote_cast (
    vote_id   TEXT REFERENCES plenary_vote(vote_id),
    mep_id    TEXT REFERENCES mep(mep_id),
    position  TEXT NOT NULL,                    -- for|against|abstain|absent
    PRIMARY KEY (vote_id, mep_id)
);
CREATE INDEX idx_vote_cast_mep ON vote_cast (mep_id);

-- Temporal committee membership for "as-of" composition queries
CREATE TABLE committee_membership (
    mep_id     TEXT REFERENCES mep(mep_id),
    code       TEXT REFERENCES committee(code),
    role       TEXT,                            -- chair|vice|member|substitute
    valid_from DATE NOT NULL,
    valid_to   DATE,
    PRIMARY KEY (mep_id, code, valid_from)
);
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Representative Analytical Queries

-- Group cohesion: share of a group voting with its modal position per dossier
SELECT pg.group_code,
       d.procedure_ref,
       AVG(modal.share) AS cohesion
FROM political_group pg
JOIN mep m              ON m.group_code = pg.group_code
JOIN vote_cast vc       ON vc.mep_id = m.mep_id
JOIN plenary_vote pv    ON pv.vote_id = vc.vote_id
JOIN dossier d          ON d.procedure_ref = pv.procedure_ref
JOIN LATERAL (
    SELECT MAX(cnt)::float / NULLIF(SUM(cnt),0) AS share
    FROM (SELECT position, COUNT(*) cnt
          FROM vote_cast x JOIN mep mm ON mm.mep_id = x.mep_id
          WHERE x.vote_id = pv.vote_id AND mm.group_code = pg.group_code
          GROUP BY position) t
) modal ON true
GROUP BY pg.group_code, d.procedure_ref;
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• Scaling: Aurora Serverless v2 0.5–N ACU, auto-pause off for warm read replicas.
• Temporal: valid_from/valid_to ranges + system_time-style snapshots enable "what did committee LIBE look like on 2027-09-01?" analysis.
• Resilience: Multi-AZ, automated backups, PITR; cross-region read replica for DR.

Old → new: self-managed PostgreSQL / TimescaleDB → Aurora Serverless v2.

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🔍 Amazon OpenSearch Serverless — Full-Text + Vector Search

Role: unified lexical (BM25) and semantic (kNN vector) search across the analysis corpus, EP documents, and dashboards. Replaces the obsolete Elasticsearch tier. A single collection serves both keyword search and the vector store backing Bedrock Knowledge Bases.

Index Mapping (hybrid lexical + vector)

{
  "settings": { "index.knn": true },
  "mappings": {
    "properties": {
      "source_id":   { "type": "keyword" },
      "source_type": { "type": "keyword" },
      "article_type":{ "type": "keyword" },
      "language":    { "type": "keyword" },
      "title":       { "type": "text", "analyzer": "standard" },
      "body":        { "type": "text" },
      "published_at":{ "type": "date" },
      "mep_ids":     { "type": "keyword" },
      "group_codes": { "type": "keyword" },
      "committee":   { "type": "keyword" },
      "embedding": {
        "type": "knn_vector",
        "dimension": 1024,
        "method": { "name": "hnsw", "space_type": "cosinesimil", "engine": "faiss" }
      }
    }
  }
}
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Hybrid Query Example

{
  "size": 10,
  "query": {
    "hybrid": {
      "queries": [
        { "match": { "body": "carbon border adjustment cohesion" } },
        { "knn": { "embedding": { "vector": [/* 1024-d query embedding */],
                                  "k": 10 } } }
      ]
    }
  },
  "post_filter": { "term": { "language": "en" } }
}
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• Serverless: OCUs auto-scale; no node management; encryption with KMS CMK.
• Vector source: embeddings generated by Amazon Bedrock (Titan Embeddings / Cohere) at ingestion, stored alongside lexical fields for hybrid ranking.
• Access: queried by AppSync resolvers and by Bedrock Knowledge Bases retrieval.

Old → new: Elasticsearch → OpenSearch Serverless (lexical + vector).

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🕸️ Amazon Neptune Serverless — Political Knowledge Graph

Role: the political knowledge graph linking MEP ↔ political group ↔ committee ↔ dossier ↔ vote ↔ country, plus derived coalition and actor networks. Replaces the obsolete Neo4j tier with a managed, auto-scaling property-graph + RDF engine (openCypher / Gremlin / SPARQL). This is the spine of v3.0 OSINT capability: natural network queries ("which Renew MEPs broke with their group on ENVI dossiers and how does that cluster by country?") that are awkward in relational SQL.

Graph Schema

graph LR
    MEP(("MEP")):::node
    GRP(("Political Group")):::node
    CTE(("Committee")):::node
    DOS(("Dossier")):::node
    VOTE(("Vote")):::node
    CNTRY(("Country")):::node
    COAL(("Coalition")):::node
    PARTY(("National Party")):::node

    MEP -->|MEMBER_OF| GRP
    MEP -->|ELECTED_IN| CNTRY
    MEP -->|REPRESENTS| PARTY
    MEP -->|SITS_ON| CTE
    MEP -->|CAST| VOTE
    CTE -->|RESPONSIBLE_FOR| DOS
    DOS -->|DECIDED_BY| VOTE
    GRP -->|JOINS| COAL
    COAL -->|ON_DOSSIER| DOS
    PARTY -->|AFFILIATED_WITH| GRP

    classDef node fill:#e3f2fd,stroke:#1565c0,color:#000
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Property-Graph Model (openCypher)

// Vertices carry only PUBLIC role attributes
(:MEP {mep_id, full_name, term_start, term_end})
(:PoliticalGroup {group_code, name, ideology_band, seat_count})
(:Country {iso_code, name, ep_seats})
(:Committee {code, name, policy_area})
(:Dossier {procedure_ref, title, stage})
(:Vote {vote_id, vote_time, result})
(:Coalition {coalition_id, winning_margin})
(:NationalParty {party_id, name})

// Edges
(:MEP)-[:MEMBER_OF {since}]->(:PoliticalGroup)
(:MEP)-[:ELECTED_IN]->(:Country)
(:MEP)-[:REPRESENTS]->(:NationalParty)
(:MEP)-[:SITS_ON {role, valid_from, valid_to}]->(:Committee)
(:MEP)-[:CAST {position}]->(:Vote)
(:Committee)-[:RESPONSIBLE_FOR]->(:Dossier)
(:Dossier)-[:DECIDED_BY]->(:Vote)
(:PoliticalGroup)-[:JOINS {co_vote_rate}]->(:Coalition)
(:Coalition)-[:ON_DOSSIER]->(:Dossier)
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Representative Network Query

// Cross-party defection clustering on environment dossiers
MATCH (m:MEP)-[:MEMBER_OF]->(g:PoliticalGroup {group_code:'Renew'}),
      (m)-[c:CAST]->(v:Vote)<-[:DECIDED_BY]-(d:Dossier),
      (cte:Committee {code:'ENVI'})-[:RESPONSIBLE_FOR]->(d),
      (m)-[:ELECTED_IN]->(country:Country)
WHERE c.position <> g.modal_position
RETURN country.iso_code, count(DISTINCT m) AS defectors
ORDER BY defectors DESC;
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• Serverless: Neptune Serverless NCUs scale with query load; KMS-encrypted.
• Provenance: every edge references the EP source vote_id / procedure_ref, preserving evidence chains required by the OSINT tradecraft methodology.
• Time-versioning: edges carry valid_from/valid_to so coalition graphs can be reconstructed "as of" any plenary week.
• Bidirectional with v2.0: the static coalition-network.json dataset is the same logical graph, exported for client-side D3 rendering.

Old → new: Neo4j knowledge graph → Amazon Neptune Serverless.

---

🏞️ S3 Data Lake + AWS Glue + Amazon Athena + Amazon QuickSight

Role: cost-efficient analytics and BI over the full historical corpus. The committed artifacts and EP feeds land in an S3 data lake (partitioned Parquet), are catalogued by AWS Glue, queried ad-hoc with Amazon Athena (serverless SQL), and visualised in Amazon QuickSight dashboards for internal analysts and partner journalists.

Lake Zones & Catalog

s3://epm-datalake/
├── raw/        ep_mcp/ , imf/ , worldbank/        (immutable landing, JSON)
├── curated/    votes/ meps/ dossiers/ coalitions/ (Parquet, partitioned by year/term)
└── analytics/  scorecards/ cohesion/ projections/ (aggregated marts)
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graph LR
    RAW["S3 raw zone<br/>JSON landing"]:::s3
    GLUE["AWS Glue<br/>crawlers + ETL jobs"]:::aws
    CUR["S3 curated zone<br/>partitioned Parquet"]:::s3
    ATH["Amazon Athena<br/>serverless SQL"]:::aws
    QS["Amazon QuickSight<br/>BI dashboards"]:::aws
    RAW --> GLUE --> CUR --> ATH --> QS

    classDef s3 fill:#e8f5e9,stroke:#2e7d32,color:#000
    classDef aws fill:#fff3e0,stroke:#e65100,color:#000
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• Glue Data Catalog is the single schema registry shared by Athena, EMR Serverless (if needed), and QuickSight.
• Athena powers long-horizon trend queries (multi-term voting drift) that would be expensive to keep hot in Aurora.
• QuickSight SPICE datasets refresh on a schedule from Athena views; row-level security ties to Cognito groups.
• Format: Parquet + Snappy, partition projection for fast pruning; lifecycle policy tiers cold partitions to S3 Glacier Instant Retrieval.

Old → new: ad-hoc warehouse / Datadog dashboards → S3 + Glue + Athena + QuickSight.

---

🤖 Amazon Bedrock Knowledge Bases — Managed RAG Layer

Role: the managed Retrieval-Augmented-Generation layer over the EP corpus and the platform's own analysis artifacts, enabling grounded natural-language query ("Summarise how the EPP voted on migration dossiers this term, with citations"). Replaces any bespoke OpenAI/LangChain gateway with a model-agnostic Bedrock layer.

graph TD
    SRC["Sources<br/>S3 artifacts + EP documents"]:::s3
    EMB["Bedrock Embeddings<br/>Titan / Cohere"]:::ai
    VEC["OpenSearch Serverless<br/>vector collection"]:::aws
    KB["Bedrock Knowledge Base<br/>ep-corpus-kb"]:::ai
    AG["Bedrock Agents<br/>tool use / OSINT workflows"]:::ai
    GR["Bedrock Guardrails<br/>neutrality + PII/GDPR + hallucination"]:::ai
    APP["AppSync / API Gateway<br/>NL query endpoint"]:::aws

    SRC --> EMB --> VEC --> KB --> AG --> GR --> APP

    classDef s3 fill:#e8f5e9,stroke:#2e7d32,color:#000
    classDef aws fill:#fff3e0,stroke:#e65100,color:#000
    classDef ai fill:#ede7f6,stroke:#4527a0,color:#000
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• Vector store: the same OpenSearch Serverless collection used for hybrid search — one index, two consumers (search UI and RAG retrieval).
• Models: Anthropic Claude, Amazon Nova, and others, swapped via configuration — no application rewrite when a better model ships (see AI lookahead below).
• Bedrock Guardrails enforce: political neutrality, PII / GDPR redaction (block any non-public-role personal data), and hallucination control (answers must cite retrieved EP source passages).
• Governance: per the AI Policy, AI generates proposals, humans remain accountable, and no autonomous deploy of generated content occurs — RAG answers are advisory and citation-backed.

Old → new: OpenAI / LangChain gateway → Amazon Bedrock + Knowledge Bases + Agents + Guardrails.

---

🔄 Data Synchronization & Ingestion Strategy

v3.0 ingestion is event-driven and serverless end-to-end. EP MCP / EP Open Data changes are detected, streamed, transformed, and projected into each purpose-fit store with eventual consistency. The S3 source of record stays authoritative; every derived store can be rebuilt from it.

sequenceDiagram
    participant EP as EP MCP / EP Open Data
    participant ING as Lambda Ingestor
    participant KIN as Amazon Kinesis
    participant EB as Amazon EventBridge
    participant SFN as Step Functions
    participant S3 as S3 Source of Record
    participant DDB as DynamoDB
    participant AUR as Aurora Serverless v2
    participant OSS as OpenSearch Serverless
    participant NEP as Neptune Serverless

    EP->>ING: poll feeds / webhook (votes, sessions, docs)
    ING->>S3: write immutable raw payload (hashed)
    ING->>KIN: emit change record
    KIN->>EB: route by detail-type
    EB->>SFN: start projection workflow
    SFN->>DDB: upsert run index / live state
    SFN->>AUR: upsert relational rows (idempotent)
    SFN->>OSS: index doc + embedding
    SFN->>NEP: upsert vertices/edges
    SFN-->>EB: emit projection-complete event
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Components & Responsibilities

Concern	AWS Service	Behaviour
Scheduled / triggered polling	EventBridge Scheduler + Lambda	Pulls EP MCP sliding/fixed-window feeds
Change streaming	Amazon Kinesis Data Streams	Ordered, replayable change records
Routing / fan-out	Amazon EventBridge	Content-based routing by detail-type
Orchestration	AWS Step Functions	Idempotent multi-store projection sagas
Async buffering	Amazon SQS / SNS	Backpressure + retry + DLQ
Source of record	Amazon S3	Immutable, versioned, hashed payloads

Eventual-Consistency & Idempotency

• Every projection is idempotent keyed on the EP source id + content hash, so a Kinesis replay never double-writes.
• Dead-letter queues capture poison records; a Step Functions retry policy with exponential backoff handles transient EP API throttling.
• DynamoDB Streams provide a secondary change feed so a store added later (e.g. a new analytics mart) can backfill from history without touching the ingestor.
• Reconciliation job (nightly Glue) diffs each derived store against the S3 SOR and emits drift metrics to CloudWatch.

Old → new: Kafka event bus → EventBridge + Kinesis + SQS/SNS; Socket.io / Apollo → API Gateway WebSocket / AppSync for live push.

---

📈 Data Migration Plan (File-Based Static → AWS Serverless)

Migration is additive and reversible: the file-based corpus keeps running and serving traffic while serverless stores are populated from it. No "big bang."

flowchart TD
    A["Phase 0: v1.0.x baseline<br/>committed artifacts on S3+CloudFront"] --> B
    B["Phase 1: Backfill S3 data lake<br/>load historical artifacts + EP feeds (Parquet)"] --> C
    C["Phase 2: Stand up read stores<br/>Aurora + DynamoDB + OpenSearch from lake"] --> D
    D["Phase 3: Build knowledge graph<br/>Neptune loader from curated zone"] --> E
    E["Phase 4: Enable RAG<br/>Bedrock KB over OpenSearch vectors"] --> F
    F["Phase 5: Real-time ingestion<br/>EventBridge+Kinesis live projection"] --> G
    G["Phase 6: Expose APIs<br/>API Gateway + AppSync + Cognito"] --> H
    H["Phase 7: Multi-parliament adapters<br/>pluggable source mappers"]
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Migration Principles

Principle	Implementation
Source of record unchanged	Git + S3 artifacts remain authoritative; stores are projections
Idempotent loaders	Re-runnable Glue / Lambda jobs keyed on EP ids + hashes
Reversibility	Any store can be dropped and rebuilt from S3 SOR
Zero downtime	Static edge keeps serving; dynamic features feature-flagged
Validation gates	Row-count + checksum parity checks before cut-over
Cost discipline	Scale-to-zero serverless; backfill on Spot/serverless batch

Illustrative Backfill Job (TypeScript / Lambda)

// Project a committed analysis run into DynamoDB + OpenSearch (idempotent)
import { readManifest } from "../src/aggregator/analysis-aggregator.js";

export async function projectRun(runDir: string): Promise<void> {
  const manifest = await readManifest(runDir);          // src/types/analysis.ts
  const key = `RUN#${manifest.runId}`;
  await ddb.put({                                        // idempotent upsert
    TableName: "epm-core",
    Item: { PK: key, SK: "META", ...manifest,
            gsi1pk: `TYPE#${manifest.articleType}`,
            gsi1sk: `DATE#${manifest.generatedAt.slice(0, 10)}` },
  });
  for (const path of manifest.files.classification ?? []) {
    const body = await readArtifact(runDir, path);
    const embedding = await bedrockEmbed(body);          // Titan / Cohere
    await opensearch.index({
      index: "epm-corpus",
      id: `${manifest.runId}:${path}`,                   // deterministic id
      document: { source_id: path, source_type: "artifact",
                  article_type: manifest.articleType, body, embedding },
    });
  }
}
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---

📊 Data Quality & Monitoring

Dimension	Mechanism	AWS Service
Freshness	Feed lag vs EP publication timestamps	CloudWatch metric + alarm
Completeness	Row/edge parity vs S3 SOR	AWS Glue Data Quality
Schema conformance	Contract tests on ingest	Glue Data Quality rulesets
Integrity	SHA-256 of raw payloads vs stored	Lambda check + CloudTrail
Referential integrity	Orphan vote_cast / dangling edges	Athena scheduled query
Drift	Nightly reconciliation diff	Glue job → CloudWatch dashboard
Cost / capacity	ACU / OCU / NCU utilisation	CloudWatch + Budgets alerts

-- Athena data-quality probe: votes with no recorded per-MEP positions
SELECT pv.vote_id, pv.vote_time
FROM curated.plenary_vote pv
LEFT JOIN curated.vote_cast vc ON vc.vote_id = pv.vote_id
WHERE vc.vote_id IS NULL
ORDER BY pv.vote_time DESC;
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• Glue Data Quality rulesets enforce non-null keys, enum domains (e.g. position IN ('for','against','abstain','absent')), and freshness SLAs at ingest time.
• CloudWatch dashboards track per-store health; alarms route to the Incident Response Plan via SNS.
• Quality lineage mirrors the existing reference-quality-thresholds.json philosophy — every projection records its source artifact and line/row counts.

---

🔐 Data Security & GDPR

Security is defence-in-depth and PUBLIC-data-only by design.

Control	Implementation
Encryption at rest	AWS KMS customer-managed keys for DynamoDB, Aurora, OpenSearch, Neptune, S3
Encryption in transit	TLS 1.3 everywhere; VPC endpoints / PrivateLink for store access
Identity	Amazon Cognito user pools (journalists / researchers / API consumers), federated IdP
Authorization	IAM least-privilege roles per Lambda; fine-grained DynamoDB / row-level Aurora policies
Network	Private subnets, security groups, AWS WAF + Shield on public APIs
Secrets	AWS Secrets Manager for any source credentials; no secrets in code
Audit	AWS CloudTrail + Security Hub + GuardDuty anomaly detection
PII / GDPR	Bedrock Guardrails block non-public-role personal data in any RAG output

GDPR Public-Roles-Only Guarantee

• Lawful basis: public-interest transparency (GDPR Art. 6(1)(e)); the data subject set is elected officials acting in their public parliamentary role.
• Data minimisation (Art. 5): stores hold only votes, memberships, tabled questions, declarations published by the EP — no private life, no contact data beyond public office, no protected characteristics, no inferred psychographics.
• Schema-level enforcement: Aurora tables carry no private-attribute columns; Neptune vertices expose only public role properties; Bedrock Guardrails redact any accidental PII before it can surface in a generated answer.
• Auditability: all access to MEP-linked data is logged (CloudTrail) per the intelligence-operative GDPR duty; processing recorded per Art. 30.
• See FUTURE_SECURITY_ARCHITECTURE.md and FUTURE_THREAT_MODEL.md for the full control set.

---

🕵️ Political Intelligence Data Capabilities (2026 → 2037)

The stores above hold the base political data — MEPs, groups, votes, dossiers. This section adds the intelligence-grade data structures required by the OSINT capability roadmap in FUTURE_MINDMAP.md and realised architecturally in FUTURE_ARCHITECTURE.md. Each new entity family is the data backbone of a missing capability a senior intelligence operative would expect — and every one stays inside the PUBLIC open-data, public-parliamentary-role boundary with provenance attached.

GDPR / neutrality invariant. New entities describe public roles, public declarations, public documents, and public discourse only. No private-life attribute, no protected characteristic, and no psychographic field is ever modelled. Integrity findings are stored as questions with evidence, never as adjudicated accusations.

New Intelligence Entity Families

Capability	New Entities	Store	Horizon
Collection management / PIR	IntelligenceRequirement, CollectionTask, CoverageGap	DynamoDB	🟢 v2.0 → 🔵 v3.0
Indications and Warning	Indicator, Watchlist, WarningEvent, Tripwire	DynamoDB + Kinesis	🔵 v3.0
Integrity / conflict-of-interest	FinancialInterest, OutsideActivity, RegisterEntry, Meeting, InterestOrganization	Aurora + Neptune	🔵 v3.1
Verbatim speech intelligence	Speech, Utterance, StanceSignal	Aurora + OpenSearch	🔵 v3.1
Counter-FIMI	Narrative, MediaItem, NarrativeCampaign, FIMIIncident	OpenSearch + Neptune	⚪ v3.2
Forecasting + ACH	Forecast, Hypothesis, ConfidenceAssessment, RedTeamReview	Aurora	🔵 v3.0
Provenance + authenticity	EvidenceChain, SourceGrade, ContentCredential	S3 + Neptune	🟢 → ⚪ v3.2

Knowledge-Graph Extensions (openCypher)

These vertices and edges extend the v3.0 Neptune political knowledge graph so multi-hop influence and integrity tracing becomes a single query.

// New PUBLIC-only intelligence vertices
(:InterestOrganization {org_id, name, register_id, country, category})
(:RegisterEntry {entry_id, declared_interest, lobby_budget_band, updated})
(:Meeting {meeting_id, date, subject, dossier_ref})
(:FinancialInterest {decl_id, type, declared_on, source})
(:Narrative {narrative_id, theme, first_seen, languages})
(:FIMIIncident {incident_id, disarm_ttp, confidence_band, status})
(:Indicator {indicator_id, name, baseline, threshold})
(:WarningEvent {warning_id, level, raised_at, confirmed_by})

// New edges — every edge references a PUBLIC EP / register source
(:MEP)-[:DECLARED {declared_on}]->(:FinancialInterest)
(:MEP)-[:MET_WITH {date}]->(:InterestOrganization)
(:InterestOrganization)-[:LISTED_IN]->(:RegisterEntry)
(:InterestOrganization)-[:LOBBIED_ON]->(:Dossier)
(:Meeting)-[:CONCERNS]->(:Dossier)
(:Narrative)-[:REFERENCES]->(:Dossier)
(:FIMIIncident)-[:AMPLIFIES]->(:Narrative)
(:Indicator)-[:WATCHES]->(:PoliticalGroup)
(:WarningEvent)-[:RAISED_BY]->(:Indicator)
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// Integrity question (NOT an accusation): rapporteurs whose dossier overlaps a
// declared outside interest AND a registered lobby meeting on the same dossier.
MATCH (m:MEP)-[:SITS_ON {role:'Rapporteur'}]->(:Committee)-[:RESPONSIBLE_FOR]->(d:Dossier),
      (m)-[:DECLARED]->(fi:FinancialInterest),
      (m)-[:MET_WITH]->(o:InterestOrganization)-[:LOBBIED_ON]->(d)
RETURN m.full_name AS public_role, d.procedure_ref, fi.type, o.name
ORDER BY d.procedure_ref;
// Output is a SOURCED prompt for journalistic review, WEP-banded, human-reviewed.
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Indications-and-Warning Indicator Schema (DynamoDB)

The I&W store turns the abstract "warning problem" into queryable indicators with calibrated tripwires and an auditable promotion history.

{
  "PK": "WATCHLIST#coalition-collapse",
  "SK": "INDICATOR#cohesion-decline-EPP",
  "indicator": "EPP roll-call cohesion 30-day rolling mean",
  "baseline": 0.92,
  "current": 0.81,
  "tripwire": 0.85,
  "deviation_sigma": 2.4,
  "confidence_band": "likely (WEP 55-70%)",
  "state": "WARNING",
  "evidence_vote_ids": ["RCV-2029-0412", "RCV-2029-0418"],
  "raised_at": "2029-03-14T09:00:00Z",
  "human_confirmed_by": "analyst-on-duty",
  "anchoring_methodology": "coalition-dynamics-analysis.md"
}
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Forecast and Hypothesis Schema (Aurora)

Predictive products are stored with their competing hypotheses, confidence band, and red-team review attached — never a bare point estimate — so every forecast is auditable against the outcome and the analytic track record is measurable.

CREATE TABLE forecast (
  forecast_id      uuid PRIMARY KEY,
  subject_ref      text NOT NULL,          -- dossier / coalition / election
  question         text NOT NULL,          -- the estimative question
  estimate         numeric,                -- probability 0..1
  wep_band         text NOT NULL,          -- Kent / Words of Estimative Probability
  confidence       text NOT NULL,          -- ICD 203 low/moderate/high
  competing_hyps   jsonb NOT NULL,         -- >= 2 hypotheses required
  red_team_review  jsonb,                  -- devil's-advocate dissent record
  evidence_chain   jsonb NOT NULL,         -- cited PUBLIC sources
  resolved_outcome numeric,                -- filled after the event for calibration
  human_signoff    text NOT NULL
);
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Calibration loop. resolved_outcome closes the feedback arm of the intelligence cycle: forecast accuracy is scored over time (Brier-style), feeding the analytic track record and re-tasking the collection plan — the data-model realisation of "be early and honest about it".

---

🔮 Visionary Data Model Roadmap: 2027-2037

As AI models evolve — major upgrades roughly annually, with competitors evaluated at each release — the data model evolves toward a model-agnostic semantic fabric on the same AWS serverless substrate. Bedrock's model abstraction means new foundation models are adopted by configuration, not rearchitecture.

AI Model Evolution — DevSecOps & Development Perspective

Year	AI Model	DevSecOps Capability Evolution
2026	Opus 4.6–4.9	🟢 AI-assisted code review, automated test generation, agentic CI/CD workflows
2027	Opus 5.x	🔵 Predictive vulnerability detection, intelligent dependency management
2028	Opus 6.x	🟣 Multi-modal security analysis (code + architecture + runtime), automated threat modeling
2029	Opus 7.x	🟠 Autonomous security pipeline orchestration, self-healing build systems
2030	Opus 8.x	🔴 Near-expert automated security review, AI-driven architecture validation
2031–2033	Opus 9–10.x / Pre-AGI	⚪ Autonomous secure development lifecycle management
2034–2037	AGI / Post-AGI	⭐ Transformative software engineering with built-in security assurance

Assumptions: major AI model upgrades annually; competitors (OpenAI, Google, Meta, EU sovereign AI) evaluated at each release; architecture accommodates potential paradigm shifts (quantum AI, neuromorphic computing). Full cross-perspective analysis lives in the Hack23 Information Security Strategy § AI Model Evolution Strategy; governance per AI Policy (AI = proposal generator, human accountability, no autonomous deploy).

Data-Model Eras

Era	Years	Data Model Paradigm	Key Additions
Near-Term	2027-2029	AWS serverless multi-store + Neptune graph	Hybrid vector/lexical search, Bedrock RAG, real-time projection
Mid-Term	2029-2032	Unified semantic fabric	OWL/RDF parliament ontology over Neptune, automated schema evolution with human gates
Long-Term	2032-2035	Autonomous data curation	Self-healing pipelines, predictive indexing, causal inference layer
Visionary	2035-2037	AGI-ready, quantum-safe	Dynamic schema generation, universal multi-parliament intelligence, PQC migration

Phase 5: Semantic Data Fabric (2027-2029)

• Universal Parliament Ontology: a formal OWL/RDF ontology over Neptune's RDF engine mapping bills, votes, committees, and amendments across the EP and national parliaments into one semantic model — exported as linked open data.
• Hybrid retrieval everywhere: OpenSearch Serverless hybrid (BM25 + kNN) becomes the default access path; Bedrock Knowledge Bases ground every analytical answer.
• Temporal graph: Neptune time-versioned edges enable "what did Parliament look like on date X?" reconstruction across the whole term.

Phase 6: Autonomous Data Curation (2029-2032)

• Self-healing pipelines: Step Functions + Bedrock Agents detect Glue Data Quality failures, propose corrections, and apply them with human approval gates and full CloudTrail audit (per AI Policy — no autonomous mutation of the SOR).
• Predictive indexing: SageMaker models anticipate query demand from the EP calendar and pre-warm Aurora replicas / OpenSearch OCUs.
• Cross-lingual unification: Amazon Translate + Comprehend align 24+ EU-language parliamentary data into the shared ontology without manual mapping.

Phase 7: Global Democratic Data & Multi-Parliament Expansion (2032-2035)

• Pluggable source adapters: a normalisation layer maps additional national parliaments into the shared ontology, reusing the EP MCP contract pattern.
• Causal layer: graph + relational features feed SageMaker causal models for "why did this coalition form?" analysis beyond correlation.
• Linked-data publishing: SPARQL endpoint + bulk RDF exports make the corpus a reusable civic-tech public good.

Phase 8: AGI-Ready & Quantum-Safe Data (2035-2037)

• Dynamic schema generation: AGI-class systems propose optimal projections for a given analytical question; humans ratify before deployment.
• Quantum-safe cryptography: migrate KMS-managed keys and data-in-transit to post-quantum (PQC) algorithms as AWS exposes them, protecting long-lived public archives against "harvest-now-decrypt-later" risk.
• Model-agnostic to the end: Bedrock's abstraction means even paradigm shifts (neuromorphic, quantum AI) are adopted at the model layer, leaving the serverless data substrate and the GDPR public-roles-only guarantee intact.

---

📚 References

Current State

• Current Data Model
• Current Architecture
• Current Flowcharts

Future State

• Future Architecture
• Future Flowchart
• Future State Diagram
• Future Security Architecture
• Future Threat Model
• Future Workflows

AWS Service Documentation

• Amazon DynamoDB · Amazon Aurora Serverless v2 (PostgreSQL)
• Amazon OpenSearch Serverless (lexical + vector) · Amazon Neptune Serverless
• Amazon S3 · AWS Glue · Amazon Athena · Amazon QuickSight
• Amazon Bedrock · Bedrock Knowledge Bases · Bedrock Agents · Bedrock Guardrails
• Amazon EventBridge · Amazon Kinesis · AWS Step Functions · Amazon SQS / SNS
• Amazon API Gateway · AWS AppSync · Amazon Cognito · AWS KMS · AWS Secrets Manager

External Standards

• ISO 8601 (Date/Time) · ISO 639-1 (Language Codes) · ISO 3166-1 (Country Codes)
• W3C RDF / OWL / SPARQL (semantic fabric)
• JSON Schema · GraphQL Schema (AppSync)
• GDPR (Regulation 2016/679) — public-interest transparency basis

Governance

• Hack23 AI Policy
• Hack23 Information Security Policy
• Hack23 Data Classification Policy

---

Document Status: ✅ APPROVED FOR PLANNING
Version: 4.0 | Last Updated: 2026-05-31 (UTC) | Release: v1.0.1
Next Review: 2026-08-31 (Quarterly)
Classification: Public (Open Source European Parliament Monitoring Platform)